Advanced Services with the use of Condition Based Maintenance.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Advanced Services with the use of Condition Based Maintenance.ipynb","provenance":[],"toc_visible":true,"mount_file_id":"1DPS4w8dtdUPp8SxhrVGBueJ1tCELcmLo","authorship_tag":"ABX9TyNAkcMWOUYp7U2c3uEy4A13"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"_d-l0plcJH7n"},"source":["#Business Understanding"]},{"cell_type":"markdown","metadata":{"id":"nB4fq2KaIyns"},"source":["#Data Understanding"]},{"cell_type":"markdown","metadata":{"id":"nG7WNlQf4AA2"},"source":["##Import Data"]},{"cell_type":"code","metadata":{"id":"NVqJ5sVE39-v","executionInfo":{"status":"ok","timestamp":1615972469654,"user_tz":-60,"elapsed":4147,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}}},"source":["telemetry = pd.read_csv(\"/content/dataToService/telemetry.csv\", error_bad_lines=False)\r\n","errors = pd.read_csv(\"/content/drive/MyDrive/03 Schule/02_MSE/04_Masterarbeit/10_SAS_Workshop/Final Data/errors.csv\", error_bad_lines=False)\r\n","maint = pd.read_csv(\"/content/drive/MyDrive/03 Schule/02_MSE/04_Masterarbeit/10_SAS_Workshop/Final Data/maint.csv\", error_bad_lines=False)\r\n","failures = pd.read_csv(\"/content/drive/MyDrive/03 Schule/02_MSE/04_Masterarbeit/10_SAS_Workshop/Final Data/failures.csv\", error_bad_lines=False)\r\n","machines = pd.read_csv(\"/content/drive/MyDrive/03 Schule/02_MSE/04_Masterarbeit/10_SAS_Workshop/Final Data/machines.csv\", error_bad_lines=False)\r\n","cities = pd.read_csv(\"/content/drive/MyDrive/03 Schule/02_MSE/04_Masterarbeit/10_SAS_Workshop/Final Data/cities.csv\", error_bad_lines=False)\r\n","machinesLocation = pd.read_csv(\"/content/drive/MyDrive/03 Schule/02_MSE/04_Masterarbeit/10_SAS_Workshop/Final Data/machineLocation.csv\", error_bad_lines=False)"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9VTRWOSD5vTE"},"source":["## Telemetry Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"Bb7LWBrT4Zgc","executionInfo":{"status":"ok","timestamp":1615972475341,"user_tz":-60,"elapsed":476,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"011fb7ae-af19-40bb-87cf-4dc945310bb4"},"source":["telemetry.head()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>volt</th>\n","      <th>rotate</th>\n","      <th>pressure</th>\n","      <th>vibration</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-01 06:00:00</td>\n","      <td>1</td>\n","      <td>176.217853</td>\n","      <td>418.504078</td>\n","      <td>113.077935</td>\n","      <td>45.087686</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-01 07:00:00</td>\n","      <td>1</td>\n","      <td>162.879223</td>\n","      <td>402.747490</td>\n","      <td>95.460525</td>\n","      <td>43.413973</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-01 08:00:00</td>\n","      <td>1</td>\n","      <td>170.989902</td>\n","      <td>527.349825</td>\n","      <td>75.237905</td>\n","      <td>34.178847</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-01 09:00:00</td>\n","      <td>1</td>\n","      <td>162.462833</td>\n","      <td>346.149335</td>\n","      <td>109.248561</td>\n","      <td>41.122144</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-01 10:00:00</td>\n","      <td>1</td>\n","      <td>157.610021</td>\n","      <td>435.376873</td>\n","      <td>111.886648</td>\n","      <td>25.990511</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              datetime  machineID  ...    pressure  vibration\n","0  2015-01-01 06:00:00          1  ...  113.077935  45.087686\n","1  2015-01-01 07:00:00          1  ...   95.460525  43.413973\n","2  2015-01-01 08:00:00          1  ...   75.237905  34.178847\n","3  2015-01-01 09:00:00          1  ...  109.248561  41.122144\n","4  2015-01-01 10:00:00          1  ...  111.886648  25.990511\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"ch6xB1Zo5TBf","executionInfo":{"status":"ok","timestamp":1615972475342,"user_tz":-60,"elapsed":472,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"33cecc01-d2d4-49a6-d153-fb72fda851e3"},"source":["telemetry.tail()"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>volt</th>\n","      <th>rotate</th>\n","      <th>pressure</th>\n","      <th>vibration</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>876095</th>\n","      <td>2016-01-01 02:00:00</td>\n","      <td>100</td>\n","      <td>179.438162</td>\n","      <td>395.222827</td>\n","      <td>102.290715</td>\n","      <td>50.771941</td>\n","    </tr>\n","    <tr>\n","      <th>876096</th>\n","      <td>2016-01-01 03:00:00</td>\n","      <td>100</td>\n","      <td>189.617555</td>\n","      <td>446.207972</td>\n","      <td>98.180607</td>\n","      <td>35.123072</td>\n","    </tr>\n","    <tr>\n","      <th>876097</th>\n","      <td>2016-01-01 04:00:00</td>\n","      <td>100</td>\n","      <td>192.483414</td>\n","      <td>447.816524</td>\n","      <td>94.132837</td>\n","      <td>48.314561</td>\n","    </tr>\n","    <tr>\n","      <th>876098</th>\n","      <td>2016-01-01 05:00:00</td>\n","      <td>100</td>\n","      <td>165.475310</td>\n","      <td>413.771670</td>\n","      <td>104.081073</td>\n","      <td>44.835259</td>\n","    </tr>\n","    <tr>\n","      <th>876099</th>\n","      <td>2016-01-01 06:00:00</td>\n","      <td>100</td>\n","      <td>171.336037</td>\n","      <td>496.096870</td>\n","      <td>79.095538</td>\n","      <td>37.845245</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                   datetime  machineID  ...    pressure  vibration\n","876095  2016-01-01 02:00:00        100  ...  102.290715  50.771941\n","876096  2016-01-01 03:00:00        100  ...   98.180607  35.123072\n","876097  2016-01-01 04:00:00        100  ...   94.132837  48.314561\n","876098  2016-01-01 05:00:00        100  ...  104.081073  44.835259\n","876099  2016-01-01 06:00:00        100  ...   79.095538  37.845245\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mgadyy745iNS","executionInfo":{"status":"ok","timestamp":1615972475582,"user_tz":-60,"elapsed":708,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"75851c8b-e3f2-40e1-c36b-60af5ad53053"},"source":["telemetry.info()"],"execution_count":11,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 876100 entries, 0 to 876099\n","Data columns (total 6 columns):\n"," #   Column     Non-Null Count   Dtype  \n","---  ------     --------------   -----  \n"," 0   datetime   876100 non-null  object \n"," 1   machineID  876100 non-null  int64  \n"," 2   volt       876100 non-null  float64\n"," 3   rotate     876100 non-null  float64\n"," 4   pressure   876100 non-null  float64\n"," 5   vibration  876100 non-null  float64\n","dtypes: float64(4), int64(1), object(1)\n","memory usage: 40.1+ MB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AKufPoIO58Nd","executionInfo":{"status":"ok","timestamp":1615972475584,"user_tz":-60,"elapsed":705,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"34450dd1-1181-4092-9612-93a27e3789ed"},"source":["telemetry.dtypes"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["datetime      object\n","machineID      int64\n","volt         float64\n","rotate       float64\n","pressure     float64\n","vibration    float64\n","dtype: object"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GriQs0sB6E4t","executionInfo":{"status":"ok","timestamp":1615972475584,"user_tz":-60,"elapsed":702,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"5fd635c4-1896-42e7-b42c-95b58be09bed"},"source":["telemetry[\"machineID\"].nunique()"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["100"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"PlL9niuT6iib"},"source":["Changing the format of \"datetime\" to a datetime format"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"NoDwmb1f6VeB","executionInfo":{"status":"ok","timestamp":1615972475752,"user_tz":-60,"elapsed":866,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"9720f200-facc-47b8-ee6b-546901cb1e47"},"source":["telemetry[\"datetime\"] = pd.to_datetime(telemetry[\"datetime\"], format=\"%Y-%m-%d %H:%M:%S\")\r\n","telemetry.head()"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>volt</th>\n","      <th>rotate</th>\n","      <th>pressure</th>\n","      <th>vibration</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-01 06:00:00</td>\n","      <td>1</td>\n","      <td>176.217853</td>\n","      <td>418.504078</td>\n","      <td>113.077935</td>\n","      <td>45.087686</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-01 07:00:00</td>\n","      <td>1</td>\n","      <td>162.879223</td>\n","      <td>402.747490</td>\n","      <td>95.460525</td>\n","      <td>43.413973</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-01 08:00:00</td>\n","      <td>1</td>\n","      <td>170.989902</td>\n","      <td>527.349825</td>\n","      <td>75.237905</td>\n","      <td>34.178847</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-01 09:00:00</td>\n","      <td>1</td>\n","      <td>162.462833</td>\n","      <td>346.149335</td>\n","      <td>109.248561</td>\n","      <td>41.122144</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-01 10:00:00</td>\n","      <td>1</td>\n","      <td>157.610021</td>\n","      <td>435.376873</td>\n","      <td>111.886648</td>\n","      <td>25.990511</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             datetime  machineID        volt      rotate    pressure  vibration\n","0 2015-01-01 06:00:00          1  176.217853  418.504078  113.077935  45.087686\n","1 2015-01-01 07:00:00          1  162.879223  402.747490   95.460525  43.413973\n","2 2015-01-01 08:00:00          1  170.989902  527.349825   75.237905  34.178847\n","3 2015-01-01 09:00:00          1  162.462833  346.149335  109.248561  41.122144\n","4 2015-01-01 10:00:00          1  157.610021  435.376873  111.886648  25.990511"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gkbo0ihr60yK","executionInfo":{"status":"ok","timestamp":1615972475752,"user_tz":-60,"elapsed":861,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1893542c-3735-4422-bd4c-988c8b3ec382"},"source":["telemetry.dtypes"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["datetime     datetime64[ns]\n","machineID             int64\n","volt                float64\n","rotate              float64\n","pressure            float64\n","vibration           float64\n","dtype: object"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"IavbMzhM7MgV"},"source":["the dtype of this series is \"object\" because it has mixed types"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":312},"id":"N97qG2ZmeBQ2","executionInfo":{"status":"ok","timestamp":1615972668265,"user_tz":-60,"elapsed":1236,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"6c145f7e-c7c2-4610-e2eb-e253c555be3e"},"source":["plt.hist(telemetry[\"pressure\"], bins=100)\r\n","plt.title(\"Pressure Distribution\")\r\n","plt.ylabel(\"Density\")\r\n","plt.xlabel(\"Pressure\")"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0, 'Pressure')"]},"metadata":{"tags":[]},"execution_count":22},{"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":{"base_uri":"https://localhost:8080/","height":297},"id":"_gHRsz7e8LdK","executionInfo":{"status":"ok","timestamp":1615972503394,"user_tz":-60,"elapsed":28495,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1c444047-4d56-49bf-a2b8-ac87b606b230"},"source":["telemetry.describe()"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>volt</th>\n","      <th>rotate</th>\n","      <th>pressure</th>\n","      <th>vibration</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>50.500000</td>\n","      <td>170.777736</td>\n","      <td>446.605119</td>\n","      <td>100.858668</td>\n","      <td>40.385007</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>28.866087</td>\n","      <td>15.509114</td>\n","      <td>52.673886</td>\n","      <td>11.048679</td>\n","      <td>5.370361</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>97.333604</td>\n","      <td>138.432075</td>\n","      <td>51.237106</td>\n","      <td>14.877054</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>25.750000</td>\n","      <td>160.304927</td>\n","      <td>412.305714</td>\n","      <td>93.498181</td>\n","      <td>36.777299</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>50.500000</td>\n","      <td>170.607338</td>\n","      <td>447.558150</td>\n","      <td>100.425559</td>\n","      <td>40.237247</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>75.250000</td>\n","      <td>181.004493</td>\n","      <td>482.176600</td>\n","      <td>107.555231</td>\n","      <td>43.784938</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>100.000000</td>\n","      <td>255.124717</td>\n","      <td>695.020984</td>\n","      <td>185.951998</td>\n","      <td>76.791072</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           machineID           volt  ...       pressure      vibration\n","count  876100.000000  876100.000000  ...  876100.000000  876100.000000\n","mean       50.500000     170.777736  ...     100.858668      40.385007\n","std        28.866087      15.509114  ...      11.048679       5.370361\n","min         1.000000      97.333604  ...      51.237106      14.877054\n","25%        25.750000     160.304927  ...      93.498181      36.777299\n","50%        50.500000     170.607338  ...     100.425559      40.237247\n","75%        75.250000     181.004493  ...     107.555231      43.784938\n","max       100.000000     255.124717  ...     185.951998      76.791072\n","\n","[8 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"id":"OQY3aJEd9JJu","executionInfo":{"status":"ok","timestamp":1615972503906,"user_tz":-60,"elapsed":29002,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"d27334d6-8a4a-4109-e553-61be1c39361a"},"source":["plot_df = telemetry.loc[\r\n","    (telemetry[\"machineID\"] == 1)\r\n","    & (telemetry[\"datetime\"] > pd.to_datetime(\"2015-01-01\"))\r\n","    & (telemetry[\"datetime\"] < pd.to_datetime(\"2015-06-01\")), [\"datetime\", \"volt\"]\r\n","]\r\n","\r\n","plt.figure(figsize=(12, 6))\r\n","plt.plot(plot_df[\"datetime\"], plot_df[\"volt\"])\r\n","plt.title(\"Machine 1 Voltage Variation\")\r\n","plt.ylabel(\"Voltage\")\r\n","\r\n","#make labels readable\r\n","adf = plt.gca().get_xaxis().get_major_formatter()\r\n","adf.scaled[1.0] = \"%m-%d\"\r\n","plt.xlabel(\"Time\")\r\n","plt.show()"],"execution_count":18,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"_PdZavLx-ks_"},"source":["## Error Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"nmriMSDD-rOV","executionInfo":{"status":"ok","timestamp":1615904316476,"user_tz":-60,"elapsed":555,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"7eb05adc-8c40-4a5b-88f1-af324e395959"},"source":["errors.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>errorID</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-03 07:00:00</td>\n","      <td>1</td>\n","      <td>error1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-03 20:00:00</td>\n","      <td>1</td>\n","      <td>error3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-04 06:00:00</td>\n","      <td>1</td>\n","      <td>error5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-10 15:00:00</td>\n","      <td>1</td>\n","      <td>error4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-22 10:00:00</td>\n","      <td>1</td>\n","      <td>error4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              datetime  machineID errorID\n","0  2015-01-03 07:00:00          1  error1\n","1  2015-01-03 20:00:00          1  error3\n","2  2015-01-04 06:00:00          1  error5\n","3  2015-01-10 15:00:00          1  error4\n","4  2015-01-22 10:00:00          1  error4"]},"metadata":{"tags":[]},"execution_count":116}]},{"cell_type":"markdown","metadata":{"id":"e7uMp0GT-zsV"},"source":["These are warning errors generated while the machine is still operating and are not considered faults. Error date and times are rounded to the nearest hour as telemetry data is collected on an hourly basis."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"h6vJ4-5k-vS_","executionInfo":{"status":"ok","timestamp":1615904316477,"user_tz":-60,"elapsed":550,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"56eadc8a-5b1d-48d3-b8ea-60d22c15c747"},"source":["errors.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>errorID</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3914</th>\n","      <td>2015-11-21 08:00:00</td>\n","      <td>100</td>\n","      <td>error2</td>\n","    </tr>\n","    <tr>\n","      <th>3915</th>\n","      <td>2015-12-04 02:00:00</td>\n","      <td>100</td>\n","      <td>error1</td>\n","    </tr>\n","    <tr>\n","      <th>3916</th>\n","      <td>2015-12-08 06:00:00</td>\n","      <td>100</td>\n","      <td>error2</td>\n","    </tr>\n","    <tr>\n","      <th>3917</th>\n","      <td>2015-12-08 06:00:00</td>\n","      <td>100</td>\n","      <td>error3</td>\n","    </tr>\n","    <tr>\n","      <th>3918</th>\n","      <td>2015-12-22 03:00:00</td>\n","      <td>100</td>\n","      <td>error3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 datetime  machineID errorID\n","3914  2015-11-21 08:00:00        100  error2\n","3915  2015-12-04 02:00:00        100  error1\n","3916  2015-12-08 06:00:00        100  error2\n","3917  2015-12-08 06:00:00        100  error3\n","3918  2015-12-22 03:00:00        100  error3"]},"metadata":{"tags":[]},"execution_count":117}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sLI-nEh8-_tj","executionInfo":{"status":"ok","timestamp":1615904316477,"user_tz":-60,"elapsed":545,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"9361f9ef-75eb-4cf7-e6f7-85c0711ca326"},"source":["errors.info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 3919 entries, 0 to 3918\n","Data columns (total 3 columns):\n"," #   Column     Non-Null Count  Dtype \n","---  ------     --------------  ----- \n"," 0   datetime   3919 non-null   object\n"," 1   machineID  3919 non-null   int64 \n"," 2   errorID    3919 non-null   object\n","dtypes: int64(1), object(2)\n","memory usage: 92.0+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"vB9rSoRN_NDY","executionInfo":{"status":"ok","timestamp":1615904316478,"user_tz":-60,"elapsed":541,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"d59798c7-1c44-423c-e239-cb179cdcbb13"},"source":["errors[\"datetime\"] = pd.to_datetime(errors[\"datetime\"], format=\"%Y-%m-%d %H:%M:%S\")\r\n","errors[\"errorID\"] = errors[\"errorID\"].astype(\"category\")\r\n","\r\n","errors.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>errorID</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-03 07:00:00</td>\n","      <td>1</td>\n","      <td>error1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-03 20:00:00</td>\n","      <td>1</td>\n","      <td>error3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-04 06:00:00</td>\n","      <td>1</td>\n","      <td>error5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-10 15:00:00</td>\n","      <td>1</td>\n","      <td>error4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-22 10:00:00</td>\n","      <td>1</td>\n","      <td>error4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             datetime  machineID errorID\n","0 2015-01-03 07:00:00          1  error1\n","1 2015-01-03 20:00:00          1  error3\n","2 2015-01-04 06:00:00          1  error5\n","3 2015-01-10 15:00:00          1  error4\n","4 2015-01-22 10:00:00          1  error4"]},"metadata":{"tags":[]},"execution_count":119}]},{"cell_type":"markdown","metadata":{"id":"EmY9sAQO_cu3"},"source":["*   Categories allow comparison between values, automatic sorting, easier graphing and other functions.\r\n","*   Also less memory (similar to \"factor\" in R).\r\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"tGHlyfyc_brh","executionInfo":{"status":"ok","timestamp":1615904316673,"user_tz":-60,"elapsed":731,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"62e00915-b912-485b-d56f-49bef6a5da6d"},"source":["sns.set_style(\"darkgrid\")\r\n","plt.figure(figsize=(8, 4))\r\n","errors[\"errorID\"].value_counts().plot(kind=\"bar\",color = [\"black\", \"blue\",\"yellow\", \"green\", \"red\"], rot=0)\r\n","plt.title(\"Error Types Distribution\")\r\n","plt.ylabel(\"Count\")\r\n","plt.xlabel(\"Error Type\")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"L5a2IONJAtx6"},"source":["## Maintenance Data"]},{"cell_type":"markdown","metadata":{"id":"otOZmORoA1Ew"},"source":["Records that correspond to both regular inspections and failures. A record is generated if a component is replaced during the scheduled inspection or is replaced due to a failure. Records that are created due to failures will be called as such, failures. The maintenance data has records for both 2014 and 2015."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"ypUAeyarAzdY","executionInfo":{"status":"ok","timestamp":1615905876081,"user_tz":-60,"elapsed":521,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"ef352bd6-7d1c-4e72-fd42-233c3751ea02"},"source":["maint.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>comp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>1</td>\n","      <td>comp2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2014-07-16 06:00:00</td>\n","      <td>1</td>\n","      <td>comp4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>1</td>\n","      <td>comp3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>1</td>\n","      <td>comp1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-05 06:00:00</td>\n","      <td>1</td>\n","      <td>comp4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              datetime  machineID   comp\n","0  2014-06-01 06:00:00          1  comp2\n","1  2014-07-16 06:00:00          1  comp4\n","2  2014-07-31 06:00:00          1  comp3\n","3  2014-12-13 06:00:00          1  comp1\n","4  2015-01-05 06:00:00          1  comp4"]},"metadata":{"tags":[]},"execution_count":149}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"4mSzu347BE5n","executionInfo":{"status":"ok","timestamp":1615905876081,"user_tz":-60,"elapsed":514,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"e942606e-4e85-4227-816b-423d656a32b3"},"source":["maint.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>comp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3281</th>\n","      <td>2015-10-10 06:00:00</td>\n","      <td>100</td>\n","      <td>comp3</td>\n","    </tr>\n","    <tr>\n","      <th>3282</th>\n","      <td>2015-10-25 06:00:00</td>\n","      <td>100</td>\n","      <td>comp4</td>\n","    </tr>\n","    <tr>\n","      <th>3283</th>\n","      <td>2015-11-09 06:00:00</td>\n","      <td>100</td>\n","      <td>comp4</td>\n","    </tr>\n","    <tr>\n","      <th>3284</th>\n","      <td>2015-12-09 06:00:00</td>\n","      <td>100</td>\n","      <td>comp2</td>\n","    </tr>\n","    <tr>\n","      <th>3285</th>\n","      <td>2015-12-24 06:00:00</td>\n","      <td>100</td>\n","      <td>comp2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 datetime  machineID   comp\n","3281  2015-10-10 06:00:00        100  comp3\n","3282  2015-10-25 06:00:00        100  comp4\n","3283  2015-11-09 06:00:00        100  comp4\n","3284  2015-12-09 06:00:00        100  comp2\n","3285  2015-12-24 06:00:00        100  comp2"]},"metadata":{"tags":[]},"execution_count":150}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7dJ5nWXlBItc","executionInfo":{"status":"ok","timestamp":1615905876082,"user_tz":-60,"elapsed":510,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1b1fbe20-974d-482c-d0f2-613d82b5568c"},"source":["maint.info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 3286 entries, 0 to 3285\n","Data columns (total 3 columns):\n"," #   Column     Non-Null Count  Dtype \n","---  ------     --------------  ----- \n"," 0   datetime   3286 non-null   object\n"," 1   machineID  3286 non-null   int64 \n"," 2   comp       3286 non-null   object\n","dtypes: int64(1), object(2)\n","memory usage: 77.1+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B8b-TtfGBNu2","executionInfo":{"status":"ok","timestamp":1615905876082,"user_tz":-60,"elapsed":506,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"0589fea3-39c0-4d5e-bf8d-aa22f697f102"},"source":["maint[\"datetime\"] = pd.to_datetime(maint[\"datetime\"], format=\"%Y-%m-%d %H:%M:%S\")\r\n","maint[\"comp\"] = maint[\"comp\"].astype(\"category\")\r\n","maint.dtypes"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["datetime     datetime64[ns]\n","machineID             int64\n","comp               category\n","dtype: object"]},"metadata":{"tags":[]},"execution_count":152}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"8MZj99YQBV7H","executionInfo":{"status":"ok","timestamp":1615905876289,"user_tz":-60,"elapsed":709,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"14951695-1fc8-40ca-eef2-0996c213ba83"},"source":["sns.set_style(\"darkgrid\")\r\n","plt.figure(figsize=(8, 4))\r\n","maint[\"comp\"].value_counts().plot(kind=\"bar\", rot=0)\r\n","plt.title(\"Component Replacement Distribution\")\r\n","plt.ylabel(\"Quantity\")\r\n","plt.xlabel(\"Component\")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"yb7NYKqXCLc9"},"source":["## Machine Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"F0A49fDWCXiw","executionInfo":{"status":"ok","timestamp":1615847860187,"user_tz":-60,"elapsed":424,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"6a38f6fb-f2bb-4cdd-9a97-c1a9938d9141"},"source":["machines.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>model</th>\n","      <th>age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>model4</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>model3</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>model3</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>model3</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID   model  age\n","0          1  model3   18\n","1          2  model4    7\n","2          3  model3    8\n","3          4  model3    7\n","4          5  model3    2"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"dd0lO2LnCiMz","executionInfo":{"status":"ok","timestamp":1615847902898,"user_tz":-60,"elapsed":414,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"83281a07-831e-4441-b185-ca9dc7d1a58e"},"source":["machines.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>model</th>\n","      <th>age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>95</th>\n","      <td>96</td>\n","      <td>model2</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>96</th>\n","      <td>97</td>\n","      <td>model2</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>98</td>\n","      <td>model2</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>98</th>\n","      <td>99</td>\n","      <td>model1</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>99</th>\n","      <td>100</td>\n","      <td>model4</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    machineID   model  age\n","95         96  model2   10\n","96         97  model2   14\n","97         98  model2   20\n","98         99  model1   14\n","99        100  model4    5"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pWoV5zLDCnpz","executionInfo":{"status":"ok","timestamp":1615847921964,"user_tz":-60,"elapsed":392,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"5ab10295-112d-42fa-cf37-6e86413869f3"},"source":["machines.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(100, 3)"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lu4DIlVuCtqd","executionInfo":{"status":"ok","timestamp":1615847951925,"user_tz":-60,"elapsed":397,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"12bb3699-fe03-4d46-d42a-60df1c354d0a"},"source":["machines.dtypes"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["machineID     int64\n","model        object\n","age           int64\n","dtype: object"]},"metadata":{"tags":[]},"execution_count":48}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"zwGxDrgPC22j","executionInfo":{"status":"ok","timestamp":1615848024333,"user_tz":-60,"elapsed":753,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"03ece696-c370-40a0-fc10-3fd1894536bf"},"source":["machines[\"age\"].plot(kind=\"kde\")\r\n","plt.title(\"Age Distribution of Machines\")\r\n","plt.xlabel(\"Age\")\r\n","plt.ylabel(\"Density\")\r\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":[]}}]},{"cell_type":"markdown","metadata":{"id":"u8HiGRR-DYXe"},"source":["We check if there are several hills as it may suggest two different groups."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"ESBnQvnHDXRd","executionInfo":{"status":"ok","timestamp":1615848139906,"user_tz":-60,"elapsed":728,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"98d22f73-03d7-4931-d177-ec0046b49ca1"},"source":["np.log(machines[machines[\"age\"] != 0].iloc[:, 0]).plot(kind=\"kde\")\r\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":[]}}]},{"cell_type":"markdown","metadata":{"id":"xlUh2z7hDfUN"},"source":["We apply natural logarithm to normalize."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OiBJBSuhDuI6","executionInfo":{"status":"ok","timestamp":1615848205997,"user_tz":-60,"elapsed":426,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"8f24e42c-7169-4d7d-8c32-86d3703a8d25"},"source":["machines[\"model\"] = machines[\"model\"].astype(\"category\")\r\n","machines.dtypes"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["machineID       int64\n","model        category\n","age             int64\n","dtype: object"]},"metadata":{"tags":[]},"execution_count":53}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K28QHMijDyoY","executionInfo":{"status":"ok","timestamp":1615896207602,"user_tz":-60,"elapsed":1093,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"6826f2e1-3644-4c06-aad2-ed6abeff0f09"},"source":["plt.figure(figsize=(8, 6))\r\n","_, bins, _ = plt.hist([\r\n","    machines.loc[machines[\"model\"] == \"model1\", \"age\"],\r\n","    machines.loc[machines[\"model\"] == \"model2\", \"age\"],\r\n","    machines.loc[machines[\"model\"] == \"model3\", \"age\"],\r\n","    machines.loc[machines[\"model\"] == \"model4\", \"age\"]],\r\n","    20, stacked=True, label=[\"model1\", \"model2\", \"model3\", \"model4\"\r\n","])\r\n","plt.title(\"Age Distribution by Model\")\r\n","plt.xlabel(\"Age (YEARS)\")\r\n","plt.ylabel(\"Quantity\")\r\n","plt.legend()\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:\n","\n","Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n","\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"bzVX0kbl858O","executionInfo":{"status":"ok","timestamp":1615846510462,"user_tz":-60,"elapsed":392,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"38ce9a2a-56f5-48b9-d472-ceee44a1b61c"},"source":["machines.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>100.000000</td>\n","      <td>100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>50.500000</td>\n","      <td>11.330000</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>29.011492</td>\n","      <td>5.856974</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>25.750000</td>\n","      <td>6.750000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>50.500000</td>\n","      <td>12.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>75.250000</td>\n","      <td>16.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>100.000000</td>\n","      <td>20.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        machineID         age\n","count  100.000000  100.000000\n","mean    50.500000   11.330000\n","std     29.011492    5.856974\n","min      1.000000    0.000000\n","25%     25.750000    6.750000\n","50%     50.500000   12.000000\n","75%     75.250000   16.000000\n","max    100.000000   20.000000"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"pphtfCScEhrx"},"source":["## Failure Data"]},{"cell_type":"markdown","metadata":{"id":"FOs2ATYlEpjO"},"source":["These are records of component replacements due to failures. Each record has a date and time, machine ID and the failed component."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"g3yFzx5mEmqg","executionInfo":{"status":"ok","timestamp":1615907480206,"user_tz":-60,"elapsed":631,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"2cb5de95-c31e-47df-ab0d-4c00bd669a2c"},"source":["failures.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>failure</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-05 06:00:00</td>\n","      <td>1</td>\n","      <td>comp4</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-03-06 06:00:00</td>\n","      <td>1</td>\n","      <td>comp1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-04-20 06:00:00</td>\n","      <td>1</td>\n","      <td>comp2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-06-19 06:00:00</td>\n","      <td>1</td>\n","      <td>comp4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-09-02 06:00:00</td>\n","      <td>1</td>\n","      <td>comp4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              datetime  machineID failure\n","0  2015-01-05 06:00:00          1   comp4\n","1  2015-03-06 06:00:00          1   comp1\n","2  2015-04-20 06:00:00          1   comp2\n","3  2015-06-19 06:00:00          1   comp4\n","4  2015-09-02 06:00:00          1   comp4"]},"metadata":{"tags":[]},"execution_count":166}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"PNvN2Zn8EuUW","executionInfo":{"status":"ok","timestamp":1615907480207,"user_tz":-60,"elapsed":625,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"770133a9-a601-48b9-f5b2-2fdb6b042644"},"source":["failures.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>failure</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>756</th>\n","      <td>2015-11-29 06:00:00</td>\n","      <td>99</td>\n","      <td>comp3</td>\n","    </tr>\n","    <tr>\n","      <th>757</th>\n","      <td>2015-12-14 06:00:00</td>\n","      <td>99</td>\n","      <td>comp4</td>\n","    </tr>\n","    <tr>\n","      <th>758</th>\n","      <td>2015-02-12 06:00:00</td>\n","      <td>100</td>\n","      <td>comp1</td>\n","    </tr>\n","    <tr>\n","      <th>759</th>\n","      <td>2015-09-10 06:00:00</td>\n","      <td>100</td>\n","      <td>comp1</td>\n","    </tr>\n","    <tr>\n","      <th>760</th>\n","      <td>2015-12-09 06:00:00</td>\n","      <td>100</td>\n","      <td>comp2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                datetime  machineID failure\n","756  2015-11-29 06:00:00         99   comp3\n","757  2015-12-14 06:00:00         99   comp4\n","758  2015-02-12 06:00:00        100   comp1\n","759  2015-09-10 06:00:00        100   comp1\n","760  2015-12-09 06:00:00        100   comp2"]},"metadata":{"tags":[]},"execution_count":167}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pVK5CYiJEyaE","executionInfo":{"status":"ok","timestamp":1615907480207,"user_tz":-60,"elapsed":620,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"cf674331-6b70-4a19-a3ec-526bf0d92ffc"},"source":["failures.info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 761 entries, 0 to 760\n","Data columns (total 3 columns):\n"," #   Column     Non-Null Count  Dtype \n","---  ------     --------------  ----- \n"," 0   datetime   761 non-null    object\n"," 1   machineID  761 non-null    int64 \n"," 2   failure    761 non-null    object\n","dtypes: int64(1), object(2)\n","memory usage: 18.0+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YJ7q3K8rE6cA","executionInfo":{"status":"ok","timestamp":1615907480208,"user_tz":-60,"elapsed":616,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"bf14dccc-552b-4127-8ff9-11900d2aec9e"},"source":["failures[\"datetime\"] = pd.to_datetime(failures[\"datetime\"], format=\"%Y-%m-%d %H:%M:%S\")\r\n","failures[\"failure\"] = failures[\"failure\"].astype(\"category\")\r\n","failures.dtypes"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["datetime     datetime64[ns]\n","machineID             int64\n","failure            category\n","dtype: object"]},"metadata":{"tags":[]},"execution_count":169}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":390},"id":"7wwX6crXE-Lr","executionInfo":{"status":"ok","timestamp":1615907480208,"user_tz":-60,"elapsed":611,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"322cfce2-d090-4615-c64a-cd1b7f175b35"},"source":["failures.describe(include=\"all\", datetime_is_numeric=True)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>failure</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>761</td>\n","      <td>761.000000</td>\n","      <td>761</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>comp2</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>259</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>2015-06-24 12:12:17.976345856</td>\n","      <td>51.911958</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>2015-01-02 03:00:00</td>\n","      <td>1.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>2015-03-23 06:00:00</td>\n","      <td>24.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>2015-06-24 06:00:00</td>\n","      <td>51.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>2015-09-23 06:00:00</td>\n","      <td>79.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>2015-12-31 06:00:00</td>\n","      <td>100.000000</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>NaN</td>\n","      <td>29.515542</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                             datetime   machineID failure\n","count                             761  761.000000     761\n","unique                            NaN         NaN       4\n","top                               NaN         NaN   comp2\n","freq                              NaN         NaN     259\n","mean    2015-06-24 12:12:17.976345856   51.911958     NaN\n","min               2015-01-02 03:00:00    1.000000     NaN\n","25%               2015-03-23 06:00:00   24.000000     NaN\n","50%               2015-06-24 06:00:00   51.000000     NaN\n","75%               2015-09-23 06:00:00   79.000000     NaN\n","max               2015-12-31 06:00:00  100.000000     NaN\n","std                               NaN   29.515542     NaN"]},"metadata":{"tags":[]},"execution_count":170}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"1ergRSA1FtNo","executionInfo":{"status":"ok","timestamp":1615907480625,"user_tz":-60,"elapsed":1022,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"f41e3fc6-13b7-4c91-cc70-a2e91e4d09d6"},"source":["plt.figure(figsize=(8, 4))\r\n","failures[\"failure\"].value_counts().plot(kind=\"bar\", rot=0)\r\n","plt.title(\"Component Failure Distribution\")\r\n","plt.ylabel(\"Quantity\")\r\n","plt.xlabel(\"Components\")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"By70mNzpGS6X"},"source":["## Machine Location Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"nAjBRMWvGaJj","executionInfo":{"status":"ok","timestamp":1615848917831,"user_tz":-60,"elapsed":429,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"3e039471-575d-43d1-e88d-11ebe0fffe18"},"source":["machinesLocation.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>cityID</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>28</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>28</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>31</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>47</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID  cityID\n","0          1      28\n","1          2      28\n","2          3      20\n","3          4      31\n","4          5      47"]},"metadata":{"tags":[]},"execution_count":64}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"x8hmVTo8HadD","executionInfo":{"status":"ok","timestamp":1615849224313,"user_tz":-60,"elapsed":393,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"ddbfb28b-4182-472f-e4ce-5290e67b153e"},"source":["machinesLocation.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>cityID</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>95</th>\n","      <td>96</td>\n","      <td>37</td>\n","    </tr>\n","    <tr>\n","      <th>96</th>\n","      <td>97</td>\n","      <td>47</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>98</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>98</th>\n","      <td>99</td>\n","      <td>34</td>\n","    </tr>\n","    <tr>\n","      <th>99</th>\n","      <td>100</td>\n","      <td>14</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    machineID  cityID\n","95         96      37\n","96         97      47\n","97         98       9\n","98         99      34\n","99        100      14"]},"metadata":{"tags":[]},"execution_count":70}]},{"cell_type":"markdown","metadata":{"id":"lIJhXrmYGiGP"},"source":["## Cities Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"QsGqljOTGnkS","executionInfo":{"status":"ok","timestamp":1615848971766,"user_tz":-60,"elapsed":338,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1ed06416-9938-44cc-e95d-ab335133556e"},"source":["cities.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>cityID</th>\n","      <th>Name</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Genf</td>\n","      <td>46.204391</td>\n","      <td>6.143158</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>Basel</td>\n","      <td>47.559599</td>\n","      <td>7.588576</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>Bern</td>\n","      <td>46.947773</td>\n","      <td>7.441641</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>Lausanne</td>\n","      <td>46.519384</td>\n","      <td>6.635008</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   cityID      Name   Latitude  Longitude\n","0       1   Zuerich  47.374156   8.539632\n","1       2      Genf  46.204391   6.143158\n","2       3     Basel  47.559599   7.588576\n","3       4      Bern  46.947773   7.441641\n","4       5  Lausanne  46.519384   6.635008"]},"metadata":{"tags":[]},"execution_count":65}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"GHCFXcK1Gt7k","executionInfo":{"status":"ok","timestamp":1615848996452,"user_tz":-60,"elapsed":408,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1a309108-7895-4d5a-dc2c-1c071593f304"},"source":["cities.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>cityID</th>\n","      <th>Name</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>45</th>\n","      <td>46</td>\n","      <td>Vevey</td>\n","      <td>46.465435</td>\n","      <td>6.836549</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>47</td>\n","      <td>Buelach</td>\n","      <td>47.517963</td>\n","      <td>8.541800</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>48</td>\n","      <td>Reinach</td>\n","      <td>47.490682</td>\n","      <td>7.589437</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>49</td>\n","      <td>Baden</td>\n","      <td>47.472880</td>\n","      <td>8.308090</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>50</td>\n","      <td>Adliswil</td>\n","      <td>47.312435</td>\n","      <td>8.527046</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    cityID      Name   Latitude  Longitude\n","45      46     Vevey  46.465435   6.836549\n","46      47   Buelach  47.517963   8.541800\n","47      48   Reinach  47.490682   7.589437\n","48      49     Baden  47.472880   8.308090\n","49      50  Adliswil  47.312435   8.527046"]},"metadata":{"tags":[]},"execution_count":66}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PPOx6B4dGyO8","executionInfo":{"status":"ok","timestamp":1615849024602,"user_tz":-60,"elapsed":391,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"a42dbc55-29d9-4c39-f7fe-c624c765749d"},"source":["cities.dtypes"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["cityID         int64\n","Name          object\n","Latitude     float64\n","Longitude    float64\n","dtype: object"]},"metadata":{"tags":[]},"execution_count":68}]},{"cell_type":"markdown","metadata":{"id":"3wQmL9WfHg-O"},"source":["## Join Data (Machine, Location)"]},{"cell_type":"code","metadata":{"id":"MebCKDZNIxRP"},"source":["citiesMachinesLocation = cities.set_index('cityID').join(machinesLocation.set_index('cityID'))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6vUShLnPJNgX","executionInfo":{"status":"ok","timestamp":1615887451545,"user_tz":-60,"elapsed":549,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"c1558526-6a66-4cb9-c402-585d9e08e678"},"source":["citiesMachinesLocation.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","      <th>machineID</th>\n","    </tr>\n","    <tr>\n","      <th>cityID</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>9.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>11.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>26.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Genf</td>\n","      <td>46.204391</td>\n","      <td>6.143158</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Basel</td>\n","      <td>47.559599</td>\n","      <td>7.588576</td>\n","      <td>95.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           Name   Latitude  Longitude  machineID\n","cityID                                          \n","1       Zuerich  47.374156   8.539632        9.0\n","1       Zuerich  47.374156   8.539632       11.0\n","1       Zuerich  47.374156   8.539632       26.0\n","2          Genf  46.204391   6.143158        NaN\n","3         Basel  47.559599   7.588576       95.0"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7rzI2BdFJSPE","executionInfo":{"status":"ok","timestamp":1615887451803,"user_tz":-60,"elapsed":804,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"70cb39e3-f711-45c6-d327-f9abc7c47b4e"},"source":["citiesMachinesLocation.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","      <th>machineID</th>\n","    </tr>\n","    <tr>\n","      <th>cityID</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>48</th>\n","      <td>Reinach</td>\n","      <td>47.490682</td>\n","      <td>7.589437</td>\n","      <td>68.0</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>Baden</td>\n","      <td>47.472880</td>\n","      <td>8.308090</td>\n","      <td>27.0</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>Baden</td>\n","      <td>47.472880</td>\n","      <td>8.308090</td>\n","      <td>38.0</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>Adliswil</td>\n","      <td>47.312435</td>\n","      <td>8.527046</td>\n","      <td>58.0</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>Adliswil</td>\n","      <td>47.312435</td>\n","      <td>8.527046</td>\n","      <td>94.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            Name   Latitude  Longitude  machineID\n","cityID                                           \n","48       Reinach  47.490682   7.589437       68.0\n","49         Baden  47.472880   8.308090       27.0\n","49         Baden  47.472880   8.308090       38.0\n","50      Adliswil  47.312435   8.527046       58.0\n","50      Adliswil  47.312435   8.527046       94.0"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"7CE-jRMIJYKA"},"source":["\r\n","citiesMachinesLocation.dropna(subset = [\"machineID\"], inplace=True)\r\n","citiesMachinesLocation[\"machineID\"] = citiesMachinesLocation[\"machineID\"].astype(int)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_Ikp5JTpKAZj","executionInfo":{"status":"ok","timestamp":1615887451804,"user_tz":-60,"elapsed":800,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"252c169b-86ed-4f1f-cac1-1d09c42d329a"},"source":["citiesMachinesLocation.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","      <th>machineID</th>\n","    </tr>\n","    <tr>\n","      <th>cityID</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>26</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Basel</td>\n","      <td>47.559599</td>\n","      <td>7.588576</td>\n","      <td>95</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bern</td>\n","      <td>46.947773</td>\n","      <td>7.441641</td>\n","      <td>56</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           Name   Latitude  Longitude  machineID\n","cityID                                          \n","1       Zuerich  47.374156   8.539632          9\n","1       Zuerich  47.374156   8.539632         11\n","1       Zuerich  47.374156   8.539632         26\n","3         Basel  47.559599   7.588576         95\n","4          Bern  46.947773   7.441641         56"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"zVUTkUkUKsFc"},"source":["citiesMachinesModelLocation = citiesMachinesLocation.set_index('machineID').join(machines.set_index('machineID'))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QacayYoVLULJ","executionInfo":{"status":"ok","timestamp":1615887451804,"user_tz":-60,"elapsed":794,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"3de895e7-6f7c-443e-a5af-697003f51ea9"},"source":["citiesMachinesModelLocation.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","      <th>model</th>\n","      <th>age</th>\n","    </tr>\n","    <tr>\n","      <th>machineID</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>9</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>model4</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>model2</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>Zuerich</td>\n","      <td>47.374156</td>\n","      <td>8.539632</td>\n","      <td>model3</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>95</th>\n","      <td>Basel</td>\n","      <td>47.559599</td>\n","      <td>7.588576</td>\n","      <td>model2</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>56</th>\n","      <td>Bern</td>\n","      <td>46.947773</td>\n","      <td>7.441641</td>\n","      <td>model1</td>\n","      <td>10</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              Name   Latitude  Longitude   model  age\n","machineID                                            \n","9          Zuerich  47.374156   8.539632  model4    7\n","11         Zuerich  47.374156   8.539632  model2    6\n","26         Zuerich  47.374156   8.539632  model3    3\n","95           Basel  47.559599   7.588576  model2   18\n","56            Bern  46.947773   7.441641  model1   10"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vsDOApzALXSG","executionInfo":{"status":"ok","timestamp":1615887451805,"user_tz":-60,"elapsed":791,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"b696a2fc-4322-41a4-d29a-e4134f39b3ab"},"source":["citiesMachinesModelLocation.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","      <th>model</th>\n","      <th>age</th>\n","    </tr>\n","    <tr>\n","      <th>machineID</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>68</th>\n","      <td>Reinach</td>\n","      <td>47.490682</td>\n","      <td>7.589437</td>\n","      <td>model3</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>Baden</td>\n","      <td>47.472880</td>\n","      <td>8.308090</td>\n","      <td>model3</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>Baden</td>\n","      <td>47.472880</td>\n","      <td>8.308090</td>\n","      <td>model4</td>\n","      <td>15</td>\n","    </tr>\n","    <tr>\n","      <th>58</th>\n","      <td>Adliswil</td>\n","      <td>47.312435</td>\n","      <td>8.527046</td>\n","      <td>model1</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>94</th>\n","      <td>Adliswil</td>\n","      <td>47.312435</td>\n","      <td>8.527046</td>\n","      <td>model2</td>\n","      <td>18</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               Name   Latitude  Longitude   model  age\n","machineID                                             \n","68          Reinach  47.490682   7.589437  model3   10\n","27            Baden  47.472880   8.308090  model3    9\n","38            Baden  47.472880   8.308090  model4   15\n","58         Adliswil  47.312435   8.527046  model1    5\n","94         Adliswil  47.312435   8.527046  model2   18"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I4YsRGtFgbEZ","executionInfo":{"status":"ok","timestamp":1615889293891,"user_tz":-60,"elapsed":356,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"4dff0421-874e-46cc-b038-d7162b7bdcf9"},"source":["citiesMachinesModelLocation.info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 100 entries, 9 to 94\n","Data columns (total 5 columns):\n"," #   Column     Non-Null Count  Dtype  \n","---  ------     --------------  -----  \n"," 0   Name       100 non-null    object \n"," 1   Latitude   100 non-null    float64\n"," 2   Longitude  100 non-null    float64\n"," 3   model      100 non-null    object \n"," 4   age        100 non-null    int64  \n","dtypes: float64(2), int64(1), object(2)\n","memory usage: 9.7+ KB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Sjv1DYlpZVEX"},"source":["## Plot on Map"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"S3K0nR2ciHB5","executionInfo":{"status":"ok","timestamp":1615890280057,"user_tz":-60,"elapsed":396,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"39f41aaa-0b92-40c9-cd47-4a1dfe20179f"},"source":["citiesCountMachines = pd.DataFrame({'count' : citiesMachinesModelLocation.groupby( [ \"Name\", \"Longitude\", \"Latitude\"] ).size()}).reset_index()\r\n","citiesCountMachines.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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<td>47.472880</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Name  Longitude   Latitude  count\n","0      Aarau   8.049558  47.391383      2\n","1   Adliswil   8.527046  47.312435      2\n","2  Allschwil   7.543362  47.552892      5\n","3       Baar   8.526087  47.195373      2\n","4      Baden   8.308090  47.472880      2"]},"metadata":{"tags":[]},"execution_count":73}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":417},"id":"r55HdHgCYqac","executionInfo":{"status":"ok","timestamp":1615895960363,"user_tz":-60,"elapsed":696,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"9903a882-f037-44a4-949f-7bfc744d46ba"},"source":["fig = px.scatter_mapbox(citiesCountMachines, lat=\"Latitude\", lon=\"Longitude\", hover_name=\"Name\", color_discrete_sequence=[\"fuchsia\"], zoom=6, height=400, size=\"count\")\r\n","fig.update_layout(mapbox_style=\"open-street-map\")\r\n","fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\r\n","fig.show()\r\n","\r\n","#hover_data=[\"model\", \"age\"],"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    <div>\n","            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n","                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n","        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n","            <div id=\"eb6ab407-1bc6-4c2f-959b-b56584b8f468\" class=\"plotly-graph-div\" style=\"height:400px; width:100%;\"></div>\n"," 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Engineering"]},{"cell_type":"markdown","metadata":{"id":"WE1ab8uA7URL"},"source":["Telemetry data almost always come with time stamps, which makes them suitable for calculating lagging features (statistics of certain values within a time window).\r\n","\r\n","We will choose the size of a window and calculate the aggregate measures (mean, standard deviation, minimum, maximum, etc.) to represent the short-term history.\r\n","\r\n","We will then calculate the moving average and standard deviation of the telemetry data in the last 3-hour delay window."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9mx0SWnz7gDG","executionInfo":{"status":"ok","timestamp":1615896410050,"user_tz":-60,"elapsed":2698,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"989ebe2b-5aa8-46c6-f43b-5433599b089d"},"source":["temp = []\r\n","fields = [\"volt\", \"rotate\", \"pressure\", \"vibration\"]\r\n","\r\n","temp = [\r\n","    pd.pivot_table(\r\n","        telemetry,\r\n","        index=\"datetime\",\r\n","        columns=\"machineID\",\r\n","        values=col).resample(\"3H\", closed=\"left\", label=\"right\").mean().unstack()\r\n","    for col in fields\r\n","]\r\n","temp[0].head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["machineID  datetime           \n","1          2015-01-01 09:00:00    170.028993\n","           2015-01-01 12:00:00    164.192565\n","           2015-01-01 15:00:00    168.134445\n","           2015-01-01 18:00:00    165.514453\n","           2015-01-01 21:00:00    168.809347\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":83}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"jKXh7kSG7-Ct","executionInfo":{"status":"ok","timestamp":1615896605940,"user_tz":-60,"elapsed":391,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"f1f8e663-d2a5-4d10-c66a-a199123e6340"},"source":["telemetry_mean_3h = pd.concat(temp, axis=1) # Join the series.\r\n","telemetry_mean_3h.columns = [col + \"mean_3h\" for col in fields] # Assign column names.\r\n","telemetry_mean_3h.reset_index(inplace=True) # Flatten the frame.\r\n","telemetry_mean_3h.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2015-01-01 09:00:00</td>\n","      <td>170.028993</td>\n","      <td>449.533798</td>\n","      <td>94.592122</td>\n","      <td>40.893502</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2015-01-01 12:00:00</td>\n","      <td>164.192565</td>\n","      <td>403.949857</td>\n","      <td>105.687417</td>\n","      <td>34.255891</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2015-01-01 15:00:00</td>\n","      <td>168.134445</td>\n","      <td>435.781707</td>\n","      <td>107.793709</td>\n","      <td>41.239405</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2015-01-01 18:00:00</td>\n","      <td>165.514453</td>\n","      <td>430.472823</td>\n","      <td>101.703289</td>\n","      <td>40.373739</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2015-01-01 21:00:00</td>\n","      <td>168.809347</td>\n","      <td>437.111120</td>\n","      <td>90.911060</td>\n","      <td>41.738542</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID            datetime  ...  pressuremean_3h  vibrationmean_3h\n","0          1 2015-01-01 09:00:00  ...        94.592122         40.893502\n","1          1 2015-01-01 12:00:00  ...       105.687417         34.255891\n","2          1 2015-01-01 15:00:00  ...       107.793709         41.239405\n","3          1 2015-01-01 18:00:00  ...       101.703289         40.373739\n","4          1 2015-01-01 21:00:00  ...        90.911060         41.738542\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":86}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"h7ZA7ZVb8YAZ","executionInfo":{"status":"ok","timestamp":1615896611171,"user_tz":-60,"elapsed":379,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"50f0b393-f8bd-4e29-b1d5-d926a390d87e"},"source":["telemetry_mean_3h.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>292095</th>\n","      <td>100</td>\n","      <td>2015-12-31 21:00:00</td>\n","      <td>166.078945</td>\n","      <td>424.431184</td>\n","      <td>98.344409</td>\n","      <td>30.907459</td>\n","    </tr>\n","    <tr>\n","      <th>292096</th>\n","      <td>100</td>\n","      <td>2016-01-01 00:00:00</td>\n","      <td>158.327984</td>\n","      <td>504.056262</td>\n","      <td>108.055441</td>\n","      <td>40.109705</td>\n","    </tr>\n","    <tr>\n","      <th>292097</th>\n","      <td>100</td>\n","      <td>2016-01-01 03:00:00</td>\n","      <td>170.306956</td>\n","      <td>438.653616</td>\n","      <td>102.346227</td>\n","      <td>47.481232</td>\n","    </tr>\n","    <tr>\n","      <th>292098</th>\n","      <td>100</td>\n","      <td>2016-01-01 06:00:00</td>\n","      <td>182.525427</td>\n","      <td>435.932056</td>\n","      <td>98.798172</td>\n","      <td>42.757630</td>\n","    </tr>\n","    <tr>\n","      <th>292099</th>\n","      <td>100</td>\n","      <td>2016-01-01 09:00:00</td>\n","      <td>171.336037</td>\n","      <td>496.096870</td>\n","      <td>79.095538</td>\n","      <td>37.845245</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        machineID            datetime  ...  pressuremean_3h  vibrationmean_3h\n","292095        100 2015-12-31 21:00:00  ...        98.344409         30.907459\n","292096        100 2016-01-01 00:00:00  ...       108.055441         40.109705\n","292097        100 2016-01-01 03:00:00  ...       102.346227         47.481232\n","292098        100 2016-01-01 06:00:00  ...        98.798172         42.757630\n","292099        100 2016-01-01 09:00:00  ...        79.095538         37.845245\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":87}]},{"cell_type":"markdown","metadata":{"id":"t-cdHBBD9lL2"},"source":["Repeat for standard deviation."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CPZsCMNZ9cCq","executionInfo":{"status":"ok","timestamp":1615897224854,"user_tz":-60,"elapsed":2825,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"93a439a7-5eba-4e55-9b80-e0aad4ddaa56"},"source":["temp = [\r\n","    pd.pivot_table(\r\n","        telemetry,\r\n","        index=\"datetime\",\r\n","        columns=\"machineID\",\r\n","        values=col).resample(\"3H\", closed=\"left\", label=\"right\").std().unstack()\r\n","    for col in fields\r\n","]\r\n","temp[0].head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["machineID  datetime           \n","1          2015-01-01 09:00:00     6.721032\n","           2015-01-01 12:00:00     7.596570\n","           2015-01-01 15:00:00    10.124584\n","           2015-01-01 18:00:00     4.673269\n","           2015-01-01 21:00:00    14.752132\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":88}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"RZS2vvA--taH","executionInfo":{"status":"ok","timestamp":1615897230272,"user_tz":-60,"elapsed":388,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"e94d878b-b05b-4169-ebc8-49cb59358162"},"source":["telemetry_sd_3h = pd.concat(temp, axis=1)\r\n","telemetry_sd_3h.columns = [i + \"sd_3h\" for i in fields]\r\n","telemetry_sd_3h.reset_index(inplace=True)\r\n","telemetry_sd_3h.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2015-01-01 09:00:00</td>\n","      <td>6.721032</td>\n","      <td>67.849599</td>\n","      <td>18.934956</td>\n","      <td>5.874970</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2015-01-01 12:00:00</td>\n","      <td>7.596570</td>\n","      <td>50.120452</td>\n","      <td>8.555032</td>\n","      <td>7.662229</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2015-01-01 15:00:00</td>\n","      <td>10.124584</td>\n","      <td>55.084734</td>\n","      <td>5.909721</td>\n","      <td>5.169304</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2015-01-01 18:00:00</td>\n","      <td>4.673269</td>\n","      <td>42.047278</td>\n","      <td>4.554047</td>\n","      <td>2.106108</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2015-01-01 21:00:00</td>\n","      <td>14.752132</td>\n","      <td>47.048609</td>\n","      <td>4.244158</td>\n","      <td>2.207884</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID            datetime  ...  pressuresd_3h  vibrationsd_3h\n","0          1 2015-01-01 09:00:00  ...      18.934956        5.874970\n","1          1 2015-01-01 12:00:00  ...       8.555032        7.662229\n","2          1 2015-01-01 15:00:00  ...       5.909721        5.169304\n","3          1 2015-01-01 18:00:00  ...       4.554047        2.106108\n","4          1 2015-01-01 21:00:00  ...       4.244158        2.207884\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":89}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"GZi7vOqQ-w96","executionInfo":{"status":"ok","timestamp":1615897246326,"user_tz":-60,"elapsed":383,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"ae7dc3ed-f650-4d16-cf29-533f7d8c1714"},"source":["telemetry_sd_3h.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>292095</th>\n","      <td>100</td>\n","      <td>2015-12-31 21:00:00</td>\n","      <td>29.351231</td>\n","      <td>69.348245</td>\n","      <td>16.940674</td>\n","      <td>3.731256</td>\n","    </tr>\n","    <tr>\n","      <th>292096</th>\n","      <td>100</td>\n","      <td>2016-01-01 00:00:00</td>\n","      <td>9.605817</td>\n","      <td>78.036116</td>\n","      <td>9.154932</td>\n","      <td>3.379639</td>\n","    </tr>\n","    <tr>\n","      <th>292097</th>\n","      <td>100</td>\n","      <td>2016-01-01 03:00:00</td>\n","      <td>8.457322</td>\n","      <td>43.021553</td>\n","      <td>0.785721</td>\n","      <td>3.198928</td>\n","    </tr>\n","    <tr>\n","      <th>292098</th>\n","      <td>100</td>\n","      <td>2016-01-01 06:00:00</td>\n","      <td>14.835200</td>\n","      <td>19.208302</td>\n","      <td>5.002788</td>\n","      <td>6.836757</td>\n","    </tr>\n","    <tr>\n","      <th>292099</th>\n","      <td>100</td>\n","      <td>2016-01-01 09:00:00</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        machineID            datetime  ...  pressuresd_3h  vibrationsd_3h\n","292095        100 2015-12-31 21:00:00  ...      16.940674        3.731256\n","292096        100 2016-01-01 00:00:00  ...       9.154932        3.379639\n","292097        100 2016-01-01 03:00:00  ...       0.785721        3.198928\n","292098        100 2016-01-01 06:00:00  ...       5.002788        6.836757\n","292099        100 2016-01-01 09:00:00  ...            NaN             NaN\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":90}]},{"cell_type":"markdown","metadata":{"id":"hkxo3_bmBpQe"},"source":["To capture a longer-term effect, 24-hour lagging functions are also calculated.\r\n","We create new values with 24-hour averages, and then select the first result every 3 hours.\r\n","In this way we can link the results with the previous lagging features (calculated at 3 hours)."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pwkOUIk_BhcE","executionInfo":{"status":"ok","timestamp":1615898016755,"user_tz":-60,"elapsed":2771,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"7663cce2-159e-4bf6-c292-70df6fd8bd59"},"source":["temp = []\r\n","fields = [\"volt\", \"rotate\", \"pressure\", \"vibration\"]\r\n","\r\n","temp = [\r\n","    pd.pivot_table(\r\n","        telemetry,\r\n","        index=\"datetime\",\r\n","        columns=\"machineID\",\r\n","        values=col).rolling(window=24).mean().resample(\"3H\", closed=\"left\", label=\"right\").first().unstack()\r\n","    for col in fields\r\n","]\r\n","temp[0].head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["machineID  datetime           \n","1          2015-01-01 09:00:00   NaN\n","           2015-01-01 12:00:00   NaN\n","           2015-01-01 15:00:00   NaN\n","           2015-01-01 18:00:00   NaN\n","           2015-01-01 21:00:00   NaN\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":91}]},{"cell_type":"markdown","metadata":{"id":"E0S987kVB8VY"},"source":["Due to the moving average method, the first 23 records are null; they must be eliminated.\r\n","It does not occur at the end of the frame because rolling stops at the end.\r\n","We end up with a frame with less data than the original telemetry frame as well as the previous 3H frame."]},{"cell_type":"code","metadata":{"id":"ldD1GHfdByUj"},"source":["telemetry_mean_24h = pd.concat(temp, axis=1)\r\n","telemetry_mean_24h.columns = [i + \"mean_24h\" for i in fields]\r\n","telemetry_mean_24h.reset_index(inplace=True)\r\n","telemetry_mean_24h = telemetry_mean_24h.loc[-telemetry_mean_24h[\"voltmean_24h\"].isnull()]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"wsi8sruKCDxO","executionInfo":{"status":"ok","timestamp":1615898100967,"user_tz":-60,"elapsed":366,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"ad6de56e-e308-4426-db41-c52ac1a9be6d"},"source":["telemetry_mean_24h.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>7</th>\n","      <td>1</td>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>169.733809</td>\n","      <td>445.179865</td>\n","      <td>96.797113</td>\n","      <td>40.385160</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>170.614862</td>\n","      <td>446.364859</td>\n","      <td>96.849785</td>\n","      <td>39.736826</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>169.893965</td>\n","      <td>447.009407</td>\n","      <td>97.715600</td>\n","      <td>39.498374</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1</td>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>171.243444</td>\n","      <td>444.233563</td>\n","      <td>96.666060</td>\n","      <td>40.229370</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>1</td>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>170.792486</td>\n","      <td>448.440437</td>\n","      <td>95.766838</td>\n","      <td>40.055214</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    machineID            datetime  ...  pressuremean_24h  vibrationmean_24h\n","7           1 2015-01-02 06:00:00  ...         96.797113          40.385160\n","8           1 2015-01-02 09:00:00  ...         96.849785          39.736826\n","9           1 2015-01-02 12:00:00  ...         97.715600          39.498374\n","10          1 2015-01-02 15:00:00  ...         96.666060          40.229370\n","11          1 2015-01-02 18:00:00  ...         95.766838          40.055214\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":94}]},{"cell_type":"markdown","metadata":{"id":"_hUybYUZCZCU"},"source":["Repeat for standard deviation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WXWDihSVCbV1","executionInfo":{"status":"ok","timestamp":1615898201829,"user_tz":-60,"elapsed":2781,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"10f08ef1-b73f-4864-9bb5-8313a9addd81"},"source":["temp = []\r\n","fields = [\"volt\", \"rotate\", \"pressure\", \"vibration\"]\r\n","\r\n","temp = [\r\n","    pd.pivot_table(\r\n","        telemetry,\r\n","        index=\"datetime\",\r\n","        columns=\"machineID\",\r\n","        values=col).rolling(window=24).std().resample(\"3H\", closed=\"left\", label=\"right\").first().unstack(level=-1)\r\n","    for col in fields\r\n","]\r\n","temp[0].head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["machineID  datetime           \n","1          2015-01-01 09:00:00   NaN\n","           2015-01-01 12:00:00   NaN\n","           2015-01-01 15:00:00   NaN\n","           2015-01-01 18:00:00   NaN\n","           2015-01-01 21:00:00   NaN\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":95}]},{"cell_type":"code","metadata":{"id":"Sr3xXOz7CfAS"},"source":["telemetry_sd_24h = pd.concat(temp, axis=1)\r\n","telemetry_sd_24h.columns = [i + \"sd_24h\" for i in fields]\r\n","telemetry_sd_24h.reset_index(inplace=True)\r\n","telemetry_sd_24h = telemetry_sd_24h.loc[-telemetry_sd_24h[\"voltsd_24h\"].isnull()]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":359},"id":"h9ttbca5CgfA","executionInfo":{"status":"ok","timestamp":1615898220506,"user_tz":-60,"elapsed":378,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"0b41191f-4a96-4952-93d2-8e52f64035a9"},"source":["telemetry_sd_24h.head(10)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>7</th>\n","      <td>1</td>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>11.233120</td>\n","      <td>48.717395</td>\n","      <td>10.079880</td>\n","      <td>5.853209</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>12.519402</td>\n","      <td>48.385076</td>\n","      <td>10.171540</td>\n","      <td>6.163231</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>13.370357</td>\n","      <td>42.432317</td>\n","      <td>9.471669</td>\n","      <td>6.195076</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1</td>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>13.299281</td>\n","      <td>41.346121</td>\n","      <td>8.731229</td>\n","      <td>5.687944</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>1</td>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>13.954518</td>\n","      <td>43.490234</td>\n","      <td>8.061653</td>\n","      <td>5.898069</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>1</td>\n","      <td>2015-01-02 21:00:00</td>\n","      <td>14.402740</td>\n","      <td>42.626186</td>\n","      <td>10.408012</td>\n","      <td>5.941890</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>1</td>\n","      <td>2015-01-03 00:00:00</td>\n","      <td>15.513819</td>\n","      <td>40.395881</td>\n","      <td>10.833294</td>\n","      <td>5.737671</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>1</td>\n","      <td>2015-01-03 03:00:00</td>\n","      <td>15.726970</td>\n","      <td>39.648116</td>\n","      <td>11.904700</td>\n","      <td>5.601191</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>1</td>\n","      <td>2015-01-03 06:00:00</td>\n","      <td>15.635083</td>\n","      <td>41.828592</td>\n","      <td>11.326412</td>\n","      <td>5.583521</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>1</td>\n","      <td>2015-01-03 09:00:00</td>\n","      <td>13.995465</td>\n","      <td>40.843882</td>\n","      <td>11.036546</td>\n","      <td>5.561553</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    machineID            datetime  ...  pressuresd_24h  vibrationsd_24h\n","7           1 2015-01-02 06:00:00  ...       10.079880         5.853209\n","8           1 2015-01-02 09:00:00  ...       10.171540         6.163231\n","9           1 2015-01-02 12:00:00  ...        9.471669         6.195076\n","10          1 2015-01-02 15:00:00  ...        8.731229         5.687944\n","11          1 2015-01-02 18:00:00  ...        8.061653         5.898069\n","12          1 2015-01-02 21:00:00  ...       10.408012         5.941890\n","13          1 2015-01-03 00:00:00  ...       10.833294         5.737671\n","14          1 2015-01-03 03:00:00  ...       11.904700         5.601191\n","15          1 2015-01-03 06:00:00  ...       11.326412         5.583521\n","16          1 2015-01-03 09:00:00  ...       11.036546         5.561553\n","\n","[10 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":97}]},{"cell_type":"markdown","metadata":{"id":"PEWuzrgmCuju"},"source":["Combine the features created so far.\r\n","We take the values 2:6 to avoid duplicate IDs and dates.\r\n","\"axis=0\" we move in the direction of the rows, \"axis=1\", we move in the direction of the columns."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"GYHqro1gC5dF","executionInfo":{"status":"ok","timestamp":1615898324556,"user_tz":-60,"elapsed":398,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"5a75a370-cc3d-42d7-ce81-34cf14cecf7f"},"source":["telemetry_feat = pd.concat([\r\n","    telemetry_mean_3h,\r\n","    telemetry_sd_3h.iloc[:, 2:6],\r\n","    telemetry_mean_24h.iloc[:, 2:6],\r\n","    telemetry_sd_24h.iloc[:, 2:6]], axis=1).dropna()\r\n","telemetry_feat.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>7</th>\n","      <td>1</td>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>180.133784</td>\n","      <td>440.608320</td>\n","      <td>94.137969</td>\n","      <td>41.551544</td>\n","      <td>21.322735</td>\n","      <td>48.770512</td>\n","      <td>2.135684</td>\n","      <td>10.037208</td>\n","      <td>169.733809</td>\n","      <td>445.179865</td>\n","      <td>96.797113</td>\n","      <td>40.385160</td>\n","      <td>11.233120</td>\n","      <td>48.717395</td>\n","      <td>10.079880</td>\n","      <td>5.853209</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>176.364293</td>\n","      <td>439.349655</td>\n","      <td>101.553209</td>\n","      <td>36.105580</td>\n","      <td>18.952210</td>\n","      <td>51.329636</td>\n","      <td>13.789279</td>\n","      <td>6.737739</td>\n","      <td>170.614862</td>\n","      <td>446.364859</td>\n","      <td>96.849785</td>\n","      <td>39.736826</td>\n","      <td>12.519402</td>\n","      <td>48.385076</td>\n","      <td>10.171540</td>\n","      <td>6.163231</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>160.384568</td>\n","      <td>424.385316</td>\n","      <td>99.598722</td>\n","      <td>36.094637</td>\n","      <td>13.047080</td>\n","      <td>13.702496</td>\n","      <td>9.988609</td>\n","      <td>1.639962</td>\n","      <td>169.893965</td>\n","      <td>447.009407</td>\n","      <td>97.715600</td>\n","      <td>39.498374</td>\n","      <td>13.370357</td>\n","      <td>42.432317</td>\n","      <td>9.471669</td>\n","      <td>6.195076</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1</td>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>170.472461</td>\n","      <td>442.933997</td>\n","      <td>102.380586</td>\n","      <td>40.483002</td>\n","      <td>16.642354</td>\n","      <td>56.290447</td>\n","      <td>3.305739</td>\n","      <td>8.854145</td>\n","      <td>171.243444</td>\n","      <td>444.233563</td>\n","      <td>96.666060</td>\n","      <td>40.229370</td>\n","      <td>13.299281</td>\n","      <td>41.346121</td>\n","      <td>8.731229</td>\n","      <td>5.687944</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>1</td>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>163.263806</td>\n","      <td>468.937558</td>\n","      <td>102.726648</td>\n","      <td>40.921802</td>\n","      <td>17.424688</td>\n","      <td>38.680380</td>\n","      <td>9.105775</td>\n","      <td>3.060781</td>\n","      <td>170.792486</td>\n","      <td>448.440437</td>\n","      <td>95.766838</td>\n","      <td>40.055214</td>\n","      <td>13.954518</td>\n","      <td>43.490234</td>\n","      <td>8.061653</td>\n","      <td>5.898069</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    machineID            datetime  ...  pressuresd_24h  vibrationsd_24h\n","7           1 2015-01-02 06:00:00  ...       10.079880         5.853209\n","8           1 2015-01-02 09:00:00  ...       10.171540         6.163231\n","9           1 2015-01-02 12:00:00  ...        9.471669         6.195076\n","10          1 2015-01-02 15:00:00  ...        8.731229         5.687944\n","11          1 2015-01-02 18:00:00  ...        8.061653         5.898069\n","\n","[5 rows x 18 columns]"]},"metadata":{"tags":[]},"execution_count":98}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"5y0dziwpC_b3","executionInfo":{"status":"ok","timestamp":1615898357990,"user_tz":-60,"elapsed":388,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"ca36456f-df6f-42d9-d3aa-e6f5905eef05"},"source":["telemetry_feat.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>292094</th>\n","      <td>100</td>\n","      <td>2015-12-31 18:00:00</td>\n","      <td>177.758754</td>\n","      <td>458.391629</td>\n","      <td>103.183160</td>\n","      <td>46.846592</td>\n","      <td>13.147011</td>\n","      <td>75.100940</td>\n","      <td>3.111700</td>\n","      <td>3.576693</td>\n","      <td>169.764418</td>\n","      <td>454.478787</td>\n","      <td>100.873360</td>\n","      <td>39.094339</td>\n","      <td>15.331755</td>\n","      <td>60.839923</td>\n","      <td>7.891711</td>\n","      <td>5.269038</td>\n","    </tr>\n","    <tr>\n","      <th>292095</th>\n","      <td>100</td>\n","      <td>2015-12-31 21:00:00</td>\n","      <td>166.078945</td>\n","      <td>424.431184</td>\n","      <td>98.344409</td>\n","      <td>30.907459</td>\n","      <td>29.351231</td>\n","      <td>69.348245</td>\n","      <td>16.940674</td>\n","      <td>3.731256</td>\n","      <td>171.547643</td>\n","      <td>452.351565</td>\n","      <td>101.789714</td>\n","      <td>39.184285</td>\n","      <td>16.429023</td>\n","      <td>62.147934</td>\n","      <td>7.475540</td>\n","      <td>5.448962</td>\n","    </tr>\n","    <tr>\n","      <th>292096</th>\n","      <td>100</td>\n","      <td>2016-01-01 00:00:00</td>\n","      <td>158.327984</td>\n","      <td>504.056262</td>\n","      <td>108.055441</td>\n","      <td>40.109705</td>\n","      <td>9.605817</td>\n","      <td>78.036116</td>\n","      <td>9.154932</td>\n","      <td>3.379639</td>\n","      <td>168.806645</td>\n","      <td>459.502212</td>\n","      <td>100.919285</td>\n","      <td>38.150193</td>\n","      <td>17.019808</td>\n","      <td>64.730136</td>\n","      <td>8.961444</td>\n","      <td>5.833191</td>\n","    </tr>\n","    <tr>\n","      <th>292097</th>\n","      <td>100</td>\n","      <td>2016-01-01 03:00:00</td>\n","      <td>170.306956</td>\n","      <td>438.653616</td>\n","      <td>102.346227</td>\n","      <td>47.481232</td>\n","      <td>8.457322</td>\n","      <td>43.021553</td>\n","      <td>0.785721</td>\n","      <td>3.198928</td>\n","      <td>169.011763</td>\n","      <td>465.923073</td>\n","      <td>101.568092</td>\n","      <td>39.456302</td>\n","      <td>17.096392</td>\n","      <td>64.718132</td>\n","      <td>9.420879</td>\n","      <td>5.738756</td>\n","    </tr>\n","    <tr>\n","      <th>292098</th>\n","      <td>100</td>\n","      <td>2016-01-01 06:00:00</td>\n","      <td>182.525427</td>\n","      <td>435.932056</td>\n","      <td>98.798172</td>\n","      <td>42.757630</td>\n","      <td>14.835200</td>\n","      <td>19.208302</td>\n","      <td>5.002788</td>\n","      <td>6.836757</td>\n","      <td>172.838745</td>\n","      <td>461.095362</td>\n","      <td>101.396728</td>\n","      <td>39.964090</td>\n","      <td>15.119775</td>\n","      <td>65.929509</td>\n","      <td>8.836617</td>\n","      <td>6.139142</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        machineID            datetime  ...  pressuresd_24h  vibrationsd_24h\n","292094        100 2015-12-31 18:00:00  ...        7.891711         5.269038\n","292095        100 2015-12-31 21:00:00  ...        7.475540         5.448962\n","292096        100 2016-01-01 00:00:00  ...        8.961444         5.833191\n","292097        100 2016-01-01 03:00:00  ...        9.420879         5.738756\n","292098        100 2016-01-01 06:00:00  ...        8.836617         6.139142\n","\n","[5 rows x 18 columns]"]},"metadata":{"tags":[]},"execution_count":99}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":317},"id":"eGkbkFcmDT64","executionInfo":{"status":"ok","timestamp":1615898429722,"user_tz":-60,"elapsed":563,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"d97badd1-414e-4443-fffe-62a72b01b34e"},"source":["telemetry_feat.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>291300.00000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>50.50000</td>\n","      <td>170.771416</td>\n","      <td>446.612973</td>\n","      <td>100.855301</td>\n","      <td>40.384328</td>\n","      <td>13.299858</td>\n","      <td>44.456335</td>\n","      <td>8.885822</td>\n","      <td>4.440660</td>\n","      <td>170.772579</td>\n","      <td>446.613440</td>\n","      <td>100.854547</td>\n","      <td>40.384564</td>\n","      <td>14.918849</td>\n","      <td>49.947916</td>\n","      <td>10.046687</td>\n","      <td>5.002065</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>28.86612</td>\n","      <td>9.497201</td>\n","      <td>33.122865</td>\n","      <td>7.408854</td>\n","      <td>3.476641</td>\n","      <td>6.966647</td>\n","      <td>23.216421</td>\n","      <td>4.656341</td>\n","      <td>2.320066</td>\n","      <td>4.716970</td>\n","      <td>18.074248</td>\n","      <td>4.733098</td>\n","      <td>2.059861</td>\n","      <td>2.260726</td>\n","      <td>7.682126</td>\n","      <td>1.713726</td>\n","      <td>0.799820</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.00000</td>\n","      <td>125.532506</td>\n","      <td>211.811184</td>\n","      <td>72.118639</td>\n","      <td>26.569635</td>\n","      <td>0.025509</td>\n","      <td>0.078991</td>\n","      <td>0.027417</td>\n","      <td>0.015278</td>\n","      <td>155.812721</td>\n","      <td>266.010419</td>\n","      <td>91.057429</td>\n","      <td>35.060087</td>\n","      <td>6.380619</td>\n","      <td>18.385248</td>\n","      <td>4.145308</td>\n","      <td>2.144863</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>25.75000</td>\n","      <td>164.445576</td>\n","      <td>427.568132</td>\n","      <td>96.238173</td>\n","      <td>38.147839</td>\n","      <td>8.027455</td>\n","      <td>26.903567</td>\n","      <td>5.370647</td>\n","      <td>2.684308</td>\n","      <td>168.070890</td>\n","      <td>441.549802</td>\n","      <td>98.668589</td>\n","      <td>39.354062</td>\n","      <td>13.358663</td>\n","      <td>44.668824</td>\n","      <td>8.924317</td>\n","      <td>4.460347</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>50.50000</td>\n","      <td>170.430790</td>\n","      <td>448.385564</td>\n","      <td>100.233139</td>\n","      <td>40.146085</td>\n","      <td>12.495595</td>\n","      <td>41.795700</td>\n","      <td>8.345785</td>\n","      <td>4.173854</td>\n","      <td>170.210913</td>\n","      <td>449.212554</td>\n","      <td>100.097694</td>\n","      <td>40.072752</td>\n","      <td>14.853948</td>\n","      <td>49.613807</td>\n","      <td>9.921407</td>\n","      <td>4.958594</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>75.25000</td>\n","      <td>176.607051</td>\n","      <td>468.447680</td>\n","      <td>104.404520</td>\n","      <td>42.227392</td>\n","      <td>17.689118</td>\n","      <td>59.103715</td>\n","      <td>11.789645</td>\n","      <td>5.898997</td>\n","      <td>172.459612</td>\n","      <td>456.368008</td>\n","      <td>101.611121</td>\n","      <td>40.833364</td>\n","      <td>16.394764</td>\n","      <td>54.821318</td>\n","      <td>10.980498</td>\n","      <td>5.484480</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>100.00000</td>\n","      <td>241.420717</td>\n","      <td>586.682904</td>\n","      <td>162.309656</td>\n","      <td>69.311324</td>\n","      <td>58.444332</td>\n","      <td>179.903039</td>\n","      <td>35.659369</td>\n","      <td>18.305595</td>\n","      <td>220.782618</td>\n","      <td>499.096975</td>\n","      <td>152.310351</td>\n","      <td>61.932124</td>\n","      <td>27.664538</td>\n","      <td>103.819404</td>\n","      <td>28.654103</td>\n","      <td>12.325783</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          machineID    voltmean_3h  ...  pressuresd_24h  vibrationsd_24h\n","count  291300.00000  291300.000000  ...   291300.000000    291300.000000\n","mean       50.50000     170.771416  ...       10.046687         5.002065\n","std        28.86612       9.497201  ...        1.713726         0.799820\n","min         1.00000     125.532506  ...        4.145308         2.144863\n","25%        25.75000     164.445576  ...        8.924317         4.460347\n","50%        50.50000     170.430790  ...        9.921407         4.958594\n","75%        75.25000     176.607051  ...       10.980498         5.484480\n","max       100.00000     241.420717  ...       28.654103        12.325783\n","\n","[8 rows x 17 columns]"]},"metadata":{"tags":[]},"execution_count":100}]},{"cell_type":"markdown","metadata":{"id":"TO-Aem-dDcoZ"},"source":["## Lag Features in Errors"]},{"cell_type":"markdown","metadata":{"id":"VcDMcmd3DjmG"},"source":["Like telemetry data, errors come with time stamps. We count the number of errors of each type in a delayed window."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"244OnLtgEGhY","executionInfo":{"status":"ok","timestamp":1615898646082,"user_tz":-60,"elapsed":388,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"fd10cdf7-1e69-4af9-9c4b-813037ca9ee2"},"source":["errors.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>errorID</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-03 07:00:00</td>\n","      <td>1</td>\n","      <td>error1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-03 20:00:00</td>\n","      <td>1</td>\n","      <td>error3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-04 06:00:00</td>\n","      <td>1</td>\n","      <td>error5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-10 15:00:00</td>\n","      <td>1</td>\n","      <td>error4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-22 10:00:00</td>\n","      <td>1</td>\n","      <td>error4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              datetime  machineID errorID\n","0  2015-01-03 07:00:00          1  error1\n","1  2015-01-03 20:00:00          1  error3\n","2  2015-01-04 06:00:00          1  error5\n","3  2015-01-10 15:00:00          1  error4\n","4  2015-01-22 10:00:00          1  error4"]},"metadata":{"tags":[]},"execution_count":103}]},{"cell_type":"markdown","metadata":{"id":"FXb43KnvElyo"},"source":["We begin by reformatting the error data so that we have one entry per machine per time\r\n","when at least one error occurred.\r\n","We create a column for each type of error."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":514},"id":"gTZB6rFaDlHw","executionInfo":{"status":"ok","timestamp":1615904342550,"user_tz":-60,"elapsed":453,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"2c059ed5-cc67-4ae5-ca0b-6041bcde0902"},"source":["error_count = pd.get_dummies(errors) # Put a 1 if the error appears for that machine, 0 otherwise.\r\n","error_count.columns = [\"datetime\", \"machineID\", \"error1\", \"error2\", \"error3\", \"error4\", \"error5\"]\r\n","error_count.head(15)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>error1</th>\n","      <th>error2</th>\n","      <th>error3</th>\n","      <th>error4</th>\n","      <th>error5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-03 07:00:00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-03 20:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-04 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-10 15:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-22 10:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>2015-01-25 15:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2015-01-27 04:00:00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>2015-03-03 22:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>2015-03-05 06:00:00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2015-03-20 18:00:00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2015-03-26 01:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>2015-03-31 23:00:00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>2015-04-19 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>2015-04-19 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>2015-04-29 19:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              datetime  machineID  error1  error2  error3  error4  error5\n","0  2015-01-03 07:00:00          1       1       0       0       0       0\n","1  2015-01-03 20:00:00          1       0       0       1       0       0\n","2  2015-01-04 06:00:00          1       0       0       0       0       1\n","3  2015-01-10 15:00:00          1       0       0       0       1       0\n","4  2015-01-22 10:00:00          1       0       0       0       1       0\n","5  2015-01-25 15:00:00          1       0       0       0       1       0\n","6  2015-01-27 04:00:00          1       1       0       0       0       0\n","7  2015-03-03 22:00:00          1       0       1       0       0       0\n","8  2015-03-05 06:00:00          1       1       0       0       0       0\n","9  2015-03-20 18:00:00          1       1       0       0       0       0\n","10 2015-03-26 01:00:00          1       0       1       0       0       0\n","11 2015-03-31 23:00:00          1       1       0       0       0       0\n","12 2015-04-19 06:00:00          1       0       1       0       0       0\n","13 2015-04-19 06:00:00          1       0       0       1       0       0\n","14 2015-04-29 19:00:00          1       0       0       0       1       0"]},"metadata":{"tags":[]},"execution_count":122}]},{"cell_type":"markdown","metadata":{"id":"FRNkGidBa2TO"},"source":["Dataframe dates are repeated, so we group by date.\r\n","We combine errors for a given machine at a specific time.\r\n","We do summation in case there are multiple errors of the same type at the same time, but not expected."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":514},"id":"GfzBGePRa3zo","executionInfo":{"status":"ok","timestamp":1615904921147,"user_tz":-60,"elapsed":355,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"d7dd846b-0723-4216-e524-4f3e0d5d56e1"},"source":["error_count_grouped = error_count.groupby([\"machineID\", \"datetime\"]).sum().reset_index()\r\n","error_count_grouped.head(15)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>error1</th>\n","      <th>error2</th>\n","      <th>error3</th>\n","      <th>error4</th>\n","      <th>error5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2015-01-03 07:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2015-01-03 20:00:00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2015-01-04 06:00:00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2015-01-10 15:00:00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2015-01-22 10:00:00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1</td>\n","      <td>2015-01-25 15:00:00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1</td>\n","      <td>2015-01-27 04:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1</td>\n","      <td>2015-03-03 22:00:00</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>2015-03-05 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>2015-03-20 18:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1</td>\n","      <td>2015-03-26 01:00:00</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>1</td>\n","      <td>2015-03-31 23:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>1</td>\n","      <td>2015-04-19 06:00:00</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>1</td>\n","      <td>2015-04-29 19:00:00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>1</td>\n","      <td>2015-05-04 23:00:00</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    machineID            datetime  error1  error2  error3  error4  error5\n","0           1 2015-01-03 07:00:00       1       0       0       0       0\n","1           1 2015-01-03 20:00:00       0       0       1       0       0\n","2           1 2015-01-04 06:00:00       0       0       0       0       1\n","3           1 2015-01-10 15:00:00       0       0       0       1       0\n","4           1 2015-01-22 10:00:00       0       0       0       1       0\n","5           1 2015-01-25 15:00:00       0       0       0       1       0\n","6           1 2015-01-27 04:00:00       1       0       0       0       0\n","7           1 2015-03-03 22:00:00       0       1       0       0       0\n","8           1 2015-03-05 06:00:00       1       0       0       0       0\n","9           1 2015-03-20 18:00:00       1       0       0       0       0\n","10          1 2015-03-26 01:00:00       0       1       0       0       0\n","11          1 2015-03-31 23:00:00       1       0       0       0       0\n","12          1 2015-04-19 06:00:00       0       1       1       0       0\n","13          1 2015-04-29 19:00:00       0       0       0       1       0\n","14          1 2015-05-04 23:00:00       0       1       0       0       0"]},"metadata":{"tags":[]},"execution_count":129}]},{"cell_type":"markdown","metadata":{"id":"_Jd4luoecstP"},"source":["Check that the registered errors exist on the available machines by filling with 0\r\n","the non-matches so we only search for matches with datetime and machineID."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"eLFawtqGaesI","executionInfo":{"status":"ok","timestamp":1615905052432,"user_tz":-60,"elapsed":583,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"215443cf-1a45-49d0-8f3d-11c0cbf5b262"},"source":["error_count_filtered = telemetry[[\"datetime\", \"machineID\"]].merge(\r\n","    error_count_grouped,\r\n","    on=[\"machineID\", \"datetime\"],\r\n","    how=\"left\"\r\n",").fillna(0.0)\r\n","\r\n","error_count_filtered.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>error1</th>\n","      <th>error2</th>\n","      <th>error3</th>\n","      <th>error4</th>\n","      <th>error5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-01 06:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-01 07:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-01 08:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-01 09:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-01 10:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             datetime  machineID  error1  error2  error3  error4  error5\n","0 2015-01-01 06:00:00          1     0.0     0.0     0.0     0.0     0.0\n","1 2015-01-01 07:00:00          1     0.0     0.0     0.0     0.0     0.0\n","2 2015-01-01 08:00:00          1     0.0     0.0     0.0     0.0     0.0\n","3 2015-01-01 09:00:00          1     0.0     0.0     0.0     0.0     0.0\n","4 2015-01-01 10:00:00          1     0.0     0.0     0.0     0.0     0.0"]},"metadata":{"tags":[]},"execution_count":141}]},{"cell_type":"markdown","metadata":{"id":"-5wj8C-cczLY"},"source":["Check for anomalies."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"9aRNK397c1kh","executionInfo":{"status":"ok","timestamp":1615905126195,"user_tz":-60,"elapsed":576,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"800f4312-c80e-44fa-87d4-d2b3a2a15582"},"source":["error_count_filtered.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>error1</th>\n","      <th>error2</th>\n","      <th>error3</th>\n","      <th>error4</th>\n","      <th>error5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>50.500000</td>\n","      <td>0.001153</td>\n","      <td>0.001128</td>\n","      <td>0.000957</td>\n","      <td>0.000830</td>\n","      <td>0.000406</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>28.866087</td>\n","      <td>0.033934</td>\n","      <td>0.033563</td>\n","      <td>0.030913</td>\n","      <td>0.028795</td>\n","      <td>0.020154</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>25.750000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>50.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>75.250000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>100.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           machineID         error1  ...         error4         error5\n","count  876100.000000  876100.000000  ...  876100.000000  876100.000000\n","mean       50.500000       0.001153  ...       0.000830       0.000406\n","std        28.866087       0.033934  ...       0.028795       0.020154\n","min         1.000000       0.000000  ...       0.000000       0.000000\n","25%        25.750000       0.000000  ...       0.000000       0.000000\n","50%        50.500000       0.000000  ...       0.000000       0.000000\n","75%        75.250000       0.000000  ...       0.000000       0.000000\n","max       100.000000       1.000000  ...       1.000000       1.000000\n","\n","[8 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":142}]},{"cell_type":"markdown","metadata":{"id":"o1jzXqfodlb8"},"source":["Calculate the total number of errors for each type of error during 24-hour periods. \r\n","We will take points every 3 hours."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jAuLYc6Gdqjf","executionInfo":{"status":"ok","timestamp":1615905343835,"user_tz":-60,"elapsed":3338,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"9df77911-b4ee-46db-a8e3-6f0c07b8a3a8"},"source":["temp = []\r\n","fields = [\r\n","    \"error%d\" % i\r\n","    for i in range(1,6)\r\n","]\r\n","\r\n","temp = [\r\n","    pd.pivot_table(\r\n","        error_count_filtered,\r\n","        index=\"datetime\",\r\n","        columns=\"machineID\",\r\n","        values=col).rolling(window=24).sum().resample(\"3H\", closed=\"left\", label=\"right\").first().unstack()\r\n","    for col in fields\r\n","]\r\n","temp[0].head(10)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["machineID  datetime           \n","1          2015-01-01 09:00:00    NaN\n","           2015-01-01 12:00:00    NaN\n","           2015-01-01 15:00:00    NaN\n","           2015-01-01 18:00:00    NaN\n","           2015-01-01 21:00:00    NaN\n","           2015-01-02 00:00:00    NaN\n","           2015-01-02 03:00:00    NaN\n","           2015-01-02 06:00:00    0.0\n","           2015-01-02 09:00:00    0.0\n","           2015-01-02 12:00:00    0.0\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":143}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"V3Y7bomJdv9A","executionInfo":{"status":"ok","timestamp":1615905360627,"user_tz":-60,"elapsed":461,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"292ead90-7dcb-44ab-96cb-48e0150cf70d"},"source":["error_count_total = pd.concat(temp, axis=1)\r\n","error_count_total.columns = [i + \"count\" for i in fields]\r\n","error_count_total.reset_index(inplace=True)\r\n","error_count_total = error_count_total.dropna()\r\n","error_count_total.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>error1count</th>\n","      <th>error2count</th>\n","      <th>error3count</th>\n","      <th>error4count</th>\n","      <th>error5count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>7</th>\n","      <td>1</td>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1</td>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>1</td>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    machineID            datetime  ...  error4count  error5count\n","7           1 2015-01-02 06:00:00  ...          0.0          0.0\n","8           1 2015-01-02 09:00:00  ...          0.0          0.0\n","9           1 2015-01-02 12:00:00  ...          0.0          0.0\n","10          1 2015-01-02 15:00:00  ...          0.0          0.0\n","11          1 2015-01-02 18:00:00  ...          0.0          0.0\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":144}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wA-_wCnqd1OU","executionInfo":{"status":"ok","timestamp":1615905424383,"user_tz":-60,"elapsed":417,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"44539d92-1508-4a83-eafc-3973deef19e0"},"source":["error_count_total[\"error2count\"].unique()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0., 1., 2.])"]},"metadata":{"tags":[]},"execution_count":148}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"WTTiDCasd7Lf","executionInfo":{"status":"ok","timestamp":1615905406200,"user_tz":-60,"elapsed":372,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"57eaf640-b26f-43f1-9e4a-4462c9648e3d"},"source":["error_count_total.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>error1count</th>\n","      <th>error2count</th>\n","      <th>error3count</th>\n","      <th>error4count</th>\n","      <th>error5count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>291400.00000</td>\n","      <td>291400.000000</td>\n","      <td>291400.000000</td>\n","      <td>291400.000000</td>\n","      <td>291400.000000</td>\n","      <td>291400.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>50.50000</td>\n","      <td>0.027649</td>\n","      <td>0.027069</td>\n","      <td>0.022907</td>\n","      <td>0.019904</td>\n","      <td>0.009753</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>28.86612</td>\n","      <td>0.166273</td>\n","      <td>0.164429</td>\n","      <td>0.151453</td>\n","      <td>0.140820</td>\n","      <td>0.098797</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>25.75000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>50.50000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>75.25000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>100.00000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          machineID    error1count  ...    error4count    error5count\n","count  291400.00000  291400.000000  ...  291400.000000  291400.000000\n","mean       50.50000       0.027649  ...       0.019904       0.009753\n","std        28.86612       0.166273  ...       0.140820       0.098797\n","min         1.00000       0.000000  ...       0.000000       0.000000\n","25%        25.75000       0.000000  ...       0.000000       0.000000\n","50%        50.50000       0.000000  ...       0.000000       0.000000\n","75%        75.25000       0.000000  ...       0.000000       0.000000\n","max       100.00000       2.000000  ...       2.000000       2.000000\n","\n","[8 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":146}]},{"cell_type":"markdown","metadata":{"id":"WrFMqNmCfbzf"},"source":["## Time Since Last Maintenance Replacement"]},{"cell_type":"markdown","metadata":{"id":"OE_vc6o5fqVx"},"source":["Maintenance records contain information from component replacement records. Possible characteristics of this data set may be to calculate how long it has been since a component was last replaced, since the longer a component is used, the greater the degradation.\r\n","\r\n","In this section, domain knowledge plays an important role in understanding the predictive factors of a problem. In the following, the days since the last component replacement are calculated for each component type as characteristics from the maintenance data."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"2cqKb4THfwcG","executionInfo":{"status":"ok","timestamp":1615905892856,"user_tz":-60,"elapsed":387,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"35991438-451d-4e2a-952d-51782921123a"},"source":["maint.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>comp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>1</td>\n","      <td>comp2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2014-07-16 06:00:00</td>\n","      <td>1</td>\n","      <td>comp4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>1</td>\n","      <td>comp3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>1</td>\n","      <td>comp1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-05 06:00:00</td>\n","      <td>1</td>\n","      <td>comp4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             datetime  machineID   comp\n","0 2014-06-01 06:00:00          1  comp2\n","1 2014-07-16 06:00:00          1  comp4\n","2 2014-07-31 06:00:00          1  comp3\n","3 2014-12-13 06:00:00          1  comp1\n","4 2015-01-05 06:00:00          1  comp4"]},"metadata":{"tags":[]},"execution_count":154}]},{"cell_type":"markdown","metadata":{"id":"99I3-xV3f2VL"},"source":["Create a column for each type of error"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"OWCK3qHNf4Pn","executionInfo":{"status":"ok","timestamp":1615905925118,"user_tz":-60,"elapsed":399,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"0cd911a5-c082-43cf-ed0e-84ea7131b474"},"source":["comp_rep = pd.get_dummies(maint)\r\n","comp_rep.columns = [\"datetime\", \"machineID\", \"comp1\", \"comp2\", \"comp3\", \"comp4\"]\r\n","comp_rep.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2014-07-16 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-05 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             datetime  machineID  comp1  comp2  comp3  comp4\n","0 2014-06-01 06:00:00          1      0      1      0      0\n","1 2014-07-16 06:00:00          1      0      0      0      1\n","2 2014-07-31 06:00:00          1      0      0      1      0\n","3 2014-12-13 06:00:00          1      1      0      0      0\n","4 2015-01-05 06:00:00          1      0      0      0      1"]},"metadata":{"tags":[]},"execution_count":155}]},{"cell_type":"markdown","metadata":{"id":"f_4TxghEgI2p"},"source":["Combine repairs for a certain machine at a certain time.\r\n","If we do not group by date we can see another perspective.\r\n","We find which components fail together, since we group by date."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"GgqQEwvUgiMG","executionInfo":{"status":"ok","timestamp":1615906091187,"user_tz":-60,"elapsed":373,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"ffbd0160-601c-4591-adb9-5a9f127b8e32"},"source":["comp_rep = comp_rep.groupby([\"machineID\", \"datetime\"]).sum().reset_index()\r\n","comp_rep.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2014-07-16 06:00:00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2015-01-05 06:00:00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID            datetime  comp1  comp2  comp3  comp4\n","0          1 2014-06-01 06:00:00      0      1      0      0\n","1          1 2014-07-16 06:00:00      0      0      0      1\n","2          1 2014-07-31 06:00:00      0      0      1      0\n","3          1 2014-12-13 06:00:00      1      0      0      0\n","4          1 2015-01-05 06:00:00      1      0      0      1"]},"metadata":{"tags":[]},"execution_count":156}]},{"cell_type":"markdown","metadata":{"id":"MnQwkJsQgugc"},"source":["The times when there were no replacements have to be added"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"IXQuCDTogxkB","executionInfo":{"status":"ok","timestamp":1615906160071,"user_tz":-60,"elapsed":823,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"6fe458e1-2a02-43ad-ea99-11b57f4b712b"},"source":["comp_rep = telemetry[[\"datetime\", \"machineID\"]].merge(\r\n","    comp_rep,\r\n","    on=[\"datetime\", \"machineID\"],\r\n","    how=\"outer\").fillna(0).sort_values(by=[\"machineID\", \"datetime\"]\r\n",")\r\n","comp_rep.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>876100</th>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>876101</th>\n","      <td>2014-07-16 06:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>876102</th>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>876103</th>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-01 06:00:00</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  datetime  machineID  comp1  comp2  comp3  comp4\n","876100 2014-06-01 06:00:00          1    0.0    1.0    0.0    0.0\n","876101 2014-07-16 06:00:00          1    0.0    0.0    0.0    1.0\n","876102 2014-07-31 06:00:00          1    0.0    0.0    1.0    0.0\n","876103 2014-12-13 06:00:00          1    1.0    0.0    0.0    0.0\n","0      2015-01-01 06:00:00          1    0.0    0.0    0.0    0.0"]},"metadata":{"tags":[]},"execution_count":157}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"nr33Uk45g5Oc","executionInfo":{"status":"ok","timestamp":1615906322041,"user_tz":-60,"elapsed":4079,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1eec89db-b4ed-4e68-c47a-b715e9038a8a"},"source":["components = [\"comp1\", \"comp2\", \"comp3\", \"comp4\"]\r\n","for comp in components:\r\n","    # We want to obtain the date of the most recent component change.\r\n","    comp_rep.loc[comp_rep[comp] < 1, comp] = None # We fill with null the samples without replacement.\r\n","    # the dates of the entries that do have replacements.\r\n","    comp_rep.loc[-comp_rep[comp].isnull(), comp] = comp_rep.loc[-comp_rep[comp].isnull(), \"datetime\"]\r\n","    # We do a forward-fill of the most recent dates of a component change.\r\n","    # We fill with the last valid value found top-bottom.\r\n","    comp_rep[comp] = pd.to_datetime(comp_rep[comp].fillna(method=\"ffill\"))\r\n","\r\n","# We eliminated samples from 2014, they could have nulls, the maintenance started that year.\r\n","comp_rep = comp_rep.loc[comp_rep[\"datetime\"] > pd.to_datetime(\"2015-01-01\")]\r\n","comp_rep.head(50)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-01 06:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-01 07:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-01 08:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-01 09:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-01 10:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>2015-01-01 11:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2015-01-01 12:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>2015-01-01 13:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>2015-01-01 14:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2015-01-01 15:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2015-01-01 16:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>2015-01-01 17:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>2015-01-01 18:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>2015-01-01 19:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>2015-01-01 20:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>2015-01-01 21:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>2015-01-01 22:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>2015-01-01 23:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>2015-01-02 00:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>2015-01-02 01:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>2015-01-02 02:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>2015-01-02 03:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>2015-01-02 04:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>2015-01-02 05:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>2015-01-02 07:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>2015-01-02 08:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>2015-01-02 10:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>2015-01-02 11:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>2015-01-02 13:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>2015-01-02 14:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>2015-01-02 16:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>2015-01-02 17:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>2015-01-02 19:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>2015-01-02 20:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>2015-01-02 21:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>2015-01-02 22:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>2015-01-02 23:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>2015-01-03 00:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>2015-01-03 01:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>2015-01-03 02:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>2015-01-03 03:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>2015-01-03 04:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>2015-01-03 05:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>2015-01-03 06:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>2015-01-03 07:00:00</td>\n","      <td>1</td>\n","      <td>2014-12-13 06:00:00</td>\n","      <td>2014-06-01 06:00:00</td>\n","      <td>2014-07-31 06:00:00</td>\n","      <td>2014-07-16 06:00:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              datetime  machineID  ...               comp3               comp4\n","0  2015-01-01 06:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","1  2015-01-01 07:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","2  2015-01-01 08:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","3  2015-01-01 09:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","4  2015-01-01 10:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","5  2015-01-01 11:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","6  2015-01-01 12:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","7  2015-01-01 13:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","8  2015-01-01 14:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","9  2015-01-01 15:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","10 2015-01-01 16:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","11 2015-01-01 17:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","12 2015-01-01 18:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","13 2015-01-01 19:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","14 2015-01-01 20:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","15 2015-01-01 21:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","16 2015-01-01 22:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","17 2015-01-01 23:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","18 2015-01-02 00:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","19 2015-01-02 01:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","20 2015-01-02 02:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","21 2015-01-02 03:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","22 2015-01-02 04:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","23 2015-01-02 05:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","24 2015-01-02 06:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","25 2015-01-02 07:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","26 2015-01-02 08:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","27 2015-01-02 09:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","28 2015-01-02 10:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","29 2015-01-02 11:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","30 2015-01-02 12:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","31 2015-01-02 13:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","32 2015-01-02 14:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","33 2015-01-02 15:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","34 2015-01-02 16:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","35 2015-01-02 17:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","36 2015-01-02 18:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","37 2015-01-02 19:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","38 2015-01-02 20:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","39 2015-01-02 21:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","40 2015-01-02 22:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","41 2015-01-02 23:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","42 2015-01-03 00:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","43 2015-01-03 01:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","44 2015-01-03 02:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","45 2015-01-03 03:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","46 2015-01-03 04:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","47 2015-01-03 05:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","48 2015-01-03 06:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","49 2015-01-03 07:00:00          1  ... 2014-07-31 06:00:00 2014-07-16 06:00:00\n","\n","[50 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":158}]},{"cell_type":"markdown","metadata":{"id":"mIRsxmHThoSw"},"source":["Replace the most recent change dates with the number of days since the most recent change."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"raC_dNiJhjei","executionInfo":{"status":"ok","timestamp":1615906394754,"user_tz":-60,"elapsed":553,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"126bd1b4-280a-4961-a8a6-2121d1c02a5d"},"source":["for comp in components: comp_rep[comp] = (comp_rep[\"datetime\"] - pd.to_datetime(comp_rep[comp])) / np.timedelta64(1, \"D\")\r\n","comp_rep.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>datetime</th>\n","      <th>machineID</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2015-01-01 06:00:00</td>\n","      <td>1</td>\n","      <td>19.000000</td>\n","      <td>214.000000</td>\n","      <td>154.000000</td>\n","      <td>169.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2015-01-01 07:00:00</td>\n","      <td>1</td>\n","      <td>19.041667</td>\n","      <td>214.041667</td>\n","      <td>154.041667</td>\n","      <td>169.041667</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2015-01-01 08:00:00</td>\n","      <td>1</td>\n","      <td>19.083333</td>\n","      <td>214.083333</td>\n","      <td>154.083333</td>\n","      <td>169.083333</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2015-01-01 09:00:00</td>\n","      <td>1</td>\n","      <td>19.125000</td>\n","      <td>214.125000</td>\n","      <td>154.125000</td>\n","      <td>169.125000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015-01-01 10:00:00</td>\n","      <td>1</td>\n","      <td>19.166667</td>\n","      <td>214.166667</td>\n","      <td>154.166667</td>\n","      <td>169.166667</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             datetime  machineID      comp1       comp2       comp3       comp4\n","0 2015-01-01 06:00:00          1  19.000000  214.000000  154.000000  169.000000\n","1 2015-01-01 07:00:00          1  19.041667  214.041667  154.041667  169.041667\n","2 2015-01-01 08:00:00          1  19.083333  214.083333  154.083333  169.083333\n","3 2015-01-01 09:00:00          1  19.125000  214.125000  154.125000  169.125000\n","4 2015-01-01 10:00:00          1  19.166667  214.166667  154.166667  169.166667"]},"metadata":{"tags":[]},"execution_count":159}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"Kx3ICJ_ehzWo","executionInfo":{"status":"ok","timestamp":1615906422947,"user_tz":-60,"elapsed":379,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1699cbd1-cc30-4ce4-d7b0-a58cd5260ff3"},"source":["comp_rep.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","      <td>876100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>50.500000</td>\n","      <td>53.525185</td>\n","      <td>51.540806</td>\n","      <td>52.725962</td>\n","      <td>53.834191</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>28.866087</td>\n","      <td>62.491679</td>\n","      <td>59.269254</td>\n","      <td>58.873114</td>\n","      <td>59.707978</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>25.750000</td>\n","      <td>13.291667</td>\n","      <td>12.125000</td>\n","      <td>13.125000</td>\n","      <td>13.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>50.500000</td>\n","      <td>32.791667</td>\n","      <td>29.666667</td>\n","      <td>32.291667</td>\n","      <td>32.500000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>75.250000</td>\n","      <td>68.708333</td>\n","      <td>66.541667</td>\n","      <td>67.333333</td>\n","      <td>70.458333</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>100.000000</td>\n","      <td>491.958333</td>\n","      <td>348.958333</td>\n","      <td>370.958333</td>\n","      <td>394.958333</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["           machineID          comp1  ...          comp3          comp4\n","count  876100.000000  876100.000000  ...  876100.000000  876100.000000\n","mean       50.500000      53.525185  ...      52.725962      53.834191\n","std        28.866087      62.491679  ...      58.873114      59.707978\n","min         1.000000       0.000000  ...       0.000000       0.000000\n","25%        25.750000      13.291667  ...      13.125000      13.000000\n","50%        50.500000      32.791667  ...      32.291667      32.500000\n","75%        75.250000      68.708333  ...      67.333333      70.458333\n","max       100.000000     491.958333  ...     370.958333     394.958333\n","\n","[8 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":160}]},{"cell_type":"markdown","metadata":{"id":"NaYbh-n9iEnq"},"source":["## Machine Features"]},{"cell_type":"markdown","metadata":{"id":"K9fAozSLiNdS"},"source":["The features of the machines can be used without additional modifications. These include descriptive information on the type of each machine and its age (number of years in service)."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"_qYAwmLUiQx8","executionInfo":{"status":"ok","timestamp":1615906572169,"user_tz":-60,"elapsed":821,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"34c394a1-40f8-41af-fdc6-54addb587dc9"},"source":["# Join all the created features together.\r\n","final_feat = telemetry_feat.merge(error_count_total, on=[\"datetime\", \"machineID\"], how=\"left\")\r\n","final_feat = final_feat.merge(comp_rep, on=[\"datetime\", \"machineID\"], how=\"left\")\r\n","final_feat = final_feat.merge(machines, on=[\"machineID\"], how=\"left\")\r\n","final_feat.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","      <th>error1count</th>\n","      <th>error2count</th>\n","      <th>error3count</th>\n","      <th>error4count</th>\n","      <th>error5count</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","      <th>model</th>\n","      <th>age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>180.133784</td>\n","      <td>440.608320</td>\n","      <td>94.137969</td>\n","      <td>41.551544</td>\n","      <td>21.322735</td>\n","      <td>48.770512</td>\n","      <td>2.135684</td>\n","      <td>10.037208</td>\n","      <td>169.733809</td>\n","      <td>445.179865</td>\n","      <td>96.797113</td>\n","      <td>40.385160</td>\n","      <td>11.233120</td>\n","      <td>48.717395</td>\n","      <td>10.079880</td>\n","      <td>5.853209</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.000</td>\n","      <td>215.000</td>\n","      <td>155.000</td>\n","      <td>170.000</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>176.364293</td>\n","      <td>439.349655</td>\n","      <td>101.553209</td>\n","      <td>36.105580</td>\n","      <td>18.952210</td>\n","      <td>51.329636</td>\n","      <td>13.789279</td>\n","      <td>6.737739</td>\n","      <td>170.614862</td>\n","      <td>446.364859</td>\n","      <td>96.849785</td>\n","      <td>39.736826</td>\n","      <td>12.519402</td>\n","      <td>48.385076</td>\n","      <td>10.171540</td>\n","      <td>6.163231</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.125</td>\n","      <td>215.125</td>\n","      <td>155.125</td>\n","      <td>170.125</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>160.384568</td>\n","      <td>424.385316</td>\n","      <td>99.598722</td>\n","      <td>36.094637</td>\n","      <td>13.047080</td>\n","      <td>13.702496</td>\n","      <td>9.988609</td>\n","      <td>1.639962</td>\n","      <td>169.893965</td>\n","      <td>447.009407</td>\n","      <td>97.715600</td>\n","      <td>39.498374</td>\n","      <td>13.370357</td>\n","      <td>42.432317</td>\n","      <td>9.471669</td>\n","      <td>6.195076</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.250</td>\n","      <td>215.250</td>\n","      <td>155.250</td>\n","      <td>170.250</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>170.472461</td>\n","      <td>442.933997</td>\n","      <td>102.380586</td>\n","      <td>40.483002</td>\n","      <td>16.642354</td>\n","      <td>56.290447</td>\n","      <td>3.305739</td>\n","      <td>8.854145</td>\n","      <td>171.243444</td>\n","      <td>444.233563</td>\n","      <td>96.666060</td>\n","      <td>40.229370</td>\n","      <td>13.299281</td>\n","      <td>41.346121</td>\n","      <td>8.731229</td>\n","      <td>5.687944</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.375</td>\n","      <td>215.375</td>\n","      <td>155.375</td>\n","      <td>170.375</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>163.263806</td>\n","      <td>468.937558</td>\n","      <td>102.726648</td>\n","      <td>40.921802</td>\n","      <td>17.424688</td>\n","      <td>38.680380</td>\n","      <td>9.105775</td>\n","      <td>3.060781</td>\n","      <td>170.792486</td>\n","      <td>448.440437</td>\n","      <td>95.766838</td>\n","      <td>40.055214</td>\n","      <td>13.954518</td>\n","      <td>43.490234</td>\n","      <td>8.061653</td>\n","      <td>5.898069</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.500</td>\n","      <td>215.500</td>\n","      <td>155.500</td>\n","      <td>170.500</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID            datetime  voltmean_3h  ...    comp4   model  age\n","0          1 2015-01-02 06:00:00   180.133784  ...  170.000  model3   18\n","1          1 2015-01-02 09:00:00   176.364293  ...  170.125  model3   18\n","2          1 2015-01-02 12:00:00   160.384568  ...  170.250  model3   18\n","3          1 2015-01-02 15:00:00   170.472461  ...  170.375  model3   18\n","4          1 2015-01-02 18:00:00   163.263806  ...  170.500  model3   18\n","\n","[5 rows x 29 columns]"]},"metadata":{"tags":[]},"execution_count":161}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"jjWeCMtBiwUW","executionInfo":{"status":"ok","timestamp":1615906689014,"user_tz":-60,"elapsed":380,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"c6cc18d0-2584-4882-8f6e-c2942247e675"},"source":["final_feat.tail()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","      <th>error1count</th>\n","      <th>error2count</th>\n","      <th>error3count</th>\n","      <th>error4count</th>\n","      <th>error5count</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","      <th>model</th>\n","      <th>age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>291295</th>\n","      <td>100</td>\n","      <td>2015-12-31 18:00:00</td>\n","      <td>177.758754</td>\n","      <td>458.391629</td>\n","      <td>103.183160</td>\n","      <td>46.846592</td>\n","      <td>13.147011</td>\n","      <td>75.100940</td>\n","      <td>3.111700</td>\n","      <td>3.576693</td>\n","      <td>169.764418</td>\n","      <td>454.478787</td>\n","      <td>100.873360</td>\n","      <td>39.094339</td>\n","      <td>15.331755</td>\n","      <td>60.839923</td>\n","      <td>7.891711</td>\n","      <td>5.269038</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>82.500</td>\n","      <td>7.500</td>\n","      <td>82.500</td>\n","      <td>52.500</td>\n","      <td>model4</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>291296</th>\n","      <td>100</td>\n","      <td>2015-12-31 21:00:00</td>\n","      <td>166.078945</td>\n","      <td>424.431184</td>\n","      <td>98.344409</td>\n","      <td>30.907459</td>\n","      <td>29.351231</td>\n","      <td>69.348245</td>\n","      <td>16.940674</td>\n","      <td>3.731256</td>\n","      <td>171.547643</td>\n","      <td>452.351565</td>\n","      <td>101.789714</td>\n","      <td>39.184285</td>\n","      <td>16.429023</td>\n","      <td>62.147934</td>\n","      <td>7.475540</td>\n","      <td>5.448962</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>82.625</td>\n","      <td>7.625</td>\n","      <td>82.625</td>\n","      <td>52.625</td>\n","      <td>model4</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>291297</th>\n","      <td>100</td>\n","      <td>2016-01-01 00:00:00</td>\n","      <td>158.327984</td>\n","      <td>504.056262</td>\n","      <td>108.055441</td>\n","      <td>40.109705</td>\n","      <td>9.605817</td>\n","      <td>78.036116</td>\n","      <td>9.154932</td>\n","      <td>3.379639</td>\n","      <td>168.806645</td>\n","      <td>459.502212</td>\n","      <td>100.919285</td>\n","      <td>38.150193</td>\n","      <td>17.019808</td>\n","      <td>64.730136</td>\n","      <td>8.961444</td>\n","      <td>5.833191</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>82.750</td>\n","      <td>7.750</td>\n","      <td>82.750</td>\n","      <td>52.750</td>\n","      <td>model4</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>291298</th>\n","      <td>100</td>\n","      <td>2016-01-01 03:00:00</td>\n","      <td>170.306956</td>\n","      <td>438.653616</td>\n","      <td>102.346227</td>\n","      <td>47.481232</td>\n","      <td>8.457322</td>\n","      <td>43.021553</td>\n","      <td>0.785721</td>\n","      <td>3.198928</td>\n","      <td>169.011763</td>\n","      <td>465.923073</td>\n","      <td>101.568092</td>\n","      <td>39.456302</td>\n","      <td>17.096392</td>\n","      <td>64.718132</td>\n","      <td>9.420879</td>\n","      <td>5.738756</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>82.875</td>\n","      <td>7.875</td>\n","      <td>82.875</td>\n","      <td>52.875</td>\n","      <td>model4</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>291299</th>\n","      <td>100</td>\n","      <td>2016-01-01 06:00:00</td>\n","      <td>182.525427</td>\n","      <td>435.932056</td>\n","      <td>98.798172</td>\n","      <td>42.757630</td>\n","      <td>14.835200</td>\n","      <td>19.208302</td>\n","      <td>5.002788</td>\n","      <td>6.836757</td>\n","      <td>172.838745</td>\n","      <td>461.095362</td>\n","      <td>101.396728</td>\n","      <td>39.964090</td>\n","      <td>15.119775</td>\n","      <td>65.929509</td>\n","      <td>8.836617</td>\n","      <td>6.139142</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>83.000</td>\n","      <td>8.000</td>\n","      <td>83.000</td>\n","      <td>53.000</td>\n","      <td>model4</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        machineID            datetime  voltmean_3h  ...   comp4   model  age\n","291295        100 2015-12-31 18:00:00   177.758754  ...  52.500  model4    5\n","291296        100 2015-12-31 21:00:00   166.078945  ...  52.625  model4    5\n","291297        100 2016-01-01 00:00:00   158.327984  ...  52.750  model4    5\n","291298        100 2016-01-01 03:00:00   170.306956  ...  52.875  model4    5\n","291299        100 2016-01-01 06:00:00   182.525427  ...  53.000  model4    5\n","\n","[5 rows x 29 columns]"]},"metadata":{"tags":[]},"execution_count":162}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":317},"id":"Nv2oW_Cai6Cm","executionInfo":{"status":"ok","timestamp":1615906713119,"user_tz":-60,"elapsed":1042,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"4add00cd-7cf2-4a78-c109-d2dfbd5098fa"},"source":["final_feat.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","      <th>error1count</th>\n","      <th>error2count</th>\n","      <th>error3count</th>\n","      <th>error4count</th>\n","      <th>error5count</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","      <th>age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>291300.00000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","      <td>291300.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>50.50000</td>\n","      <td>170.771416</td>\n","      <td>446.612973</td>\n","      <td>100.855301</td>\n","      <td>40.384328</td>\n","      <td>13.299858</td>\n","      <td>44.456335</td>\n","      <td>8.885822</td>\n","      <td>4.440660</td>\n","      <td>170.772579</td>\n","      <td>446.613440</td>\n","      <td>100.854547</td>\n","      <td>40.384564</td>\n","      <td>14.918849</td>\n","      <td>49.947916</td>\n","      <td>10.046687</td>\n","      <td>5.002065</td>\n","      <td>0.027659</td>\n","      <td>0.027075</td>\n","      <td>0.022901</td>\n","      <td>0.019907</td>\n","      <td>0.009756</td>\n","      <td>53.350748</td>\n","      <td>51.318963</td>\n","      <td>52.512108</td>\n","      <td>53.619118</td>\n","      <td>11.330000</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>28.86612</td>\n","      <td>9.497201</td>\n","      <td>33.122865</td>\n","      <td>7.408854</td>\n","      <td>3.476641</td>\n","      <td>6.966647</td>\n","      <td>23.216421</td>\n","      <td>4.656341</td>\n","      <td>2.320066</td>\n","      <td>4.716970</td>\n","      <td>18.074248</td>\n","      <td>4.733098</td>\n","      <td>2.059861</td>\n","      <td>2.260726</td>\n","      <td>7.682126</td>\n","      <td>1.713726</td>\n","      <td>0.799820</td>\n","      <td>0.166301</td>\n","      <td>0.164446</td>\n","      <td>0.151435</td>\n","      <td>0.140832</td>\n","      <td>0.098813</td>\n","      <td>62.418063</td>\n","      <td>59.140743</td>\n","      <td>58.765904</td>\n","      <td>59.606886</td>\n","      <td>5.827625</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.00000</td>\n","      <td>125.532506</td>\n","      <td>211.811184</td>\n","      <td>72.118639</td>\n","      <td>26.569635</td>\n","      <td>0.025509</td>\n","      <td>0.078991</td>\n","      <td>0.027417</td>\n","      <td>0.015278</td>\n","      <td>155.812721</td>\n","      <td>266.010419</td>\n","      <td>91.057429</td>\n","      <td>35.060087</td>\n","      <td>6.380619</td>\n","      <td>18.385248</td>\n","      <td>4.145308</td>\n","      <td>2.144863</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>25.75000</td>\n","      <td>164.445576</td>\n","      <td>427.568132</td>\n","      <td>96.238173</td>\n","      <td>38.147839</td>\n","      <td>8.027455</td>\n","      <td>26.903567</td>\n","      <td>5.370647</td>\n","      <td>2.684308</td>\n","      <td>168.070890</td>\n","      <td>441.549802</td>\n","      <td>98.668589</td>\n","      <td>39.354062</td>\n","      <td>13.358663</td>\n","      <td>44.668824</td>\n","      <td>8.924317</td>\n","      <td>4.460347</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>13.250000</td>\n","      <td>12.000000</td>\n","      <td>13.000000</td>\n","      <td>12.875000</td>\n","      <td>6.750000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>50.50000</td>\n","      <td>170.430790</td>\n","      <td>448.385564</td>\n","      <td>100.233139</td>\n","      <td>40.146085</td>\n","      <td>12.495595</td>\n","      <td>41.795700</td>\n","      <td>8.345785</td>\n","      <td>4.173854</td>\n","      <td>170.210913</td>\n","      <td>449.212554</td>\n","      <td>100.097694</td>\n","      <td>40.072752</td>\n","      <td>14.853948</td>\n","      <td>49.613807</td>\n","      <td>9.921407</td>\n","      <td>4.958594</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>32.625000</td>\n","      <td>29.500000</td>\n","      <td>32.125000</td>\n","      <td>32.375000</td>\n","      <td>12.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>75.25000</td>\n","      <td>176.607051</td>\n","      <td>468.447680</td>\n","      <td>104.404520</td>\n","      <td>42.227392</td>\n","      <td>17.689118</td>\n","      <td>59.103715</td>\n","      <td>11.789645</td>\n","      <td>5.898997</td>\n","      <td>172.459612</td>\n","      <td>456.368008</td>\n","      <td>101.611121</td>\n","      <td>40.833364</td>\n","      <td>16.394764</td>\n","      <td>54.821318</td>\n","      <td>10.980498</td>\n","      <td>5.484480</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>68.500000</td>\n","      <td>66.250000</td>\n","      <td>67.000000</td>\n","      <td>70.125000</td>\n","      <td>16.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>100.00000</td>\n","      <td>241.420717</td>\n","      <td>586.682904</td>\n","      <td>162.309656</td>\n","      <td>69.311324</td>\n","      <td>58.444332</td>\n","      <td>179.903039</td>\n","      <td>35.659369</td>\n","      <td>18.305595</td>\n","      <td>220.782618</td>\n","      <td>499.096975</td>\n","      <td>152.310351</td>\n","      <td>61.932124</td>\n","      <td>27.664538</td>\n","      <td>103.819404</td>\n","      <td>28.654103</td>\n","      <td>12.325783</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>491.875000</td>\n","      <td>348.875000</td>\n","      <td>370.875000</td>\n","      <td>394.875000</td>\n","      <td>20.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          machineID    voltmean_3h  ...          comp4            age\n","count  291300.00000  291300.000000  ...  291300.000000  291300.000000\n","mean       50.50000     170.771416  ...      53.619118      11.330000\n","std        28.86612       9.497201  ...      59.606886       5.827625\n","min         1.00000     125.532506  ...       0.000000       0.000000\n","25%        25.75000     164.445576  ...      12.875000       6.750000\n","50%        50.50000     170.430790  ...      32.375000      12.000000\n","75%        75.25000     176.607051  ...      70.125000      16.000000\n","max       100.00000     241.420717  ...     394.875000      20.000000\n","\n","[8 rows x 27 columns]"]},"metadata":{"tags":[]},"execution_count":163}]},{"cell_type":"markdown","metadata":{"id":"TjhWPATwj4VD"},"source":["## Labeling Response Variable"]},{"cell_type":"markdown","metadata":{"id":"Nm2Z_XN6kwc-"},"source":["When using multi-class classification to predict failures due to a problem, the labeling is done by taking a window of time before an asset failure and labeling the feature records that fall into that window as \"about to fail due to a problem\" while labeling all other records as \"normal\". This time window should be selected according to the business rule: in some situations it may be sufficient to predict failures hours in advance, while in others days or weeks in advance may be needed."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"Gq7fc6OFk56l","executionInfo":{"status":"ok","timestamp":1615907236202,"user_tz":-60,"elapsed":450,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"66afcfd1-53c0-4419-c6fd-a0675f5bbfc2"},"source":["final_feat.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","      <th>error1count</th>\n","      <th>error2count</th>\n","      <th>error3count</th>\n","      <th>error4count</th>\n","      <th>error5count</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","      <th>model</th>\n","      <th>age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>180.133784</td>\n","      <td>440.608320</td>\n","      <td>94.137969</td>\n","      <td>41.551544</td>\n","      <td>21.322735</td>\n","      <td>48.770512</td>\n","      <td>2.135684</td>\n","      <td>10.037208</td>\n","      <td>169.733809</td>\n","      <td>445.179865</td>\n","      <td>96.797113</td>\n","      <td>40.385160</td>\n","      <td>11.233120</td>\n","      <td>48.717395</td>\n","      <td>10.079880</td>\n","      <td>5.853209</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.000</td>\n","      <td>215.000</td>\n","      <td>155.000</td>\n","      <td>170.000</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>176.364293</td>\n","      <td>439.349655</td>\n","      <td>101.553209</td>\n","      <td>36.105580</td>\n","      <td>18.952210</td>\n","      <td>51.329636</td>\n","      <td>13.789279</td>\n","      <td>6.737739</td>\n","      <td>170.614862</td>\n","      <td>446.364859</td>\n","      <td>96.849785</td>\n","      <td>39.736826</td>\n","      <td>12.519402</td>\n","      <td>48.385076</td>\n","      <td>10.171540</td>\n","      <td>6.163231</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.125</td>\n","      <td>215.125</td>\n","      <td>155.125</td>\n","      <td>170.125</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>160.384568</td>\n","      <td>424.385316</td>\n","      <td>99.598722</td>\n","      <td>36.094637</td>\n","      <td>13.047080</td>\n","      <td>13.702496</td>\n","      <td>9.988609</td>\n","      <td>1.639962</td>\n","      <td>169.893965</td>\n","      <td>447.009407</td>\n","      <td>97.715600</td>\n","      <td>39.498374</td>\n","      <td>13.370357</td>\n","      <td>42.432317</td>\n","      <td>9.471669</td>\n","      <td>6.195076</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.250</td>\n","      <td>215.250</td>\n","      <td>155.250</td>\n","      <td>170.250</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>170.472461</td>\n","      <td>442.933997</td>\n","      <td>102.380586</td>\n","      <td>40.483002</td>\n","      <td>16.642354</td>\n","      <td>56.290447</td>\n","      <td>3.305739</td>\n","      <td>8.854145</td>\n","      <td>171.243444</td>\n","      <td>444.233563</td>\n","      <td>96.666060</td>\n","      <td>40.229370</td>\n","      <td>13.299281</td>\n","      <td>41.346121</td>\n","      <td>8.731229</td>\n","      <td>5.687944</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.375</td>\n","      <td>215.375</td>\n","      <td>155.375</td>\n","      <td>170.375</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>163.263806</td>\n","      <td>468.937558</td>\n","      <td>102.726648</td>\n","      <td>40.921802</td>\n","      <td>17.424688</td>\n","      <td>38.680380</td>\n","      <td>9.105775</td>\n","      <td>3.060781</td>\n","      <td>170.792486</td>\n","      <td>448.440437</td>\n","      <td>95.766838</td>\n","      <td>40.055214</td>\n","      <td>13.954518</td>\n","      <td>43.490234</td>\n","      <td>8.061653</td>\n","      <td>5.898069</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.500</td>\n","      <td>215.500</td>\n","      <td>155.500</td>\n","      <td>170.500</td>\n","      <td>model3</td>\n","      <td>18</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID            datetime  voltmean_3h  ...    comp4   model  age\n","0          1 2015-01-02 06:00:00   180.133784  ...  170.000  model3   18\n","1          1 2015-01-02 09:00:00   176.364293  ...  170.125  model3   18\n","2          1 2015-01-02 12:00:00   160.384568  ...  170.250  model3   18\n","3          1 2015-01-02 15:00:00   170.472461  ...  170.375  model3   18\n","4          1 2015-01-02 18:00:00   163.263806  ...  170.500  model3   18\n","\n","[5 rows x 29 columns]"]},"metadata":{"tags":[]},"execution_count":164}]},{"cell_type":"markdown","metadata":{"id":"OA2BKJ18lEOi"},"source":["The objective is to calculate the probability that a machine will fail in the next 24 hours due to a given component failure.\r\n","\r\n","Next, a categorical failure feature is created to serve as a label. All records within a 24-hour window before a component failure of component 1 have failure = comp1, and so on for components 2, 3 and 4; All records not within 24 hours after a component failure have failure = none"]},{"cell_type":"markdown","metadata":{"id":"8-hgMe6klcZ-"},"source":["We are telling the model that any value similar to those within the 24-hour window is a failure of that component, which is why the machines repeat.\r\n","We use limit=7 because we have 3 hour separations; 8 * 3 = 24 hours but we take into account the first non-zero value, so it is 7."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"vBJe3pldlGQH","executionInfo":{"status":"ok","timestamp":1615907485469,"user_tz":-60,"elapsed":561,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"61228032-66d6-4708-bf08-7cb67ec28d34"},"source":["labeled_features = final_feat.merge(failures, on=[\"datetime\", \"machineID\"], how=\"left\")\r\n","# We apply a backward-fill of up to 24h.\r\n","# Pass to object or string, apply the operation, and return to category.\r\n","labeled_features[\"failure\"] = labeled_features[\"failure\"].astype(object).fillna(method=\"bfill\", limit=7)\r\n","labeled_features[\"failure\"] = labeled_features[\"failure\"].fillna(\"none\")\r\n","labeled_features[\"failure\"] = labeled_features[\"failure\"].astype(\"category\")\r\n","labeled_features.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","      <th>error1count</th>\n","      <th>error2count</th>\n","      <th>error3count</th>\n","      <th>error4count</th>\n","      <th>error5count</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","      <th>model</th>\n","      <th>age</th>\n","      <th>failure</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>180.133784</td>\n","      <td>440.608320</td>\n","      <td>94.137969</td>\n","      <td>41.551544</td>\n","      <td>21.322735</td>\n","      <td>48.770512</td>\n","      <td>2.135684</td>\n","      <td>10.037208</td>\n","      <td>169.733809</td>\n","      <td>445.179865</td>\n","      <td>96.797113</td>\n","      <td>40.385160</td>\n","      <td>11.233120</td>\n","      <td>48.717395</td>\n","      <td>10.079880</td>\n","      <td>5.853209</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.000</td>\n","      <td>215.000</td>\n","      <td>155.000</td>\n","      <td>170.000</td>\n","      <td>model3</td>\n","      <td>18</td>\n","      <td>none</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>176.364293</td>\n","      <td>439.349655</td>\n","      <td>101.553209</td>\n","      <td>36.105580</td>\n","      <td>18.952210</td>\n","      <td>51.329636</td>\n","      <td>13.789279</td>\n","      <td>6.737739</td>\n","      <td>170.614862</td>\n","      <td>446.364859</td>\n","      <td>96.849785</td>\n","      <td>39.736826</td>\n","      <td>12.519402</td>\n","      <td>48.385076</td>\n","      <td>10.171540</td>\n","      <td>6.163231</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.125</td>\n","      <td>215.125</td>\n","      <td>155.125</td>\n","      <td>170.125</td>\n","      <td>model3</td>\n","      <td>18</td>\n","      <td>none</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>160.384568</td>\n","      <td>424.385316</td>\n","      <td>99.598722</td>\n","      <td>36.094637</td>\n","      <td>13.047080</td>\n","      <td>13.702496</td>\n","      <td>9.988609</td>\n","      <td>1.639962</td>\n","      <td>169.893965</td>\n","      <td>447.009407</td>\n","      <td>97.715600</td>\n","      <td>39.498374</td>\n","      <td>13.370357</td>\n","      <td>42.432317</td>\n","      <td>9.471669</td>\n","      <td>6.195076</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.250</td>\n","      <td>215.250</td>\n","      <td>155.250</td>\n","      <td>170.250</td>\n","      <td>model3</td>\n","      <td>18</td>\n","      <td>none</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>170.472461</td>\n","      <td>442.933997</td>\n","      <td>102.380586</td>\n","      <td>40.483002</td>\n","      <td>16.642354</td>\n","      <td>56.290447</td>\n","      <td>3.305739</td>\n","      <td>8.854145</td>\n","      <td>171.243444</td>\n","      <td>444.233563</td>\n","      <td>96.666060</td>\n","      <td>40.229370</td>\n","      <td>13.299281</td>\n","      <td>41.346121</td>\n","      <td>8.731229</td>\n","      <td>5.687944</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.375</td>\n","      <td>215.375</td>\n","      <td>155.375</td>\n","      <td>170.375</td>\n","      <td>model3</td>\n","      <td>18</td>\n","      <td>none</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>163.263806</td>\n","      <td>468.937558</td>\n","      <td>102.726648</td>\n","      <td>40.921802</td>\n","      <td>17.424688</td>\n","      <td>38.680380</td>\n","      <td>9.105775</td>\n","      <td>3.060781</td>\n","      <td>170.792486</td>\n","      <td>448.440437</td>\n","      <td>95.766838</td>\n","      <td>40.055214</td>\n","      <td>13.954518</td>\n","      <td>43.490234</td>\n","      <td>8.061653</td>\n","      <td>5.898069</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.500</td>\n","      <td>215.500</td>\n","      <td>155.500</td>\n","      <td>170.500</td>\n","      <td>model3</td>\n","      <td>18</td>\n","      <td>none</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID            datetime  voltmean_3h  ...   model  age  failure\n","0          1 2015-01-02 06:00:00   180.133784  ...  model3   18     none\n","1          1 2015-01-02 09:00:00   176.364293  ...  model3   18     none\n","2          1 2015-01-02 12:00:00   160.384568  ...  model3   18     none\n","3          1 2015-01-02 15:00:00   170.472461  ...  model3   18     none\n","4          1 2015-01-02 18:00:00   163.263806  ...  model3   18     none\n","\n","[5 rows x 30 columns]"]},"metadata":{"tags":[]},"execution_count":172}]},{"cell_type":"code","metadata":{"id":"BxCWpehvnP44"},"source":["model_dummies = pd.get_dummies(labeled_features[\"model\"])\r\n","labeled_features = pd.concat([labeled_features, model_dummies], axis=1)\r\n","labeled_features.drop(\"model\", axis=1, inplace=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"qFFW555EnUPZ","executionInfo":{"status":"ok","timestamp":1615907868189,"user_tz":-60,"elapsed":766,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"fa92ae67-cce6-4339-a523-b69aa6cab430"},"source":["labeled_features.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>machineID</th>\n","      <th>datetime</th>\n","      <th>voltmean_3h</th>\n","      <th>rotatemean_3h</th>\n","      <th>pressuremean_3h</th>\n","      <th>vibrationmean_3h</th>\n","      <th>voltsd_3h</th>\n","      <th>rotatesd_3h</th>\n","      <th>pressuresd_3h</th>\n","      <th>vibrationsd_3h</th>\n","      <th>voltmean_24h</th>\n","      <th>rotatemean_24h</th>\n","      <th>pressuremean_24h</th>\n","      <th>vibrationmean_24h</th>\n","      <th>voltsd_24h</th>\n","      <th>rotatesd_24h</th>\n","      <th>pressuresd_24h</th>\n","      <th>vibrationsd_24h</th>\n","      <th>error1count</th>\n","      <th>error2count</th>\n","      <th>error3count</th>\n","      <th>error4count</th>\n","      <th>error5count</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","      <th>age</th>\n","      <th>failure</th>\n","      <th>model1</th>\n","      <th>model2</th>\n","      <th>model3</th>\n","      <th>model4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2015-01-02 06:00:00</td>\n","      <td>180.133784</td>\n","      <td>440.608320</td>\n","      <td>94.137969</td>\n","      <td>41.551544</td>\n","      <td>21.322735</td>\n","      <td>48.770512</td>\n","      <td>2.135684</td>\n","      <td>10.037208</td>\n","      <td>169.733809</td>\n","      <td>445.179865</td>\n","      <td>96.797113</td>\n","      <td>40.385160</td>\n","      <td>11.233120</td>\n","      <td>48.717395</td>\n","      <td>10.079880</td>\n","      <td>5.853209</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.000</td>\n","      <td>215.000</td>\n","      <td>155.000</td>\n","      <td>170.000</td>\n","      <td>18</td>\n","      <td>none</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2015-01-02 09:00:00</td>\n","      <td>176.364293</td>\n","      <td>439.349655</td>\n","      <td>101.553209</td>\n","      <td>36.105580</td>\n","      <td>18.952210</td>\n","      <td>51.329636</td>\n","      <td>13.789279</td>\n","      <td>6.737739</td>\n","      <td>170.614862</td>\n","      <td>446.364859</td>\n","      <td>96.849785</td>\n","      <td>39.736826</td>\n","      <td>12.519402</td>\n","      <td>48.385076</td>\n","      <td>10.171540</td>\n","      <td>6.163231</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.125</td>\n","      <td>215.125</td>\n","      <td>155.125</td>\n","      <td>170.125</td>\n","      <td>18</td>\n","      <td>none</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>2015-01-02 12:00:00</td>\n","      <td>160.384568</td>\n","      <td>424.385316</td>\n","      <td>99.598722</td>\n","      <td>36.094637</td>\n","      <td>13.047080</td>\n","      <td>13.702496</td>\n","      <td>9.988609</td>\n","      <td>1.639962</td>\n","      <td>169.893965</td>\n","      <td>447.009407</td>\n","      <td>97.715600</td>\n","      <td>39.498374</td>\n","      <td>13.370357</td>\n","      <td>42.432317</td>\n","      <td>9.471669</td>\n","      <td>6.195076</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.250</td>\n","      <td>215.250</td>\n","      <td>155.250</td>\n","      <td>170.250</td>\n","      <td>18</td>\n","      <td>none</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>2015-01-02 15:00:00</td>\n","      <td>170.472461</td>\n","      <td>442.933997</td>\n","      <td>102.380586</td>\n","      <td>40.483002</td>\n","      <td>16.642354</td>\n","      <td>56.290447</td>\n","      <td>3.305739</td>\n","      <td>8.854145</td>\n","      <td>171.243444</td>\n","      <td>444.233563</td>\n","      <td>96.666060</td>\n","      <td>40.229370</td>\n","      <td>13.299281</td>\n","      <td>41.346121</td>\n","      <td>8.731229</td>\n","      <td>5.687944</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.375</td>\n","      <td>215.375</td>\n","      <td>155.375</td>\n","      <td>170.375</td>\n","      <td>18</td>\n","      <td>none</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>2015-01-02 18:00:00</td>\n","      <td>163.263806</td>\n","      <td>468.937558</td>\n","      <td>102.726648</td>\n","      <td>40.921802</td>\n","      <td>17.424688</td>\n","      <td>38.680380</td>\n","      <td>9.105775</td>\n","      <td>3.060781</td>\n","      <td>170.792486</td>\n","      <td>448.440437</td>\n","      <td>95.766838</td>\n","      <td>40.055214</td>\n","      <td>13.954518</td>\n","      <td>43.490234</td>\n","      <td>8.061653</td>\n","      <td>5.898069</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>20.500</td>\n","      <td>215.500</td>\n","      <td>155.500</td>\n","      <td>170.500</td>\n","      <td>18</td>\n","      <td>none</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   machineID            datetime  voltmean_3h  ...  model2  model3  model4\n","0          1 2015-01-02 06:00:00   180.133784  ...       0       1       0\n","1          1 2015-01-02 09:00:00   176.364293  ...       0       1       0\n","2          1 2015-01-02 12:00:00   160.384568  ...       0       1       0\n","3          1 2015-01-02 15:00:00   170.472461  ...       0       1       0\n","4          1 2015-01-02 18:00:00   163.263806  ...       0       1       0\n","\n","[5 rows x 33 columns]"]},"metadata":{"tags":[]},"execution_count":174}]},{"cell_type":"markdown","metadata":{"id":"LUFTcVy1nW4p"},"source":["## Correlation Analysis"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":585},"id":"UeJbIqGXnbTV","executionInfo":{"status":"ok","timestamp":1615907940356,"user_tz":-60,"elapsed":2147,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"a48f626a-aeef-4904-ec0f-cf5c22a14225"},"source":["# It is necessary to eliminate variables with high correlation (only one), consider > 70%.\r\n","f, ax = plt.subplots(figsize=(10, 8))\r\n","corr = labeled_features.corr()\r\n","sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\r\n","            square=True, ax=ax)\r\n","plt.title(\"Correlation Between Variables\")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"9Kam9_wKocKe"},"source":["# Modeling - Training, Validation and Testing"]},{"cell_type":"markdown","metadata":{"id":"DiP8mAvRo4mD"},"source":["When working with time series as in this example, record partitioning in training, validation and testing must be done carefully to avoid overestimating model performance. In predictive maintenance, features are generally generated using lagged aggregates: records in the same time window are likely to have identical labels and similar feature values. These correlations can give a model an \"unfair advantage\" by predicting a record belonging to the test set that shares its time window with a record from the training set. Therefore, we divide the records in training, validation, and test sets into large portions, to minimize the number of time intervals shared between them.\r\n","\r\n","Predictive models do not have advanced knowledge of future timing trends: in practice, such trends are likely to exist and have an adverse impact on model performance. To obtain an accurate assessment of the performance of a predictive model, it is recommended to perform training on older records and validation/testing using the newer records.\r\n","\r\n","For both reasons, a time-dependent log splitting strategy is an excellent choice for predictive maintenance models. The splitting is performed by choosing a point in time according to the desired size of the training and test sets: all records prior to the time point are used to train the model, and all remaining records are used for testing."]},{"cell_type":"code","metadata":{"id":"wpLGdOkOpC9z"},"source":["# Establish the times corresponding to the records to be used for training and testing.\r\n","threshold_dates = [\r\n","    pd.to_datetime(\"2015-09-30 01:00:00\"), pd.to_datetime(\"2015-10-01 01:00:00\")\r\n","]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"F2Ny3EgOpPK3"},"source":["test_results = []\r\n","test_results_tree = []\r\n","test_results_randomForrest = []\r\n","models = []\r\n","models_tree = []\r\n","models_randomForrest = []\r\n","total = len(threshold_dates)\r\n","\r\n","# Partition of separate dates.\r\n","last_train_date = threshold_dates[0]\r\n","first_test_date = threshold_dates[1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X3c3yeOqpYkZ","executionInfo":{"status":"ok","timestamp":1615913903558,"user_tz":-60,"elapsed":369,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"dbca2f34-053b-4422-959c-a43840bfeb55"},"source":["# Typically 20-30% of the data is used.\r\n","ntraining = labeled_features.loc[labeled_features[\"datetime\"] < last_train_date]\r\n","ntesting = labeled_features.loc[labeled_features[\"datetime\"] > first_test_date]\r\n","print(f\"{ntraining.shape[0]} training records.\")\r\n","print(f\"{ntesting.shape[0]} records for testing.\")\r\n","print(f\"{ntesting.shape[0] / ntraining.shape[0] * 100:0.1f}% of the data will be used for testing.\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["216732 training records.\n","73809 records for testing.\n","34.1% of the data will be used for testing.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wpiNeWpEqHHf","executionInfo":{"status":"ok","timestamp":1615913905566,"user_tz":-60,"elapsed":360,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"36879000-5ebf-4539-df5c-001112c8deb1"},"source":["fails_train = ntraining[ntraining[\"failure\"] != \"none\"].shape[0]\r\n","no_fails_train = ntraining[ntraining[\"failure\"] == \"none\"].shape[0]\r\n","fails_test = ntesting[ntesting[\"failure\"] != \"none\"].shape[0]\r\n","no_fails_test = ntesting[ntesting[\"failure\"] == \"none\"].shape[0]\r\n","\r\n","print(f\"{fails_train / no_fails_train * 100:0.1f}% of the cases are training set failures.\")\r\n","print(f\"{fails_test / no_fails_test * 100:0.1f}% of the cases are test set failures.\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["2.0% of the cases are training set failures.\n","1.9% of the cases are test set failures.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"d_MoKhfmrAZv"},"source":["# Assign the corresponding values to training and tests.\r\n","train_y = labeled_features.loc[labeled_features[\"datetime\"] < last_train_date, \"failure\"]\r\n","train_X = labeled_features.loc[labeled_features[\"datetime\"] < last_train_date].drop([\"datetime\",\r\n","                                                                                    \"machineID\",\r\n","                                                                                    \"failure\"], axis=1)\r\n","test_y = labeled_features.loc[labeled_features[\"datetime\"] > first_test_date, \"failure\"]\r\n","test_X = labeled_features.loc[labeled_features[\"datetime\"] > first_test_date].drop([\"datetime\",\r\n","                                                                                   \"machineID\",\r\n","                                                                                   \"failure\"], axis=1)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"DeB534WwrYMm"},"source":["## Model Training - Gradient Boosting"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Im0fQhrTrJQ1","executionInfo":{"status":"ok","timestamp":1615912326255,"user_tz":-60,"elapsed":124905,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"330d6f61-221a-457b-abf1-c893598eef98"},"source":["# Model training.\r\n","# model = GradientBoostingClassifier(random_state=42)\r\n","model = xgb(n_jobs=-1)\r\n","model.fit(train_X, train_y)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n","              colsample_bynode=1, colsample_bytree=1, gamma=0,\n","              learning_rate=0.1, max_delta_step=0, max_depth=3,\n","              min_child_weight=1, missing=None, n_estimators=100, n_jobs=-1,\n","              nthread=None, objective='multi:softprob', random_state=0,\n","              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n","              silent=None, subsample=1, verbosity=1)"]},"metadata":{"tags":[]},"execution_count":213}]},{"cell_type":"code","metadata":{"id":"nRzkFTqEr8iZ"},"source":["# Obtain results on the test set.\r\n","test_result = pd.DataFrame(labeled_features.loc[labeled_features[\"datetime\"] > first_test_date])\r\n","test_result[\"predicted_failure\"] = model.predict(test_X)\r\n","test_results.append(test_result)\r\n","models.append(model)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"id":"CnD7hX-bsm1-","executionInfo":{"status":"ok","timestamp":1615912344933,"user_tz":-60,"elapsed":1023,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"0e14b226-26eb-4688-9f0a-c55ce4904ecc"},"source":["# Below, we plot the feature importances in the (first) trained model\r\n","plt.figure(figsize=(10, 10))\r\n","labels, importances = zip(*sorted(zip(test_X.columns, models[0].feature_importances_), reverse=False, key=lambda x: x[1]))\r\n","plt.yticks(range(len(labels)), labels)\r\n","_, labels = plt.xticks()\r\n","plt.setp(labels, rotation=0)\r\n","plt.barh(range(len(importances)), importances)\r\n","plt.ylabel(\"Features\")\r\n","plt.xlabel(\"Relevance [%]\")\r\n","plt.title(\"Relevance of Featrues\")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"W8-MGWvDuxAD"},"source":["### Evaluation - Gradient Boosting"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"id":"o7d7MAZWurr3","executionInfo":{"status":"ok","timestamp":1615909966797,"user_tz":-60,"elapsed":666,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"c2e97ef9-679f-4bd1-d8ac-0d73f7a5d348"},"source":["plt.figure(figsize=(15, 5))\r\n","labeled_features[\"failure\"].value_counts().plot(kind=\"bar\", rot=0, color = [\"blue\", \"orange\", \"green\", \"red\", \"black\"])\r\n","plt.title(\"Distribution of Failure Causes\")\r\n","plt.xlabel(\"Component\")\r\n","plt.ylabel(\"Quantity\")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"hv6kauYHu6wQ"},"source":["There is an expected imbalance."]},{"cell_type":"code","metadata":{"id":"k5ewzLR-vpoc"},"source":["def Evaluate(predicted, actual, labels):\r\n","    output_labels = []\r\n","    output = []\r\n","    \r\n","    # Calculate and display confusion matrix\r\n","    cm = confusion_matrix(actual, predicted, labels=labels)\r\n","    #print(\"Confusion matrix\\n- x-axis is true labels (none, comp1, etc.)\\n- y-axis is predicted labels\")\r\n","    #print(cm)\r\n","    \r\n","    # Calculate precision, recall, and F1 score\r\n","    accuracy = np.array([float(np.trace(cm)) / np.sum(cm)] * len(labels))\r\n","    precision = precision_score(actual, predicted, average=None, labels=labels)\r\n","    recall = recall_score(actual, predicted, average=None, labels=labels)\r\n","    f1 = 2 * precision * recall / (precision + recall)\r\n","    output.extend([accuracy.tolist(), precision.tolist(), recall.tolist(), f1.tolist()])\r\n","    output_labels.extend([\"accuracy\", \"precision\", \"recall\", \"F1\"])\r\n","    \r\n","    # Calculate the macro versions of these metrics\r\n","    output.extend([[np.mean(precision)] * len(labels),\r\n","                   [np.mean(recall)] * len(labels),\r\n","                   [np.mean(f1)] * len(labels)])\r\n","    output_labels.extend([\"macro precision\", \"macro recall\", \"macro F1\"])\r\n","    \r\n","    # Find the one-vs.-all confusion matrix\r\n","    cm_row_sums = cm.sum(axis = 1)\r\n","    cm_col_sums = cm.sum(axis = 0)\r\n","    s = np.zeros((2, 2))\r\n","    for i in range(len(labels)):\r\n","        v = np.array([[cm[i, i],\r\n","                       cm_row_sums[i] - cm[i, i]],\r\n","                      [cm_col_sums[i] - cm[i, i],\r\n","                       np.sum(cm) + cm[i, i] - (cm_row_sums[i] + cm_col_sums[i])]])\r\n","        s += v\r\n","    s_row_sums = s.sum(axis = 1)\r\n","    \r\n","    # Add average accuracy and micro-averaged  precision/recall/F1\r\n","    avg_accuracy = [np.trace(s) / np.sum(s)] * len(labels)\r\n","    micro_prf = [float(s[0,0]) / s_row_sums[0]] * len(labels)\r\n","    output.extend([avg_accuracy, micro_prf])\r\n","    output_labels.extend([\"average accuracy\",\r\n","                          \"micro-averaged precision/recall/F1\"])\r\n","    \r\n","    # Compute metrics for the majority classifier\r\n","    mc_index = np.where(cm_row_sums == np.max(cm_row_sums))[0][0]\r\n","    cm_row_dist = cm_row_sums / float(np.sum(cm))\r\n","    mc_accuracy = 0 * cm_row_dist; mc_accuracy[mc_index] = cm_row_dist[mc_index]\r\n","    mc_recall = 0 * cm_row_dist; mc_recall[mc_index] = 1\r\n","    mc_precision = 0 * cm_row_dist\r\n","    mc_precision[mc_index] = cm_row_dist[mc_index]\r\n","    mc_F1 = 0 * cm_row_dist;\r\n","    mc_F1[mc_index] = 2 * mc_precision[mc_index] / (mc_precision[mc_index] + 1)\r\n","    output.extend([mc_accuracy.tolist(), mc_recall.tolist(),\r\n","                   mc_precision.tolist(), mc_F1.tolist()])\r\n","    output_labels.extend([\"majority class accuracy\", \"majority class recall\",\r\n","                          \"majority class precision\", \"majority class F1\"])\r\n","        \r\n","    # Random accuracy and kappa\r\n","    cm_col_dist = cm_col_sums / float(np.sum(cm))\r\n","    exp_accuracy = np.array([np.sum(cm_row_dist * cm_col_dist)] * len(labels))\r\n","    kappa = (accuracy - exp_accuracy) / (1 - exp_accuracy)\r\n","    output.extend([exp_accuracy.tolist(), kappa.tolist()])\r\n","    output_labels.extend([\"expected accuracy\", \"kappa\"])\r\n","    \r\n","\r\n","    # Random guess\r\n","    rg_accuracy = np.ones(len(labels)) / float(len(labels))\r\n","    rg_precision = cm_row_dist\r\n","    rg_recall = np.ones(len(labels)) / float(len(labels))\r\n","    rg_F1 = 2 * cm_row_dist / (len(labels) * cm_row_dist + 1)\r\n","    output.extend([rg_accuracy.tolist(), rg_precision.tolist(),\r\n","                   rg_recall.tolist(), rg_F1.tolist()])\r\n","    output_labels.extend([\"random guess accuracy\", \"random guess precision\",\r\n","                          \"random guess recall\", \"random guess F1\"])\r\n","    \r\n","    # Random weighted guess\r\n","    rwg_accuracy = np.ones(len(labels)) * sum(cm_row_dist**2)\r\n","    rwg_precision = cm_row_dist\r\n","    rwg_recall = cm_row_dist\r\n","    rwg_F1 = cm_row_dist\r\n","    output.extend([rwg_accuracy.tolist(), rwg_precision.tolist(),\r\n","                   rwg_recall.tolist(), rwg_F1.tolist()])\r\n","    output_labels.extend([\"random weighted guess accuracy\",\r\n","                          \"random weighted guess precision\",\r\n","                          \"random weighted guess recall\",\r\n","                          \"random weighted guess F1\"])\r\n","\r\n","    output_df = pd.DataFrame(output, columns=labels)\r\n","    output_df.index = output_labels\r\n","                  \r\n","    return output_df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"UjkA7Q_Wv6hO","executionInfo":{"status":"ok","timestamp":1615910346950,"user_tz":-60,"elapsed":3024,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"56ccffd3-a35a-420d-8691-493f82883552"},"source":["evaluation_results = []\r\n","test_result = test_results[0]\r\n","evaluation_result = Evaluate(actual = test_result[\"failure\"],\r\n","                             predicted = test_result[\"predicted_failure\"],\r\n","                             labels = [\"none\", \"comp1\", \"comp2\", \"comp3\", \"comp4\"])\r\n","skplt.metrics.plot_confusion_matrix(\r\n","    test_result[\"failure\"],\r\n","    test_result[\"predicted_failure\"],\r\n","    normalize=False,\r\n","    title=\"Confusion Matrix\"\r\n",")\r\n","plt.ylabel('True classes', fontsize=14)\r\n","plt.xlabel('Predicted classes', fontsize=14)\r\n","plt.xticks(fontsize=12)\r\n","plt.yticks(fontsize=12)\r\n","plt.title(\"Confusion Matrix\", fontsize=15)\r\n","\r\n","skplt.metrics.plot_confusion_matrix(\r\n","    test_result[\"failure\"],\r\n","    test_result[\"predicted_failure\"],\r\n","    normalize=True,\r\n",")\r\n","plt.ylabel('True classes', fontsize=14)\r\n","plt.xlabel('Predicted classes', fontsize=14)\r\n","plt.xticks(fontsize=12)\r\n","plt.yticks(fontsize=12)\r\n","plt.title(\"Confusion Matrix (normalized)\", fontsize=15)\r\n","\r\n","plt.show()\r\n","\r\n","evaluation_results.append(evaluation_result)\r\n","evaluation_results[0]  # show full results for first split only"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>none</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","    </tr>\n","    <tr>\n","      <th>precision</th>\n","      <td>0.999586</td>\n","      <td>0.967846</td>\n","      <td>0.994614</td>\n","      <td>0.981395</td>\n","      <td>0.978648</td>\n","    </tr>\n","    <tr>\n","      <th>recall</th>\n","      <td>0.999807</td>\n","      <td>0.940625</td>\n","      <td>0.996403</td>\n","      <td>0.941964</td>\n","      <td>0.982143</td>\n","    </tr>\n","    <tr>\n","      <th>F1</th>\n","      <td>0.999696</td>\n","      <td>0.954041</td>\n","      <td>0.995508</td>\n","      <td>0.961276</td>\n","      <td>0.980392</td>\n","    </tr>\n","    <tr>\n","      <th>macro precision</th>\n","      <td>0.984418</td>\n","      <td>0.984418</td>\n","      <td>0.984418</td>\n","      <td>0.984418</td>\n","      <td>0.984418</td>\n","    </tr>\n","    <tr>\n","      <th>macro recall</th>\n","      <td>0.972188</td>\n","      <td>0.972188</td>\n","      <td>0.972188</td>\n","      <td>0.972188</td>\n","      <td>0.972188</td>\n","    </tr>\n","    <tr>\n","      <th>macro F1</th>\n","      <td>0.978183</td>\n","      <td>0.978183</td>\n","      <td>0.978183</td>\n","      <td>0.978183</td>\n","      <td>0.978183</td>\n","    </tr>\n","    <tr>\n","      <th>average accuracy</th>\n","      <td>0.999713</td>\n","      <td>0.999713</td>\n","      <td>0.999713</td>\n","      <td>0.999713</td>\n","      <td>0.999713</td>\n","    </tr>\n","    <tr>\n","      <th>micro-averaged precision/recall/F1</th>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","    </tr>\n","    <tr>\n","      <th>majority class accuracy</th>\n","      <td>0.981303</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class recall</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class precision</th>\n","      <td>0.981303</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class F1</th>\n","      <td>0.990563</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>expected accuracy</th>\n","      <td>0.963267</td>\n","      <td>0.963267</td>\n","      <td>0.963267</td>\n","      <td>0.963267</td>\n","      <td>0.963267</td>\n","    </tr>\n","    <tr>\n","      <th>kappa</th>\n","      <td>0.980452</td>\n","      <td>0.980452</td>\n","      <td>0.980452</td>\n","      <td>0.980452</td>\n","      <td>0.980452</td>\n","    </tr>\n","    <tr>\n","      <th>random guess accuracy</th>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>random guess precision</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random guess recall</th>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>random guess F1</th>\n","      <td>0.332278</td>\n","      <td>0.008487</td>\n","      <td>0.014519</td>\n","      <td>0.005979</td>\n","      <td>0.007446</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess accuracy</th>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess precision</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess recall</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess F1</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                        none     comp1  ...     comp3     comp4\n","accuracy                            0.999282  0.999282  ...  0.999282  0.999282\n","precision                           0.999586  0.967846  ...  0.981395  0.978648\n","recall                              0.999807  0.940625  ...  0.941964  0.982143\n","F1                                  0.999696  0.954041  ...  0.961276  0.980392\n","macro precision                     0.984418  0.984418  ...  0.984418  0.984418\n","macro recall                        0.972188  0.972188  ...  0.972188  0.972188\n","macro F1                            0.978183  0.978183  ...  0.978183  0.978183\n","average accuracy                    0.999713  0.999713  ...  0.999713  0.999713\n","micro-averaged precision/recall/F1  0.999282  0.999282  ...  0.999282  0.999282\n","majority class accuracy             0.981303  0.000000  ...  0.000000  0.000000\n","majority class recall               1.000000  0.000000  ...  0.000000  0.000000\n","majority class precision            0.981303  0.000000  ...  0.000000  0.000000\n","majority class F1                   0.990563  0.000000  ...  0.000000  0.000000\n","expected accuracy                   0.963267  0.963267  ...  0.963267  0.963267\n","kappa                               0.980452  0.980452  ...  0.980452  0.980452\n","random guess accuracy               0.200000  0.200000  ...  0.200000  0.200000\n","random guess precision              0.981303  0.004336  ...  0.003035  0.003794\n","random guess recall                 0.200000  0.200000  ...  0.200000  0.200000\n","random guess F1                     0.332278  0.008487  ...  0.005979  0.007446\n","random weighted guess accuracy      0.963055  0.963055  ...  0.963055  0.963055\n","random weighted guess precision     0.981303  0.004336  ...  0.003035  0.003794\n","random weighted guess recall        0.981303  0.004336  ...  0.003035  0.003794\n","random weighted guess F1            0.981303  0.004336  ...  0.003035  0.003794\n","\n","[23 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":191}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XnG50SksxnUy","executionInfo":{"status":"ok","timestamp":1615910567936,"user_tz":-60,"elapsed":416,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1a1bd0f2-22d9-4639-fa28-13b8b3907e98"},"source":["evaluation_results[0].mean(axis=1)[0:4]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["accuracy     0.999282\n","precision    0.984418\n","recall       0.972188\n","F1           0.978183\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":192}]},{"cell_type":"markdown","metadata":{"id":"cGzk-dlzx4hE"},"source":["We are interested in how many errors were found (recall)."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"id":"y5eP4KRKyBiW","executionInfo":{"status":"ok","timestamp":1615910761683,"user_tz":-60,"elapsed":2219,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"5c5b6448-344f-42cf-8761-bab6a2ef78f6"},"source":["# For a binary classification problem we usually use the ROC-AUC curve.\r\n","# For this multi-class case we will use precision vs. sensitivity.\r\n","skplt.metrics.plot_precision_recall(\r\n","    test_y,\r\n","    model.predict_proba(test_X),\r\n","    title=\"ROC\",\r\n","    figsize=(10,10)\r\n",")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fuu9jlCuymy8","executionInfo":{"status":"ok","timestamp":1615910827984,"user_tz":-60,"elapsed":425,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"09032111-d871-4c1e-b269-a24a34ed590f"},"source":["evaluation_results[0].loc[\"recall\"].values"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.99980671, 0.940625  , 0.99640288, 0.94196429, 0.98214286])"]},"metadata":{"tags":[]},"execution_count":195}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"_qv5Lv_KywW_","executionInfo":{"status":"ok","timestamp":1615910888651,"user_tz":-60,"elapsed":373,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"0f3d5cba-81a1-4bee-a0eb-4bd53017d054"},"source":["recall_df = pd.DataFrame([evaluation_results[0].loc[\"recall\"].values],\r\n","                         columns=[\"none\", \"comp1\", \"comp2\", \"comp3\", \"comp4\"],\r\n","                         index=[\"Sensitivity per Component\"])\r\n","recall_df.T"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Sensitivity per Component</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>none</th>\n","      <td>0.999807</td>\n","    </tr>\n","    <tr>\n","      <th>comp1</th>\n","      <td>0.940625</td>\n","    </tr>\n","    <tr>\n","      <th>comp2</th>\n","      <td>0.996403</td>\n","    </tr>\n","    <tr>\n","      <th>comp3</th>\n","      <td>0.941964</td>\n","    </tr>\n","    <tr>\n","      <th>comp4</th>\n","      <td>0.982143</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Sensitivity per Component\n","none                    0.999807\n","comp1                   0.940625\n","comp2                   0.996403\n","comp3                   0.941964\n","comp4                   0.982143"]},"metadata":{"tags":[]},"execution_count":197}]},{"cell_type":"markdown","metadata":{"id":"6wfforlW0gc8"},"source":["## Model Training - Tree"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_xJLHGoc0sPb","executionInfo":{"status":"ok","timestamp":1615913933523,"user_tz":-60,"elapsed":4197,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"3a917ba4-9d64-4b25-c797-f5b1915295fb"},"source":["model_tree = tree.DecisionTreeClassifier()\r\n","model_tree = model_tree.fit(train_X,train_y)\r\n","model_tree"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n","                       max_depth=None, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=1, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=None, splitter='best')"]},"metadata":{"tags":[]},"execution_count":241}]},{"cell_type":"code","metadata":{"id":"cE2ZPuWy2O5U"},"source":["# Obtain results on the test set.\r\n","test_result_tree = pd.DataFrame(labeled_features.loc[labeled_features[\"datetime\"] > first_test_date])\r\n","test_result_tree[\"predicted_failure\"] = model_tree.predict(test_X)\r\n","test_results_tree.append(test_result_tree)\r\n","models_tree.append(model_tree)\r\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"id":"ol7Xz7Hg24db","executionInfo":{"status":"ok","timestamp":1615913941717,"user_tz":-60,"elapsed":967,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"02dfd9ef-cf41-4249-d47b-8025e487ded0"},"source":["# Below, we plot the feature importances in the (tree) trained model\r\n","plt.figure(figsize=(10, 10))\r\n","labels, importances = zip(*sorted(zip(test_X.columns, models_tree[0].feature_importances_), reverse=False, key=lambda x: x[1]))\r\n","plt.yticks(range(len(labels)), labels)\r\n","_, labels = plt.xticks()\r\n","plt.setp(labels, rotation=0)\r\n","plt.barh(range(len(importances)), importances)\r\n","plt.ylabel(\"Features\")\r\n","plt.xlabel(\"Relevance [%]\")\r\n","plt.title(\"Relevance of Featrues\")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"3CE7U_7g4kkP"},"source":["tree.plot_tree(model_tree)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vArpX-G9C89G"},"source":["### Evaluation - Tree"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"OImNUQED8rP5","executionInfo":{"status":"ok","timestamp":1615914027581,"user_tz":-60,"elapsed":3238,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"0cb5d521-7cfd-40e4-85e5-05d8452ec0fb"},"source":["evaluation_results_tree = []\r\n","test_result = test_results_tree[0]\r\n","evaluation_result_tree = Evaluate(actual = test_result[\"failure\"],\r\n","                             predicted = test_result[\"predicted_failure\"],\r\n","                             labels = [\"none\", \"comp1\", \"comp2\", \"comp3\", \"comp4\"])\r\n","skplt.metrics.plot_confusion_matrix(\r\n","    test_result_tree[\"failure\"],\r\n","    test_result_tree[\"predicted_failure\"],\r\n","    normalize=False,\r\n","    title=\"Confusion Matrix\"\r\n",")\r\n","plt.ylabel('True classes', fontsize=14)\r\n","plt.xlabel('Predicted classes', fontsize=14)\r\n","plt.xticks(fontsize=12)\r\n","plt.yticks(fontsize=12)\r\n","plt.title(\"Confusion Matrix\", fontsize=15)\r\n","\r\n","skplt.metrics.plot_confusion_matrix(\r\n","    test_result_tree[\"failure\"],\r\n","    test_result_tree[\"predicted_failure\"],\r\n","    normalize=True,\r\n",")\r\n","plt.ylabel('True classes', fontsize=14)\r\n","plt.xlabel('Predicted classes', fontsize=14)\r\n","plt.xticks(fontsize=12)\r\n","plt.yticks(fontsize=12)\r\n","plt.title(\"Confusion Matrix (normalized)\", fontsize=15)\r\n","\r\n","plt.show()\r\n","\r\n","evaluation_results_tree.append(evaluation_result_tree)\r\n","evaluation_results_tree[0]  # show full results for first split only"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>none</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.998781</td>\n","      <td>0.998781</td>\n","      <td>0.998781</td>\n","      <td>0.998781</td>\n","      <td>0.998781</td>\n","    </tr>\n","    <tr>\n","      <th>precision</th>\n","      <td>0.999338</td>\n","      <td>0.922601</td>\n","      <td>0.994614</td>\n","      <td>0.975000</td>\n","      <td>0.965649</td>\n","    </tr>\n","    <tr>\n","      <th>recall</th>\n","      <td>0.999862</td>\n","      <td>0.931250</td>\n","      <td>0.996403</td>\n","      <td>0.870536</td>\n","      <td>0.903571</td>\n","    </tr>\n","    <tr>\n","      <th>F1</th>\n","      <td>0.999600</td>\n","      <td>0.926905</td>\n","      <td>0.995508</td>\n","      <td>0.919811</td>\n","      <td>0.933579</td>\n","    </tr>\n","    <tr>\n","      <th>macro precision</th>\n","      <td>0.971440</td>\n","      <td>0.971440</td>\n","      <td>0.971440</td>\n","      <td>0.971440</td>\n","      <td>0.971440</td>\n","    </tr>\n","    <tr>\n","      <th>macro recall</th>\n","      <td>0.940324</td>\n","      <td>0.940324</td>\n","      <td>0.940324</td>\n","      <td>0.940324</td>\n","      <td>0.940324</td>\n","    </tr>\n","    <tr>\n","      <th>macro F1</th>\n","      <td>0.955081</td>\n","      <td>0.955081</td>\n","      <td>0.955081</td>\n","      <td>0.955081</td>\n","      <td>0.955081</td>\n","    </tr>\n","    <tr>\n","      <th>average accuracy</th>\n","      <td>0.999512</td>\n","      <td>0.999512</td>\n","      <td>0.999512</td>\n","      <td>0.999512</td>\n","      <td>0.999512</td>\n","    </tr>\n","    <tr>\n","      <th>micro-averaged precision/recall/F1</th>\n","      <td>0.998781</td>\n","      <td>0.998781</td>\n","      <td>0.998781</td>\n","      <td>0.998781</td>\n","      <td>0.998781</td>\n","    </tr>\n","    <tr>\n","      <th>majority class accuracy</th>\n","      <td>0.981303</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class recall</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class precision</th>\n","      <td>0.981303</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class F1</th>\n","      <td>0.990563</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>expected accuracy</th>\n","      <td>0.963558</td>\n","      <td>0.963558</td>\n","      <td>0.963558</td>\n","      <td>0.963558</td>\n","      <td>0.963558</td>\n","    </tr>\n","    <tr>\n","      <th>kappa</th>\n","      <td>0.966539</td>\n","      <td>0.966539</td>\n","      <td>0.966539</td>\n","      <td>0.966539</td>\n","      <td>0.966539</td>\n","    </tr>\n","    <tr>\n","      <th>random guess accuracy</th>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>random guess precision</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random guess recall</th>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>random guess F1</th>\n","      <td>0.332278</td>\n","      <td>0.008487</td>\n","      <td>0.014519</td>\n","      <td>0.005979</td>\n","      <td>0.007446</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess accuracy</th>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess precision</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess recall</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess F1</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                        none     comp1  ...     comp3     comp4\n","accuracy                            0.998781  0.998781  ...  0.998781  0.998781\n","precision                           0.999338  0.922601  ...  0.975000  0.965649\n","recall                              0.999862  0.931250  ...  0.870536  0.903571\n","F1                                  0.999600  0.926905  ...  0.919811  0.933579\n","macro precision                     0.971440  0.971440  ...  0.971440  0.971440\n","macro recall                        0.940324  0.940324  ...  0.940324  0.940324\n","macro F1                            0.955081  0.955081  ...  0.955081  0.955081\n","average accuracy                    0.999512  0.999512  ...  0.999512  0.999512\n","micro-averaged precision/recall/F1  0.998781  0.998781  ...  0.998781  0.998781\n","majority class accuracy             0.981303  0.000000  ...  0.000000  0.000000\n","majority class recall               1.000000  0.000000  ...  0.000000  0.000000\n","majority class precision            0.981303  0.000000  ...  0.000000  0.000000\n","majority class F1                   0.990563  0.000000  ...  0.000000  0.000000\n","expected accuracy                   0.963558  0.963558  ...  0.963558  0.963558\n","kappa                               0.966539  0.966539  ...  0.966539  0.966539\n","random guess accuracy               0.200000  0.200000  ...  0.200000  0.200000\n","random guess precision              0.981303  0.004336  ...  0.003035  0.003794\n","random guess recall                 0.200000  0.200000  ...  0.200000  0.200000\n","random guess F1                     0.332278  0.008487  ...  0.005979  0.007446\n","random weighted guess accuracy      0.963055  0.963055  ...  0.963055  0.963055\n","random weighted guess precision     0.981303  0.004336  ...  0.003035  0.003794\n","random weighted guess recall        0.981303  0.004336  ...  0.003035  0.003794\n","random weighted guess F1            0.981303  0.004336  ...  0.003035  0.003794\n","\n","[23 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":245}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kYFiyjQBGYPa","executionInfo":{"status":"ok","timestamp":1615916018434,"user_tz":-60,"elapsed":380,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"f1d51544-ed68-4831-d876-5933f4aa3f9b"},"source":["evaluation_results_tree[0].mean(axis=1)[0:4]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["accuracy     0.998781\n","precision    0.971440\n","recall       0.940324\n","F1           0.955081\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":252}]},{"cell_type":"markdown","metadata":{"id":"8QLsk_-P0mMq"},"source":["## Model Training - Randome Forrest"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A9z_NyPSCHdY","executionInfo":{"status":"ok","timestamp":1615915092806,"user_tz":-60,"elapsed":92769,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"c747e8d8-cabe-4175-d233-33e775fc9a93"},"source":["model_randomForrest = RandomForestClassifier()\r\n","model_randomForrest = model_randomForrest.fit(train_X,train_y)\r\n","model_randomForrest"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n","                       criterion='gini', max_depth=None, max_features='auto',\n","                       max_leaf_nodes=None, max_samples=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=1, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, n_estimators=100,\n","                       n_jobs=None, oob_score=False, random_state=None,\n","                       verbose=0, warm_start=False)"]},"metadata":{"tags":[]},"execution_count":248}]},{"cell_type":"code","metadata":{"id":"CWLmwqXJEib5"},"source":["# Obtain results on the test set.\r\n","test_result_randomForrest = pd.DataFrame(labeled_features.loc[labeled_features[\"datetime\"] > first_test_date])\r\n","test_result_randomForrest[\"predicted_failure\"] = model_randomForrest.predict(test_X)\r\n","test_results_randomForrest.append(test_result_randomForrest)\r\n","models_randomForrest.append(model_randomForrest)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"id":"uKFwMlLCEzCd","executionInfo":{"status":"ok","timestamp":1615915623869,"user_tz":-60,"elapsed":914,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"1ed1b8c6-2a22-4018-8c2a-6597326c7578"},"source":["# Below, we plot the feature importances in the (randomForrest) trained model\r\n","plt.figure(figsize=(10, 10))\r\n","labels, importances = zip(*sorted(zip(test_X.columns, models_randomForrest[0].feature_importances_), reverse=False, key=lambda x: x[1]))\r\n","plt.yticks(range(len(labels)), labels)\r\n","_, labels = plt.xticks()\r\n","plt.setp(labels, rotation=0)\r\n","plt.barh(range(len(importances)), importances)\r\n","plt.ylabel(\"Features\")\r\n","plt.xlabel(\"Relevance [%]\")\r\n","plt.title(\"Relevance of Featrues\")\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"l_kZywbgF3VD"},"source":["### Evaluation Random Forrest"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"U0wzpKRXFXUp","executionInfo":{"status":"ok","timestamp":1615915832954,"user_tz":-60,"elapsed":3133,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"20a668f0-e643-4255-a452-5726906973b5"},"source":["evaluation_results_randomForrest = []\r\n","test_result = test_results_randomForrest[0]\r\n","evaluation_result_randomForrest = Evaluate(actual = test_result[\"failure\"],\r\n","                             predicted = test_result[\"predicted_failure\"],\r\n","                             labels = [\"none\", \"comp1\", \"comp2\", \"comp3\", \"comp4\"])\r\n","skplt.metrics.plot_confusion_matrix(\r\n","    test_result_randomForrest[\"failure\"],\r\n","    test_result_randomForrest[\"predicted_failure\"],\r\n","    normalize=False,\r\n","    title=\"Confusion Matrix\"\r\n",")\r\n","plt.ylabel('True classes', fontsize=14)\r\n","plt.xlabel('Predicted classes', fontsize=14)\r\n","plt.xticks(fontsize=12)\r\n","plt.yticks(fontsize=12)\r\n","plt.title(\"Confusion Matrix\", fontsize=15)\r\n","\r\n","skplt.metrics.plot_confusion_matrix(\r\n","    test_result_tree[\"failure\"],\r\n","    test_result_tree[\"predicted_failure\"],\r\n","    normalize=True,\r\n",")\r\n","plt.ylabel('True classes', fontsize=14)\r\n","plt.xlabel('Predicted classes', fontsize=14)\r\n","plt.xticks(fontsize=12)\r\n","plt.yticks(fontsize=12)\r\n","plt.title(\"Confusion Matrix (normalized)\", fontsize=15)\r\n","\r\n","plt.show()\r\n","\r\n","evaluation_results_randomForrest.append(evaluation_result_randomForrest)\r\n","evaluation_results_randomForrest[0]  # show full results for first split only"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>none</th>\n","      <th>comp1</th>\n","      <th>comp2</th>\n","      <th>comp3</th>\n","      <th>comp4</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","    </tr>\n","    <tr>\n","      <th>precision</th>\n","      <td>0.999531</td>\n","      <td>0.973597</td>\n","      <td>0.991087</td>\n","      <td>1.000000</td>\n","      <td>0.978571</td>\n","    </tr>\n","    <tr>\n","      <th>recall</th>\n","      <td>0.999876</td>\n","      <td>0.921875</td>\n","      <td>1.000000</td>\n","      <td>0.941964</td>\n","      <td>0.978571</td>\n","    </tr>\n","    <tr>\n","      <th>F1</th>\n","      <td>0.999703</td>\n","      <td>0.947030</td>\n","      <td>0.995524</td>\n","      <td>0.970115</td>\n","      <td>0.978571</td>\n","    </tr>\n","    <tr>\n","      <th>macro precision</th>\n","      <td>0.988557</td>\n","      <td>0.988557</td>\n","      <td>0.988557</td>\n","      <td>0.988557</td>\n","      <td>0.988557</td>\n","    </tr>\n","    <tr>\n","      <th>macro recall</th>\n","      <td>0.968457</td>\n","      <td>0.968457</td>\n","      <td>0.968457</td>\n","      <td>0.968457</td>\n","      <td>0.968457</td>\n","    </tr>\n","    <tr>\n","      <th>macro F1</th>\n","      <td>0.978189</td>\n","      <td>0.978189</td>\n","      <td>0.978189</td>\n","      <td>0.978189</td>\n","      <td>0.978189</td>\n","    </tr>\n","    <tr>\n","      <th>average accuracy</th>\n","      <td>0.999713</td>\n","      <td>0.999713</td>\n","      <td>0.999713</td>\n","      <td>0.999713</td>\n","      <td>0.999713</td>\n","    </tr>\n","    <tr>\n","      <th>micro-averaged precision/recall/F1</th>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","      <td>0.999282</td>\n","    </tr>\n","    <tr>\n","      <th>majority class accuracy</th>\n","      <td>0.981303</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class recall</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class precision</th>\n","      <td>0.981303</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>majority class F1</th>\n","      <td>0.990563</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>expected accuracy</th>\n","      <td>0.963386</td>\n","      <td>0.963386</td>\n","      <td>0.963386</td>\n","      <td>0.963386</td>\n","      <td>0.963386</td>\n","    </tr>\n","    <tr>\n","      <th>kappa</th>\n","      <td>0.980388</td>\n","      <td>0.980388</td>\n","      <td>0.980388</td>\n","      <td>0.980388</td>\n","      <td>0.980388</td>\n","    </tr>\n","    <tr>\n","      <th>random guess accuracy</th>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>random guess precision</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random guess recall</th>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>random guess F1</th>\n","      <td>0.332278</td>\n","      <td>0.008487</td>\n","      <td>0.014519</td>\n","      <td>0.005979</td>\n","      <td>0.007446</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess accuracy</th>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","      <td>0.963055</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess precision</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess recall</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","    <tr>\n","      <th>random weighted guess F1</th>\n","      <td>0.981303</td>\n","      <td>0.004336</td>\n","      <td>0.007533</td>\n","      <td>0.003035</td>\n","      <td>0.003794</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                        none     comp1  ...     comp3     comp4\n","accuracy                            0.999282  0.999282  ...  0.999282  0.999282\n","precision                           0.999531  0.973597  ...  1.000000  0.978571\n","recall                              0.999876  0.921875  ...  0.941964  0.978571\n","F1                                  0.999703  0.947030  ...  0.970115  0.978571\n","macro precision                     0.988557  0.988557  ...  0.988557  0.988557\n","macro recall                        0.968457  0.968457  ...  0.968457  0.968457\n","macro F1                            0.978189  0.978189  ...  0.978189  0.978189\n","average accuracy                    0.999713  0.999713  ...  0.999713  0.999713\n","micro-averaged precision/recall/F1  0.999282  0.999282  ...  0.999282  0.999282\n","majority class accuracy             0.981303  0.000000  ...  0.000000  0.000000\n","majority class recall               1.000000  0.000000  ...  0.000000  0.000000\n","majority class precision            0.981303  0.000000  ...  0.000000  0.000000\n","majority class F1                   0.990563  0.000000  ...  0.000000  0.000000\n","expected accuracy                   0.963386  0.963386  ...  0.963386  0.963386\n","kappa                               0.980388  0.980388  ...  0.980388  0.980388\n","random guess accuracy               0.200000  0.200000  ...  0.200000  0.200000\n","random guess precision              0.981303  0.004336  ...  0.003035  0.003794\n","random guess recall                 0.200000  0.200000  ...  0.200000  0.200000\n","random guess F1                     0.332278  0.008487  ...  0.005979  0.007446\n","random weighted guess accuracy      0.963055  0.963055  ...  0.963055  0.963055\n","random weighted guess precision     0.981303  0.004336  ...  0.003035  0.003794\n","random weighted guess recall        0.981303  0.004336  ...  0.003035  0.003794\n","random weighted guess F1            0.981303  0.004336  ...  0.003035  0.003794\n","\n","[23 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":251}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zqt33VISGlUF","executionInfo":{"status":"ok","timestamp":1615916071100,"user_tz":-60,"elapsed":487,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"d0b4f34a-83c4-46c2-bbb4-0f8132a47aa3"},"source":["evaluation_results_randomForrest[0].mean(axis=1)[0:4]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["accuracy     0.999282\n","precision    0.988557\n","recall       0.968457\n","F1           0.978189\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":253}]},{"cell_type":"markdown","metadata":{"id":"Ki9oZgkRzWSe"},"source":["#Libraries and Configuration - Phyton Context"]},{"cell_type":"code","metadata":{"id":"knn920Ej3CZy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615972336308,"user_tz":-60,"elapsed":5036,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"74458402-7ec7-494e-e423-927a4386f7f5"},"source":["!git clone https://github.com/lukasSchweiger/dataToService.git"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Cloning into 'dataToService'...\n","remote: Enumerating objects: 14, done.\u001b[K\n","remote: Counting objects: 100% (14/14), done.\u001b[K\n","remote: Compressing objects: 100% (11/11), done.\u001b[K\n","remote: Total 14 (delta 2), reused 4 (delta 1), pack-reused 0\u001b[K\n","Unpacking objects: 100% (14/14), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5sZSqWlPzImQ","executionInfo":{"status":"ok","timestamp":1615972445164,"user_tz":-60,"elapsed":4671,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}},"outputId":"56d1d470-e0ec-47a2-d290-363a25e918de"},"source":["pip install scikit-plot"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Collecting scikit-plot\n","  Downloading https://files.pythonhosted.org/packages/7c/47/32520e259340c140a4ad27c1b97050dd3254fdc517b1d59974d47037510e/scikit_plot-0.3.7-py3-none-any.whl\n","Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.4.1)\n","Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (0.22.2.post1)\n","Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (3.2.2)\n","Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.0.1)\n","Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scipy>=0.9->scikit-plot) (1.19.5)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.3.1)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.4.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.10.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=1.4.0->scikit-plot) (1.15.0)\n","Installing collected packages: scikit-plot\n","Successfully installed scikit-plot-0.3.7\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Is3YYh7Zx5eU","executionInfo":{"status":"ok","timestamp":1615972448158,"user_tz":-60,"elapsed":6000,"user":{"displayName":"Lukas Schweiger","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjxNKu5gYXhiLmc6A4hFh0LiqCVecGjoBU-lw7kr7o=s64","userId":"10785070685566481750"}}},"source":["#Chart creation\r\n","import matplotlib.pyplot as plt\r\n","import seaborn as sns\r\n","import scikitplot as skplt\r\n","import plotly.express as px\r\n","#Data manipulation / linear algebra\r\n","import numpy as np\r\n","import pandas as pd\r\n","# Utilities\r\n","from sklearn.preprocessing import scale\r\n","from sklearn.metrics import confusion_matrix, recall_score, accuracy_score, precision_score\r\n","#Algorithms\r\n","from sklearn.ensemble import GradientBoostingClassifier\r\n","from xgboost import XGBClassifier as xgb\r\n","from sklearn import tree\r\n","from sklearn.ensemble import RandomForestClassifier\r\n","#Others\r\n","import warnings"],"execution_count":7,"outputs":[]}]}