DroneDetector/logs/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "215a6c3a",
   "metadata": {},
   "source": [
    "# NN inference analysis\n",
    "\n",
    "Анализ CSV с результатами инференса: доля класса `drone`, частоты срабатываний, уверенность модели и интервалы между `drone`-классификациями."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4e8cff32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSV path: /home/sibscience-4/from_ssh/DroneDetector/logs/nn_results_live_6gb.csv\n",
      "Rows: 27258\n",
      "Time range: 2026-05-04 17:35:21.019627763+07:00 -> 2026-05-05 12:17:19.369858371+07:00\n",
      "Missing freq rows: 0\n"
     ]
    },
    {
     "data": {
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       "\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>docker_timestamp</th>\n",
       "      <th>event_time_iso</th>\n",
       "      <th>event_time_epoch</th>\n",
       "      <th>freq</th>\n",
       "      <th>model_id</th>\n",
       "      <th>model_type</th>\n",
       "      <th>prediction</th>\n",
       "      <th>probability</th>\n",
       "      <th>ts</th>\n",
       "      <th>local_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2026-05-04T10:35:21.019627763Z</td>\n",
       "      <td>2026-05-04T17:35:21+07:00</td>\n",
       "      <td>1.777891e+09</td>\n",
       "      <td>2400</td>\n",
       "      <td>2</td>\n",
       "      <td>ensemble_2400_v44</td>\n",
       "      <td>drone</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2026-05-04 10:35:21.019627763+00:00</td>\n",
       "      <td>2026-05-04 17:35:21.019627763+07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2026-05-04T10:35:21.019631281Z</td>\n",
       "      <td>2026-05-04T17:35:21+07:00</td>\n",
       "      <td>1.777891e+09</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>ensemble_1200_v44</td>\n",
       "      <td>noise</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2026-05-04 10:35:21.019631281+00:00</td>\n",
       "      <td>2026-05-04 17:35:21.019631281+07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2026-05-04T10:35:27.048188525Z</td>\n",
       "      <td>2026-05-04T17:35:27+07:00</td>\n",
       "      <td>1.777891e+09</td>\n",
       "      <td>2400</td>\n",
       "      <td>2</td>\n",
       "      <td>ensemble_2400_v44</td>\n",
       "      <td>drone</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2026-05-04 10:35:27.048188525+00:00</td>\n",
       "      <td>2026-05-04 17:35:27.048188525+07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2026-05-04T10:35:29.238925690Z</td>\n",
       "      <td>2026-05-04T17:35:29+07:00</td>\n",
       "      <td>1.777891e+09</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>ensemble_1200_v44</td>\n",
       "      <td>noise</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2026-05-04 10:35:29.238925690+00:00</td>\n",
       "      <td>2026-05-04 17:35:29.238925690+07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2026-05-04T10:35:32.842234116Z</td>\n",
       "      <td>2026-05-04T17:35:32+07:00</td>\n",
       "      <td>1.777891e+09</td>\n",
       "      <td>2400</td>\n",
       "      <td>2</td>\n",
       "      <td>ensemble_2400_v44</td>\n",
       "      <td>drone</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2026-05-04 10:35:32.842234116+00:00</td>\n",
       "      <td>2026-05-04 17:35:32.842234116+07:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 docker_timestamp             event_time_iso  \\\n",
       "0  2026-05-04T10:35:21.019627763Z  2026-05-04T17:35:21+07:00   \n",
       "1  2026-05-04T10:35:21.019631281Z  2026-05-04T17:35:21+07:00   \n",
       "2  2026-05-04T10:35:27.048188525Z  2026-05-04T17:35:27+07:00   \n",
       "3  2026-05-04T10:35:29.238925690Z  2026-05-04T17:35:29+07:00   \n",
       "4  2026-05-04T10:35:32.842234116Z  2026-05-04T17:35:32+07:00   \n",
       "\n",
       "   event_time_epoch  freq  model_id         model_type prediction  \\\n",
       "0      1.777891e+09  2400         2  ensemble_2400_v44      drone   \n",
       "1      1.777891e+09  1200         1  ensemble_1200_v44      noise   \n",
       "2      1.777891e+09  2400         2  ensemble_2400_v44      drone   \n",
       "3      1.777891e+09  1200         1  ensemble_1200_v44      noise   \n",
       "4      1.777891e+09  2400         2  ensemble_2400_v44      drone   \n",
       "\n",
       "   probability                                  ts  \\\n",
       "0         0.99 2026-05-04 10:35:21.019627763+00:00   \n",
       "1         1.00 2026-05-04 10:35:21.019631281+00:00   \n",
       "2         0.99 2026-05-04 10:35:27.048188525+00:00   \n",
       "3         1.00 2026-05-04 10:35:29.238925690+00:00   \n",
       "4         0.92 2026-05-04 10:35:32.842234116+00:00   \n",
       "\n",
       "                           local_time  \n",
       "0 2026-05-04 17:35:21.019627763+07:00  \n",
       "1 2026-05-04 17:35:21.019631281+07:00  \n",
       "2 2026-05-04 17:35:27.048188525+07:00  \n",
       "3 2026-05-04 17:35:29.238925690+07:00  \n",
       "4 2026-05-04 17:35:32.842234116+07:00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "\n",
    "csv_path = Path('/home/sibscience-4/from_ssh/DroneDetector/logs/nn_results_live_6gb.csv')\n",
    "df = pd.read_csv(csv_path)\n",
    "\n",
    "df['ts'] = pd.to_datetime(df['docker_timestamp'], utc=True)\n",
    "df['local_time'] = df['ts'].dt.tz_convert('Asia/Novosibirsk')\n",
    "df['freq'] = pd.to_numeric(df['freq'], errors='coerce').astype('Int64')\n",
    "df['probability'] = pd.to_numeric(df['probability'], errors='coerce')\n",
    "df = df.sort_values('ts').reset_index(drop=True)\n",
    "\n",
    "print(f'CSV path: {csv_path}')\n",
    "print(f'Rows: {len(df)}')\n",
    "print(f'Time range: {df[\"local_time\"].min()} -> {df[\"local_time\"].max()}')\n",
    "print(f'Missing freq rows: {df[\"freq\"].isna().sum()}')\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcc6dd6",
   "metadata": {},
   "source": [
    "## Общая сводка по частотам и классам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7bf0fc3f",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq</th>\n",
       "      <th>prediction</th>\n",
       "      <th>count</th>\n",
       "      <th>avg_probability</th>\n",
       "      <th>min_probability</th>\n",
       "      <th>max_probability</th>\n",
       "      <th>freq_total</th>\n",
       "      <th>class_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1200</td>\n",
       "      <td>drone</td>\n",
       "      <td>3</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.91</td>\n",
       "      <td>13632</td>\n",
       "      <td>0.000220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1200</td>\n",
       "      <td>noise</td>\n",
       "      <td>13629</td>\n",
       "      <td>0.997436</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>13632</td>\n",
       "      <td>0.999780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>drone</td>\n",
       "      <td>11921</td>\n",
       "      <td>0.868013</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>13626</td>\n",
       "      <td>0.874872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2400</td>\n",
       "      <td>noise</td>\n",
       "      <td>1705</td>\n",
       "      <td>0.649185</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>13626</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   freq prediction  count  avg_probability  min_probability  max_probability  \\\n",
       "0  1200      drone      3         0.840000             0.78             0.91   \n",
       "1  1200      noise  13629         0.997436             0.91             1.00   \n",
       "2  2400      drone  11921         0.868013             0.50             1.00   \n",
       "3  2400      noise   1705         0.649185             0.50             1.00   \n",
       "\n",
       "   freq_total  class_rate  \n",
       "0       13632    0.000220  \n",
       "1       13632    0.999780  \n",
       "2       13626    0.874872  \n",
       "3       13626    0.125128  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_summary = (\n",
    "    df.groupby(['freq', 'prediction'], dropna=False)\n",
    "      .agg(\n",
    "          count=('prediction', 'size'),\n",
    "          avg_probability=('probability', 'mean'),\n",
    "          min_probability=('probability', 'min'),\n",
    "          max_probability=('probability', 'max'),\n",
    "      )\n",
    "      .reset_index()\n",
    ")\n",
    "\n",
    "freq_total = df.groupby('freq', dropna=False).size().rename('freq_total').reset_index()\n",
    "class_summary = class_summary.merge(freq_total, on='freq', how='left')\n",
    "class_summary['class_rate'] = class_summary['count'] / class_summary['freq_total']\n",
    "class_summary = class_summary.sort_values(['freq', 'prediction']).reset_index(drop=True)\n",
    "\n",
    "display(class_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "039fcfa6",
   "metadata": {},
   "source": [
    "## Статистика по классу drone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c1ed2bc4",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq</th>\n",
       "      <th>total_inferences</th>\n",
       "      <th>drone_count</th>\n",
       "      <th>avg_drone_probability</th>\n",
       "      <th>median_drone_probability</th>\n",
       "      <th>min_drone_probability</th>\n",
       "      <th>max_drone_probability</th>\n",
       "      <th>first_drone_time</th>\n",
       "      <th>last_drone_time</th>\n",
       "      <th>drone_rate</th>\n",
       "      <th>dataset_duration_min</th>\n",
       "      <th>drone_per_min</th>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
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       "      <td>0.83</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.91</td>\n",
       "      <td>2026-05-05 03:18:55.374394280+07:00</td>\n",
       "      <td>2026-05-05 08:39:23.676669045+07:00</td>\n",
       "      <td>0.000220</td>\n",
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       "      <td>0.002674</td>\n",
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       "      <th>1</th>\n",
       "      <td>2400</td>\n",
       "      <td>13626</td>\n",
       "      <td>11921</td>\n",
       "      <td>0.868013</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2026-05-04 17:35:21.019627763+07:00</td>\n",
       "      <td>2026-05-05 12:17:19.369858371+07:00</td>\n",
       "      <td>0.874872</td>\n",
       "      <td>1121.972504</td>\n",
       "      <td>10.625038</td>\n",
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      ],
      "text/plain": [
       "   freq  total_inferences  drone_count  avg_drone_probability  \\\n",
       "0  1200             13632            3               0.840000   \n",
       "1  2400             13626        11921               0.868013   \n",
       "\n",
       "   median_drone_probability  min_drone_probability  max_drone_probability  \\\n",
       "0                      0.83                   0.78                   0.91   \n",
       "1                      0.93                   0.50                   1.00   \n",
       "\n",
       "                     first_drone_time                     last_drone_time  \\\n",
       "0 2026-05-05 03:18:55.374394280+07:00 2026-05-05 08:39:23.676669045+07:00   \n",
       "1 2026-05-04 17:35:21.019627763+07:00 2026-05-05 12:17:19.369858371+07:00   \n",
       "\n",
       "   drone_rate  dataset_duration_min  drone_per_min  \n",
       "0    0.000220           1121.972504       0.002674  \n",
       "1    0.874872           1121.972504      10.625038  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drone = df[df['prediction'].eq('drone')].copy()\n",
    "\n",
    "total_by_freq = df.groupby('freq', dropna=False).size().rename('total_inferences')\n",
    "drone_by_freq = drone.groupby('freq', dropna=False).agg(\n",
    "    drone_count=('prediction', 'size'),\n",
    "    avg_drone_probability=('probability', 'mean'),\n",
    "    median_drone_probability=('probability', 'median'),\n",
    "    min_drone_probability=('probability', 'min'),\n",
    "    max_drone_probability=('probability', 'max'),\n",
    "    first_drone_time=('local_time', 'min'),\n",
    "    last_drone_time=('local_time', 'max'),\n",
    ")\n",
    "\n",
    "drone_stats = total_by_freq.to_frame().join(drone_by_freq, how='left').fillna({'drone_count': 0})\n",
    "drone_stats['drone_count'] = drone_stats['drone_count'].astype(int)\n",
    "drone_stats['drone_rate'] = drone_stats['drone_count'] / drone_stats['total_inferences']\n",
    "\n",
    "if len(df) > 1:\n",
    "    duration_min = (df['ts'].max() - df['ts'].min()).total_seconds() / 60\n",
    "else:\n",
    "    duration_min = 0\n",
    "\n",
    "drone_stats['dataset_duration_min'] = duration_min\n",
    "drone_stats['drone_per_min'] = drone_stats['drone_count'] / duration_min if duration_min > 0 else 0\n",
    "drone_stats = drone_stats.reset_index().sort_values('freq').reset_index(drop=True)\n",
    "\n",
    "display(drone_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56ae5e2b",
   "metadata": {},
   "source": [
    "## Интервалы между drone-классификациями"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0c43eb07",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq</th>\n",
       "      <th>interval_count</th>\n",
       "      <th>avg_interval_sec</th>\n",
       "      <th>median_interval_sec</th>\n",
       "      <th>min_interval_sec</th>\n",
       "      <th>max_interval_sec</th>\n",
       "      <th>p90_interval_sec</th>\n",
       "      <th>p95_interval_sec</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1200</td>\n",
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       "      <td>16810.115158</td>\n",
       "      <td>17709.610661</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2400</td>\n",
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      ],
      "text/plain": [
       "   freq  interval_count  avg_interval_sec  median_interval_sec  \\\n",
       "0  1200               2       9614.151137          9614.151137   \n",
       "1  2400           11920          5.647513             4.974157   \n",
       "\n",
       "   min_interval_sec  max_interval_sec  p90_interval_sec  p95_interval_sec  \n",
       "0        619.196112      18609.106163      16810.115158      17709.610661  \n",
       "1          0.000000       1210.107950          9.203728         10.079097  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if drone.empty:\n",
    "    print('No drone predictions found')\n",
    "    drone_interval_stats = pd.DataFrame()\n",
    "else:\n",
    "    drone = drone.sort_values(['freq', 'ts']).copy()\n",
    "    drone['dt_drone_freq_sec'] = drone.groupby('freq')['ts'].diff().dt.total_seconds()\n",
    "    drone_interval_stats = (\n",
    "        drone.groupby('freq', dropna=False)['dt_drone_freq_sec']\n",
    "             .agg(\n",
    "                 interval_count='count',\n",
    "                 avg_interval_sec='mean',\n",
    "                 median_interval_sec='median',\n",
    "                 min_interval_sec='min',\n",
    "                 max_interval_sec='max',\n",
    "                 p90_interval_sec=lambda s: s.quantile(0.90),\n",
    "                 p95_interval_sec=lambda s: s.quantile(0.95),\n",
    "             )\n",
    "             .reset_index()\n",
    "    )\n",
    "    display(drone_interval_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ecf96f0",
   "metadata": {},
   "source": [
    "## Drone-события"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff14a339",
   "metadata": {},
   "outputs": [],
   "source": [
    "drone_events = drone[[\n",
    "    'local_time',\n",
    "    'docker_timestamp',\n",
    "    'freq',\n",
    "    'model_id',\n",
    "    'model_type',\n",
    "    'prediction',\n",
    "    'probability',\n",
    "]].sort_values('local_time').reset_index(drop=True)\n",
    "\n",
    "display(drone_events.head(50))\n",
    "display(drone_events.tail(50))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376b84d0",
   "metadata": {},
   "source": [
    "## Частота drone-классификаций по минутам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f82f5c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "if drone.empty:\n",
    "    print('No drone predictions found')\n",
    "else:\n",
    "    drone_per_minute = (\n",
    "        drone.set_index('local_time')\n",
    "             .groupby('freq')\n",
    "             .resample('1min')\n",
    "             .size()\n",
    "             .rename('drone_count')\n",
    "             .reset_index()\n",
    "    )\n",
    "    display(drone_per_minute.tail(100))\n",
    "\n",
    "    pivot = drone_per_minute.pivot_table(\n",
    "        index='local_time',\n",
    "        columns='freq',\n",
    "        values='drone_count',\n",
    "        fill_value=0,\n",
    "    )\n",
    "    display(pivot.tail(50))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad570d05",
   "metadata": {},
   "source": [
    "## Быстрые графики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "895550a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "if not drone.empty:\n",
    "    ax = drone_stats.set_index('freq')['drone_rate'].plot(kind='bar', figsize=(8, 4), title='Drone rate by frequency')\n",
    "    ax.set_ylabel('drone_count / total_inferences')\n",
    "    plt.show()\n",
    "\n",
    "    ax = drone.boxplot(column='probability', by='freq', figsize=(8, 4))\n",
    "    ax.set_title('Drone probability by frequency')\n",
    "    ax.set_xlabel('freq')\n",
    "    ax.set_ylabel('probability')\n",
    "    plt.suptitle('')\n",
    "    plt.show()"
   ]
  }
 ],
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