data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\PaperLSTM\\pyTorch\\env\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import utils.data_exploration as de\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# import numexpr as ne\n",
    "import matplotlib.pyplot as plt\n",
    "from torchmetrics.functional.classification import binary_stat_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      TP  FP  TN  FN  TP + FN\n",
      "0      0   0  63   0        0\n",
      "1      0   0   0  63       63\n",
      "2      0   0  63   0        0\n",
      "3      0   0  63   0        0\n",
      "4      0   0   0  63       63\n",
      "...   ..  ..  ..  ..      ...\n",
      "2995   0   0   0  63       63\n",
      "2996   0   0   0  63       63\n",
      "2997   0   0   0  63       63\n",
      "2998   0   0   0  63       63\n",
      "2999   0   0   0  63       63\n",
      "\n",
      "[3000 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Load Dataframe and confusion matrix\n",
    "def load_df(i):\n",
    "    df = pd.read_parquet(f\"parquet_data/automation/R{i}_df.parquet\")\n",
    "    CM_df = pd.read_parquet(f\"parquet_data/automation/CM/R{i}_CM_df.parquet\")\n",
    "    CM = np.load(f\"parquet_data/automation/CM/R{i}_CM.npy\", allow_pickle=False)\n",
    "\n",
    "    # return df\n",
    "    return df, CM_df, CM\n",
    "\n",
    "\n",
    "# df = load_df(4)\n",
    "df, CM_df, CM = load_df(17)\n",
    "conf_matrix_total = np.sum(CM, axis=0)\n",
    "metrics = de.conf_matrix_metrics(conf_matrix_total)\n",
    "# print(df)\n",
    "print(CM_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FALSE NEGATIVES\n",
      "Total \n",
      "        window idx  event_idx  Pred probability  Pred label  True label  \\\n",
      "63               0          1             7.47%           0           1   \n",
      "64               1          1            13.19%           0           1   \n",
      "65               2          1            22.45%           0           1   \n",
      "66               3          1            15.03%           0           1   \n",
      "67               4          1            29.74%           0           1   \n",
      "...            ...        ...               ...         ...         ...   \n",
      "188995          58       2999            19.20%           0           1   \n",
      "188996          59       2999            18.02%           0           1   \n",
      "188997          60       2999            17.48%           0           1   \n",
      "188998          61       2999            16.93%           0           1   \n",
      "188999          62       2999            16.50%           0           1   \n",
      "\n",
      "       Result     TP    FN     FP     TN  TP + FN  \n",
      "63         FN  False  True  False  False     True  \n",
      "64         FN  False  True  False  False     True  \n",
      "65         FN  False  True  False  False     True  \n",
      "66         FN  False  True  False  False     True  \n",
      "67         FN  False  True  False  False     True  \n",
      "...       ...    ...   ...    ...    ...      ...  \n",
      "188995     FN  False  True  False  False     True  \n",
      "188996     FN  False  True  False  False     True  \n",
      "188997     FN  False  True  False  False     True  \n",
      "188998     FN  False  True  False  False     True  \n",
      "188999     FN  False  True  False  False     True  \n",
      "\n",
      "[76014 rows x 11 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LIMITS RANGE range(0, 6)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_positive = df[df[\"Result\"] == \"FP\"]\n",
    "false_negative = df[df[\"Result\"] == \"FN\"]\n",
    "\n",
    "de.false_events_plots(false_positive, \"False Positive\")\n",
    "de.false_events_plots(false_negative, \"False Negatives\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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      "Total windows:................189000\n",
      "True Positives:...............15462\n",
      "False Positives:..............   0\n",
      "True Negatives:...............97524\n",
      "False Negatives:..............76014\n",
      "Accuracy:.....................  59.8%\n",
      "True Positive Rate:...........  16.9%\n",
      "False Positive Rate:..........   0.0%\n",
      "True Negative Rate:........... 100.0%\n",
      "Positive Predictive Value:.... 100.0%\n"
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             "rgb(250,234,220)"
            ]
           ]
          },
          "colorway": [
           "rgb(76,114,176)",
           "rgb(221,132,82)",
           "rgb(85,168,104)",
           "rgb(196,78,82)",
           "rgb(129,114,179)",
           "rgb(147,120,96)",
           "rgb(218,139,195)",
           "rgb(140,140,140)",
           "rgb(204,185,116)",
           "rgb(100,181,205)"
          ],
          "font": {
           "color": "rgb(36,36,36)"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "rgb(234,234,242)",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "paper_bgcolor": "white",
          "plot_bgcolor": "rgb(234,234,242)",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "showgrid": true,
            "ticks": ""
           },
           "bgcolor": "rgb(234,234,242)",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "showgrid": true,
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "rgb(234,234,242)",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "showgrid": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "rgb(234,234,242)",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "showgrid": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "rgb(234,234,242)",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "showgrid": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "fillcolor": "rgb(67,103,167)",
           "line": {
            "width": 0
           },
           "opacity": 0.5
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "showgrid": true,
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "showgrid": true,
            "ticks": ""
           },
           "bgcolor": "rgb(234,234,242)",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "showgrid": true,
            "ticks": ""
           }
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "showgrid": true,
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white"
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "showgrid": true,
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white"
          }
         }
        },
        "width": 400,
        "xaxis": {
         "anchor": "y",
         "constrain": "domain",
         "domain": [
          0,
          1
         ],
         "scaleanchor": "y",
         "title": {
          "text": "Predicted Label"
         }
        },
        "yaxis": {
         "anchor": "x",
         "autorange": "reversed",
         "constrain": "domain",
         "domain": [
          0,
          1
         ],
         "tickangle": -90,
         "title": {
          "text": "Real Label"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(conf_matrix_total)\n",
    "metrics = de.conf_matrix_metrics(conf_matrix_total)\n",
    "de.print_metrics(metrics)\n",
    "de.plot_confusion_matrix(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ingri\\AppData\\Local\\Temp\\ipykernel_9712\\2119455947.py:67: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_signals(relay: int, color: str, linestyle: str):\n",
    "    df = load_df(relay)\n",
    "    df = de.trip_percentage(df)\n",
    "    plt.plot(df, label=f\"R{relay}\", linestyle=linestyle, color=color)\n",
    "\n",
    "\n",
    "colors = [\n",
    "    \"red\",\n",
    "    \"blue\",\n",
    "    \"green\",\n",
    "    \"orange\",\n",
    "    \"purple\",\n",
    "    \"brown\",\n",
    "    \"pink\",\n",
    "    \"gray\",\n",
    "    \"olive\",\n",
    "    \"cyan\",\n",
    "    \"magenta\",\n",
    "    \"teal\",\n",
    "    \"gold\",\n",
    "    \"navy\",\n",
    "    \"lime\",\n",
    "    \"coral\",\n",
    "    \"darkred\",\n",
    "    \"darkgreen\",\n",
    "]\n",
    "\n",
    "linestyles = [\n",
    "    \"-\",\n",
    "    \"--\",\n",
    "    \"-.\",\n",
    "    \":\",\n",
    "    (0, (5, 10)),\n",
    "    (0, (3, 5, 1, 5)),\n",
    "    \"-\",\n",
    "    \"--\",\n",
    "    \"-.\",\n",
    "    \":\",\n",
    "    (0, (5, 10)),\n",
    "    (0, (3, 5, 1, 5)),\n",
    "    \"-\",\n",
    "    \"--\",\n",
    "    \"-.\",\n",
    "    \":\",\n",
    "    (0, (5, 10)),\n",
    "    (0, (3, 5, 1, 5)),\n",
    "]\n",
    "\n",
    "for relay, color, linestyle in zip(range(1, 15), colors, linestyles):\n",
    "    print(relay)\n",
    "    plot_signals(relay, color, linestyle)\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "plt.rcParams[\"font.serif\"] = [\"Times New Roman\"]\n",
    "plt.title(\"Detection probability vs speed\")\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Probability of detection\")\n",
    "ax = plt.gca()\n",
    "pos = ax.get_position()\n",
    "ax.set_position([pos.x0, pos.y0, pos.width * 0.9, pos.height])\n",
    "\n",
    "# Customize legend position\n",
    "ax.legend(loc=\"center right\", bbox_to_anchor=(1.25, 0.5))\n",
    "plt.gca().set_yticklabels([f\"{x:.0%}\" for x in plt.gca().get_yticks()])\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fault Events (True): 1452 \n",
      "Non Fault Events (False): 1548\n",
      "True Positives: 509 \n",
      "False Positives: 0 \n",
      "True Negatives: 1548 \n",
      "False Negatives: 943\n",
      "Total windows:................3000\n",
      "True Positives:............... 509\n",
      "False Positives:..............   0\n",
      "True Negatives:...............1548\n",
      "False Negatives:.............. 943\n",
      "Accuracy:.....................  68.6%\n",
      "True Positive Rate:...........  35.1%\n",
      "False Positive Rate:..........   0.0%\n",
      "True Negative Rate:........... 100.0%\n",
      "Positive Predictive Value:.... 100.0%\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "coloraxis": "coloraxis",
         "hovertemplate": "Predicted Label: %{x}<br>Real Label: %{y}<br>color: %{z}<extra></extra>",
         "name": "0",
         "texttemplate": "%{z}",
         "type": "heatmap",
         "x": [
          "Positive",
          "Negative"
         ],
         "xaxis": "x",
         "y": [
          "Positive",
          "Negative"
         ],
         "yaxis": "y",
         "z": [
          [
           509,
           943
          ],
          [
           0,
           1548
          ]
         ]
        }
       ],
       "layout": {
        "coloraxis": {
         "colorscale": [
          [
           0,
           "rgb(0, 0, 0)"
          ],
          [
           0.09090909090909091,
           "rgb(16, 16, 16)"
          ],
          [
           0.18181818181818182,
           "rgb(38, 38, 38)"
          ],
          [
           0.2727272727272727,
           "rgb(59, 59, 59)"
          ],
          [
           0.36363636363636365,
           "rgb(81, 80, 80)"
          ],
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           0.45454545454545453,
           "rgb(102, 101, 101)"
          ],
          [
           0.5454545454545454,
           "rgb(124, 123, 122)"
          ],
          [
           0.6363636363636364,
           "rgb(146, 146, 145)"
          ],
          [
           0.7272727272727273,
           "rgb(171, 171, 170)"
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           0.8181818181818182,
           "rgb(197, 197, 195)"
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          [
           1,
           "rgb(254, 254, 253)"
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         ]
        },
        "font": {
         "family": "Times New Roman",
         "size": 16
        },
        "height": 300,
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "rgb(36,36,36)"
            },
            "error_y": {
             "color": "rgb(36,36,36)"
            },
            "marker": {
             "line": {
              "color": "rgb(234,234,242)",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "rgb(234,234,242)",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "rgb(36,36,36)",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "rgb(36,36,36)"
            },
            "baxis": {
             "endlinecolor": "rgb(36,36,36)",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "rgb(36,36,36)"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "tickcolor": "rgb(36,36,36)",
             "ticklen": 8,
             "ticks": "outside",
             "tickwidth": 2
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            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "tickcolor": "rgb(36,36,36)",
             "ticklen": 8,
             "ticks": "outside",
             "tickwidth": 2
            },
            "colorscale": [
             [
              0,
              "rgb(2,4,25)"
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              0.06274509803921569,
              "rgb(24,15,41)"
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              0.12549019607843137,
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             [
              0.18823529411764706,
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             [
              0.25098039215686274,
              "rgb(97,30,82)"
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             [
              0.3137254901960784,
              "rgb(123,30,89)"
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        "width": 400,
        "xaxis": {
         "anchor": "y",
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         "title": {
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         }
        },
        "yaxis": {
         "anchor": "x",
         "autorange": "reversed",
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         }
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     },
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   ],
   "source": [
    "p = df[df[\"True label\"] == 1].event_idx.value_counts().shape[0]\n",
    "n = df[df[\"True label\"] == 0].event_idx.value_counts().shape[0]\n",
    "print(f\"Fault Events (True): {p} \\nNon Fault Events (False): {n}\")\n",
    "\n",
    "tp, fp, tn, fn = de.test_result(df)\n",
    "metrics = de.conf_matrix_metrics(np.array([tp, fp, tn, fn, tp + fn]))\n",
    "de.print_metrics(metrics)\n",
    "de.plot_confusion_matrix(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.027209673\n",
      "0.027\n",
      "0.49968964\n",
      "nan\n",
      "nan\n",
      "nan\n",
      "range(0, 6)\n"
     ]
    }
   ],
   "source": [
    "print(false_negative[\"Pred probability\"].apply(np.floor).min())\n",
    "print(false_negative[\"Pred probability\"].min())\n",
    "print(false_negative[\"Pred probability\"].round(3).min())\n",
    "print(false_negative[\"Pred probability\"].max())\n",
    "print(false_positive[\"Pred probability\"].apply(np.floor).min())\n",
    "print(false_positive[\"Pred probability\"].max())\n",
    "print(false_positive[\"Pred probability\"].round(3).max())\n",
    "\n",
    "min_range = (\n",
    "    range(6) if (false_negative[\"Pred probability\"].min() < 0.5) else range(5, 11)\n",
    ")\n",
    "print(min_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Exploration on False Positives and False Negatives\n",
    "\n",
    "false_positive = df[df[\"Result\"] == \"FP\"]\n",
    "false_negative = df[df[\"Result\"] == \"FN\"]\n",
    "de.false_events_plots(false_positive, \"False Positive\")\n",
    "de.false_events_plots(false_negative, \"False Negatives\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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