clap_trainer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/tqdm/auto.py:21: 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",
      "/srv/CLAP/dataset.py:36: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.\n",
      "  torchaudio.set_audio_backend(backend='soundfile')\n"
     ]
    }
   ],
   "source": [
    "from dataset import LifeWatchDataset\n",
    "import yaml\n",
    "import pathlib\n",
    "import numpy as np\n",
    "config_path=r\"config.yaml\"\n",
    "with open(config_path, 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "L=LifeWatchDataset(config)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed clap.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/CLAP/models.py:121: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.loss_func = CustomLossFunction(torch.tensor(similarity_matrix).to(device))\n",
      "epoch = 0\n",
      "train:   0%|          | 1/1451 [10:55<264:10:28, 655.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5101361714290465| Distance: 0.516893619048411| Speed: 0.625 | Activity: 0.125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train:   0%|          | 2/1451 [22:15<269:32:54, 669.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.49526674404214943| Distance: 0.5337779067369158| Speed: 0.5625 | Activity: 0.0625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train:   0%|          | 3/1451 [32:18<257:06:13, 639.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.49824589760739574| Distance: 0.5109653849012151| Speed: 0.5833333333333334 | Activity: 0.16666666666666666\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "train:   0%|          | 4/1451 [43:11<259:13:31, 644.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5153632737979907| Distance: 0.5464387896633178| Speed: 0.5625 | Activity: 0.1875\n"
     ]
    },
    {
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     "output_type": "stream",
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      "train:   0%|          | 5/1451 [53:15<253:03:15, 630.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5231336995123477| Distance: 0.5552228325205795| Speed: 0.575 | Activity: 0.175\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "train:   0%|          | 6/1451 [1:02:53<245:51:07, 612.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5190810972127102| Distance: 0.5213851620211839| Speed: 0.6041666666666666 | Activity: 0.25\n"
     ]
    },
    {
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     "output_type": "stream",
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      "train:   0%|          | 7/1451 [1:14:12<254:20:47, 634.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5190808306024273| Distance: 0.5198966224326169| Speed: 0.6071428571428571 | Activity: 0.25\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "train:   1%|          | 8/1451 [1:23:50<247:00:40, 616.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5004783699303996| Distance: 0.490380616550666| Speed: 0.609375 | Activity: 0.234375\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "train:   1%|          | 9/1451 [1:34:19<248:26:39, 620.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.4900451322648806| Distance: 0.48572336858961584| Speed: 0.5833333333333334 | Activity: 0.2361111111111111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train:   1%|          | 10/1451 [1:45:38<255:27:38, 638.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.508487154034802| Distance: 0.5120619233913366| Speed: 0.5875 | Activity: 0.25\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "train:   1%|          | 11/1451 [1:54:26<241:48:37, 604.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5130740545327002| Distance: 0.5161082727060156| Speed: 0.5909090909090909 | Activity: 0.26136363636363635\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "train:   1%|          | 12/1451 [2:07:01<259:55:38, 650.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5166994859000359| Distance: 0.5156796987222821| Speed: 0.6041666666666666 | Activity: 0.2604166666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train:   1%|          | 13/1451 [2:16:38<250:58:50, 628.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training metrics: 0.5152820052959355| Distance: 0.522264880621431| Speed: 0.5865384615384616 | Activity: 0.25961538461538464\n"
     ]
    }
   ],
   "source": [
    "best_model, valid_metric_best=L.train_clap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "\\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\ds_split-CLAP\\train.txt not found.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m d_train_path\u001b[38;5;241m=\u001b[39m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mfs\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mshared\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124monderzoek\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m6. Marine Observation Center\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mProjects\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mIMAGINE\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mUC6\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mds_split-CLAP\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mtrain.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      4\u001b[0m d_valid_path\u001b[38;5;241m=\u001b[39m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mfs\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mshared\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124monderzoek\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m6. Marine Observation Center\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mProjects\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mIMAGINE\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mUC6\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mds_split-CLAP\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mval.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 6\u001b[0m split \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenfromtxt\u001b[49m\u001b[43m(\u001b[49m\u001b[43md_train_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m d_train_loc \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(wavs_folder, i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m split[:, \u001b[38;5;241m0\u001b[39m]])\n\u001b[1;32m      9\u001b[0m split \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mgenfromtxt(d_valid_path, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m'\u001b[39m, delimiter\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/numpy/lib/npyio.py:1980\u001b[0m, in \u001b[0;36mgenfromtxt\u001b[0;34m(fname, dtype, comments, delimiter, skip_header, skip_footer, converters, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise, max_rows, encoding, ndmin, like)\u001b[0m\n\u001b[1;32m   1978\u001b[0m     fname \u001b[38;5;241m=\u001b[39m os_fspath(fname)\n\u001b[1;32m   1979\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m-> 1980\u001b[0m     fid \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_datasource\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1981\u001b[0m     fid_ctx \u001b[38;5;241m=\u001b[39m contextlib\u001b[38;5;241m.\u001b[39mclosing(fid)\n\u001b[1;32m   1982\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/numpy/lib/_datasource.py:193\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;124;03mOpen `path` with `mode` and return the file object.\u001b[39;00m\n\u001b[1;32m    158\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    189\u001b[0m \n\u001b[1;32m    190\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    192\u001b[0m ds \u001b[38;5;241m=\u001b[39m DataSource(destpath)\n\u001b[0;32m--> 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/numpy/lib/_datasource.py:533\u001b[0m, in \u001b[0;36mDataSource.open\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _file_openers[ext](found, mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m    531\u001b[0m                               encoding\u001b[38;5;241m=\u001b[39mencoding, newline\u001b[38;5;241m=\u001b[39mnewline)\n\u001b[1;32m    532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 533\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: \\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\ds_split-CLAP\\train.txt not found."
     ]
    }
   ],
   "source": [
    "import os\n",
    "wavs_folder=r'\\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\data\\data_per_station_10_vessel_information_point'\n",
    "d_train_path= r\"\\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\ds_split-CLAP\\train.txt\"\n",
    "d_valid_path= r\"\\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\ds_split-CLAP\\val.txt\"\n",
    "\n",
    "split = np.genfromtxt(d_train_path, dtype='str', delimiter=' ')\n",
    "d_train_loc = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "split = np.genfromtxt(d_valid_path, dtype='str', delimiter=' ')\n",
    "d_valid_loc = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_speed(speed):\n",
    "    if speed >= 0 and speed < 1.5:\n",
    "        return '0-1.5'\n",
    "    elif speed >= 1.5 and speed < 8.2:\n",
    "        return '1.5-8.2'\n",
    "    elif speed >= 8.2 and speed < 13.3:\n",
    "        return '8.2-13.3'\n",
    "    else:\n",
    "        return '13.3+'\n",
    "\n",
    "# Apply function to create a new column 'speed_category'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def categorize_distance(distance):\n",
    "    if distance >= 0 and distance < 1000:\n",
    "        return '0-1 km'\n",
    "    elif distance >= 1000 and distance < 2000:\n",
    "        return '1-2 km'\n",
    "    elif distance >= 2000 and distance < 3000:\n",
    "        return '2-3 km'\n",
    "    elif distance >= 3000 and distance < 4000:\n",
    "        return '3-4 km'\n",
    "    elif distance >= 4000 and distance < 5000:\n",
    "        return '4-5 km'\n",
    "    elif distance >= 5000 and distance < 6000:\n",
    "        return '5-6 km'\n",
    "    elif distance >= 6000 and distance < 7000:\n",
    "        return '6-7 km'\n",
    "    elif distance >= 7000 and distance < 8000:\n",
    "        return '7-8 km'\n",
    "    elif distance >= 8000 and distance < 9000:\n",
    "        return '8-9 km'\n",
    "    elif distance >= 9000 and distance <= 10000:\n",
    "        return '9-10 km'\n",
    "    else:\n",
    "        return '10+ km'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def process_filenames(d_train):\n",
    "    # Create DataFrame with filenames\n",
    "    df = pd.DataFrame({'filename': d_train})\n",
    "    \n",
    "    # Extract distance from the filename\n",
    "    df['distance'] = df['filename'].apply(lambda x: float(x.split('_')[-1].split('.wav')[0]))\n",
    "\n",
    "    # Extract speed from the filename\n",
    "    df['speed'] = df['filename'].apply(lambda x: float(x.split('_')[-3].replace('-', '.')))\n",
    "\n",
    "    # Extract activity from the filename\n",
    "    df['activity'] = df['filename'].apply(lambda x: x.split('_')[-4])\n",
    "\n",
    "    # Extract ship type from the filename\n",
    "    df['ship_type'] = df['filename'].apply(lambda x: x.split('_')[-5])\n",
    "\n",
    "    # Apply the function to create a new column 'distance_category'\n",
    "    df['distance_category'] = df['distance'].apply(categorize_distance)\n",
    "\n",
    "    # Apply the function to create a new column 'speed_category'\n",
    "    df['speed_category'] = df['speed'].apply(categorize_speed)\n",
    "\n",
    "    # Create a combined_info column\n",
    "    df['label'] = df['ship_type'] + ' at distance ' + df['distance_category'] + ' with speed ' + df['speed_category'] + ' is ' + df['activity']\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'd_valid_loc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[32], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m d_train\u001b[38;5;241m=\u001b[39mprocess_filenames(d_train_loc)\n\u001b[0;32m----> 2\u001b[0m d_valid\u001b[38;5;241m=\u001b[39mprocess_filenames(\u001b[43md_valid_loc\u001b[49m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'd_valid_loc' is not defined"
     ]
    }
   ],
   "source": [
    "d_train=process_filenames(d_train_loc)\n",
    "d_valid=process_filenames(d_valid_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Grafton_28434_train/Grafton_28434_2022-08-30_1...\n",
       "1        Grafton_15810_train/Grafton_15810_2022-05-08_1...\n",
       "2        Grafton_15810_train/Grafton_15810_2022-03-01_2...\n",
       "3        GardenCity_15811_train/GardenCity_15811_2022-0...\n",
       "4        Grafton_15810_train/Grafton_15810_2022-02-21_1...\n",
       "                               ...                        \n",
       "11742    GardenCity_15811_train/GardenCity_15811_2022-0...\n",
       "11743    Grafton_28434_train/Grafton_28434_2022-09-09_1...\n",
       "11744    GardenCity_15811_train/GardenCity_15811_2022-0...\n",
       "11745    GardenCity_15811_train/GardenCity_15811_2022-0...\n",
       "11746    GardenCity_15811_train/GardenCity_15811_2022-0...\n",
       "Name: filename, Length: 11747, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_train[\"filename\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "wavs_folder= '/storage/UC6_new_data/data/'\n",
    "d_train_path= \"ds_split-CLAP/train.txt\"\n",
    "d_valid_path= \"ds_split-CLAP/val.txt\"\n",
    "d_test_path= \"ds_split-CLAP/test.txt\"\n",
    "\n",
    "split = np.genfromtxt(d_train_path, dtype='str', delimiter=' ')\n",
    "d_train = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])\n",
    "d_train_loc = np.array([i for i in split[:, 0]])\n",
    "split = np.genfromtxt(d_valid_path, dtype='str', delimiter=' ')\n",
    "d_valid = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "split = np.genfromtxt(d_test_path, dtype='str', delimiter=' ')\n",
    "d_test = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])\n",
    "d_test_loc = np.array([i for i in split[:, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected CUDA device: Tesla T4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed clap.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/CLAP/models.py:121: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.loss_func = CustomLossFunction(torch.tensor(similarity_matrix).to(device))\n",
      "epoch = 0\n",
      "train:   0%|          | 0/1451 [00:01<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "import utils as u\n",
    "import torch\n",
    "import sys\n",
    "from tqdm import tqdm\n",
    "import torch.optim as optim\n",
    "from transformers import ClapModel, ClapProcessor\n",
    "from transformers import pipeline\n",
    "\n",
    "import models\n",
    "\n",
    "lr=1e-5\n",
    "batch_size=8\n",
    "stop_shuffle=False\n",
    "epochs=10\n",
    "log_path = 'roi/BioLingual/logs.log'\n",
    "log_file = open(log_path, mode='w')\n",
    "\n",
    "model_path='davidrrobinson/BioLingual'\n",
    "desired_fs = 48000\n",
    "sample_dur=10\n",
    "# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    print(\"Selected CUDA device:\", torch.cuda.get_device_name(device))\n",
    "\n",
    "\n",
    "import os\n",
    "wavs_folder= '/storage/UC6_new_data/data/'\n",
    "d_train_path= \"ds_split-CLAP/train.txt\"\n",
    "d_valid_path= \"ds_split-CLAP/val.txt\"\n",
    "d_test_path= \"ds_split-CLAP/test.txt\"\n",
    "\n",
    "split = np.genfromtxt(d_train_path, dtype='str', delimiter=' ')\n",
    "d_train_loc = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "split = np.genfromtxt(d_valid_path, dtype='str', delimiter=' ')\n",
    "d_valid_loc = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "split = np.genfromtxt(d_test_path, dtype='str', delimiter=' ')\n",
    "d_test_loc = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "# split = np.genfromtxt(self.d_train_path, dtype='str', delimiter=' ')\n",
    "# d_train_loc = np.array([os.path.join(self.wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "# split = np.genfromtxt(self.d_valid_path, dtype='str', delimiter=' ')\n",
    "# d_valid_loc = np.array([os.path.join(self.wavs_folder, i) for i in split[:, 0]])\n",
    "d_train=u.process_filenames(d_train_loc)\n",
    "d_valid=u.process_filenames(d_valid_loc)\n",
    "d_test=u.process_filenames(d_test_loc)\n",
    "\n",
    "train_labels = set(d_train[\"label\"])\n",
    "# Count the occurrences of each label in d_train\n",
    "label_counts_train = d_train[\"label\"].value_counts()\n",
    "\n",
    "# Filter out labels which have less than 5 samples in d_train\n",
    "valid_train_labels = label_counts_train[label_counts_train >= 5].index\n",
    "\n",
    "# Filter d_train and d_valid based on valid_train_labels\n",
    "d_train = d_train[d_train[\"label\"].isin(valid_train_labels)]\n",
    "d_valid = d_valid[d_valid[\"label\"].isin(valid_train_labels)]\n",
    "# Filter d_valid based on labels present in d_train\n",
    "# d_valid= d_valid[d_valid[\"label\"].isin(train_labels)]\n",
    "test_train_labels = label_counts_train[label_counts_train >= 5].index\n",
    "\n",
    "# Filter d_train and d_test based on test_train_labels\n",
    "d_train = d_train[d_train[\"label\"].isin(test_train_labels)]\n",
    "d_test = d_test[d_test[\"label\"].isin(test_train_labels)]\n",
    "\n",
    "# Display the number of unique classes\n",
    "num_classes = len(valid_train_labels)\n",
    "ids={lbl: i for i, lbl in enumerate(d_train['label'].unique())}\n",
    "\n",
    "similarity_matrix=u.similarity(ids,device,distance_weight = 0.6, speed_weight = 0.30,activity_weight = 0.10)\n",
    "similarity_matrix_distance=u.similarity(ids,device,distance_weight = 1)\n",
    "similarity_matrix_speed=u.similarity(ids,device,speed_weight = 1)\n",
    "similarity_matrix_activity=u.similarity(ids,device,activity_weight = 1)\n",
    "\n",
    "model = models.CLAPClassifier(model_path, num_classes, sr=desired_fs, device=device, similarity_matrix=similarity_matrix, multi_label=False)\n",
    "\n",
    "dataloader_train = torch.utils.data.DataLoader(\n",
    "    dataset=u.DatasetWaveform(df=d_train, wavs_folder=wavs_folder, desired_fs=desired_fs,\n",
    "                              max_duration=sample_dur,ids=ids),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=not stop_shuffle)\n",
    "\n",
    "dataloader_val = torch.utils.data.DataLoader(\n",
    "    dataset=u.DatasetWaveform(df=d_valid, wavs_folder=wavs_folder, desired_fs=desired_fs,\n",
    "                              max_duration=sample_dur,ids=ids),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=not stop_shuffle)\n",
    "\n",
    "dataloader_test = torch.utils.data.DataLoader(\n",
    "    dataset=u.DatasetWaveform(df=d_test, wavs_folder=wavs_folder, desired_fs=desired_fs,\n",
    "                              max_duration=sample_dur,ids=ids),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=not stop_shuffle)\n",
    "\n",
    "valid_metric_best = 0.\n",
    "best_model = None\n",
    "log_file.write(\"lr = {}\\n\".format(lr))\n",
    "\n",
    "# print(\"lr = {}\".format(lr), file=log_file)\n",
    "\n",
    "optimizer = optim.Adam(params=model.parameters(), lr=lr)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    # print(f'epoch = {epoch}', file=sys.stderr)\n",
    "    sys.stderr.write('epoch = {}\\n'.format(epoch))\n",
    "    model.train()\n",
    "\n",
    "    train_loss = 0.\n",
    "    train_steps = 0\n",
    "    train_metric = u.Accuracy()\n",
    "    metrics=[]\n",
    "    metrics_distance=[]\n",
    "    metrics_speed=[]\n",
    "    metrics_activity=[]\n",
    "    for x, y in tqdm(dataloader_train, desc='train'):\n",
    "        optimizer.zero_grad()\n",
    "        # print(\"optimizer\")\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "        break\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.to(device)\n",
    "loss, logits = model(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def max_finder_true(logits, ids):\n",
    "    predicted_values = []\n",
    "    for tensor in logits:\n",
    "        max_index = torch.argmax(tensor).item()  # Get the index of the maximum value in the tensor\n",
    "        for key, val in ids.items():\n",
    "            if val == max_index:\n",
    "                predicted_values.append(key)\n",
    "                break\n",
    "    return predicted_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in y:\n",
    "    z=i\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([11,  1,  8,  2, 18,  8,  9,  8], device='cuda:0')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Cargo at distance 1-2 km with speed 0-1.5 is at-anchor', 'Cargo at distance 2-3 km with speed 0-1.5 is at-anchor', 'Cargo at distance 3-4 km with speed 0-1.5 is at-anchor', 'Cargo at distance 5-6 km with speed 0-1.5 is at-anchor', 'Cargo at distance 3-4 km with speed 8.2-13.3 is underway-sailing', 'Cargo at distance 3-4 km with speed 0-1.5 is at-anchor', 'Cargo at distance 9-10 km with speed 13.3+ is underway-using-engine', 'Cargo at distance 3-4 km with speed 0-1.5 is at-anchor']\n"
     ]
    }
   ],
   "source": [
    "selected_values = [list(ids.keys())[list(ids.values()).index(idx)] for idx in y.tolist()]\n",
    "\n",
    "print(selected_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted=max_finder(logits,ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([11,  1,  8,  2, 18,  8,  9,  8], device='cuda:0')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(4, device='cuda:0')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.argmax(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cargo at distance 0-1 km with speed 0-1.5 is at-anchor',\n",
       " 'Cargo at distance 0-1 km with speed 0-1.5 is at-anchor',\n",
       " 'Cargo at distance 0-1 km with speed 0-1.5 is at-anchor',\n",
       " 'Cargo at distance 0-1 km with speed 0-1.5 is at-anchor',\n",
       " 'Cargo at distance 0-1 km with speed 0-1.5 is at-anchor',\n",
       " 'Cargo at distance 0-1 km with speed 0-1.5 is at-anchor',\n",
       " 'Cargo at distance 0-1 km with speed 0-1.5 is at-anchor',\n",
       " 'Cargo at distance 0-1 km with speed 0-1.5 is at-anchor']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_values=max_finder(y,ids)\n",
    "true_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "        \n",
    "def max_finder(logits, ids):\n",
    "    predicted_values = []\n",
    "    for tensor in logits:\n",
    "        max_index = torch.argmax(tensor).item()  # Get the index of the maximum value in the tensor\n",
    "        for key, val in ids.items():\n",
    "            if val == max_index:\n",
    "                predicted_values.append(key)\n",
    "                break\n",
    "    return predicted_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>distance</th>\n",
       "      <th>speed</th>\n",
       "      <th>activity</th>\n",
       "      <th>ship_type</th>\n",
       "      <th>distance_category</th>\n",
       "      <th>speed_category</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GardenCity_15811_test/GardenCity_15811_2022-03...</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>underway-using-engine</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>3-4 km</td>\n",
       "      <td>8.2-13.3</td>\n",
       "      <td>Cargo at distance 3-4 km with speed 8.2-13.3 i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GardenCity_15811_test/GardenCity_15811_2022-03...</td>\n",
       "      <td>4756.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>underway-using-engine</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>4-5 km</td>\n",
       "      <td>8.2-13.3</td>\n",
       "      <td>Cargo at distance 4-5 km with speed 8.2-13.3 i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GardenCity_15811_test/GardenCity_15811_2022-03...</td>\n",
       "      <td>7621.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>underway-using-engine</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>7-8 km</td>\n",
       "      <td>8.2-13.3</td>\n",
       "      <td>Cargo at distance 7-8 km with speed 8.2-13.3 i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GardenCity_15811_test/GardenCity_15811_2022-03...</td>\n",
       "      <td>9009.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>underway-using-engine</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>9-10 km</td>\n",
       "      <td>8.2-13.3</td>\n",
       "      <td>Cargo at distance 9-10 km with speed 8.2-13.3 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GardenCity_15811_test/GardenCity_15811_2022-03...</td>\n",
       "      <td>10230.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>underway-using-engine</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>10+ km</td>\n",
       "      <td>8.2-13.3</td>\n",
       "      <td>Cargo at distance 10+ km with speed 8.2-13.3 i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>Grafton_29187_test/Grafton_29187_2022-10-30_23...</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>at-anchor</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>2-3 km</td>\n",
       "      <td>0-1.5</td>\n",
       "      <td>Cargo at distance 2-3 km with speed 0-1.5 is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>Grafton_29187_test/Grafton_29187_2022-10-30_23...</td>\n",
       "      <td>3039.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>at-anchor</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>3-4 km</td>\n",
       "      <td>0-1.5</td>\n",
       "      <td>Cargo at distance 3-4 km with speed 0-1.5 is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>Grafton_29187_test/Grafton_29187_2022-10-30_23...</td>\n",
       "      <td>2822.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>at-anchor</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>2-3 km</td>\n",
       "      <td>0-1.5</td>\n",
       "      <td>Cargo at distance 2-3 km with speed 0-1.5 is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>Grafton_29187_test/Grafton_29187_2022-10-30_23...</td>\n",
       "      <td>3046.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>at-anchor</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>3-4 km</td>\n",
       "      <td>0-1.5</td>\n",
       "      <td>Cargo at distance 3-4 km with speed 0-1.5 is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>Grafton_29187_test/Grafton_29187_2022-10-30_23...</td>\n",
       "      <td>3673.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>at-anchor</td>\n",
       "      <td>Cargo</td>\n",
       "      <td>3-4 km</td>\n",
       "      <td>0-1.5</td>\n",
       "      <td>Cargo at distance 3-4 km with speed 0-1.5 is a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1253 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               filename  distance  speed  \\\n",
       "0     GardenCity_15811_test/GardenCity_15811_2022-03...    3627.0   10.8   \n",
       "1     GardenCity_15811_test/GardenCity_15811_2022-03...    4756.0   10.9   \n",
       "2     GardenCity_15811_test/GardenCity_15811_2022-03...    7621.0   11.1   \n",
       "3     GardenCity_15811_test/GardenCity_15811_2022-03...    9009.0   11.3   \n",
       "4     GardenCity_15811_test/GardenCity_15811_2022-03...   10230.0   11.2   \n",
       "...                                                 ...       ...    ...   \n",
       "1248  Grafton_29187_test/Grafton_29187_2022-10-30_23...    2750.0    0.0   \n",
       "1249  Grafton_29187_test/Grafton_29187_2022-10-30_23...    3039.0    0.0   \n",
       "1250  Grafton_29187_test/Grafton_29187_2022-10-30_23...    2822.0    0.0   \n",
       "1251  Grafton_29187_test/Grafton_29187_2022-10-30_23...    3046.0    0.0   \n",
       "1252  Grafton_29187_test/Grafton_29187_2022-10-30_23...    3673.0    0.0   \n",
       "\n",
       "                   activity ship_type distance_category speed_category  \\\n",
       "0     underway-using-engine     Cargo            3-4 km       8.2-13.3   \n",
       "1     underway-using-engine     Cargo            4-5 km       8.2-13.3   \n",
       "2     underway-using-engine     Cargo            7-8 km       8.2-13.3   \n",
       "3     underway-using-engine     Cargo           9-10 km       8.2-13.3   \n",
       "4     underway-using-engine     Cargo            10+ km       8.2-13.3   \n",
       "...                     ...       ...               ...            ...   \n",
       "1248              at-anchor     Cargo            2-3 km          0-1.5   \n",
       "1249              at-anchor     Cargo            3-4 km          0-1.5   \n",
       "1250              at-anchor     Cargo            2-3 km          0-1.5   \n",
       "1251              at-anchor     Cargo            3-4 km          0-1.5   \n",
       "1252              at-anchor     Cargo            3-4 km          0-1.5   \n",
       "\n",
       "                                                  label  \n",
       "0     Cargo at distance 3-4 km with speed 8.2-13.3 i...  \n",
       "1     Cargo at distance 4-5 km with speed 8.2-13.3 i...  \n",
       "2     Cargo at distance 7-8 km with speed 8.2-13.3 i...  \n",
       "3     Cargo at distance 9-10 km with speed 8.2-13.3 ...  \n",
       "4     Cargo at distance 10+ km with speed 8.2-13.3 i...  \n",
       "...                                                 ...  \n",
       "1248  Cargo at distance 2-3 km with speed 0-1.5 is a...  \n",
       "1249  Cargo at distance 3-4 km with speed 0-1.5 is a...  \n",
       "1250  Cargo at distance 2-3 km with speed 0-1.5 is a...  \n",
       "1251  Cargo at distance 3-4 km with speed 0-1.5 is a...  \n",
       "1252  Cargo at distance 3-4 km with speed 0-1.5 is a...  \n",
       "\n",
       "[1253 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_test_loc\n",
    "d_test=process_filenames(d_test_loc)\n",
    "d_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "L=LifeWatchDataset(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "path  \\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\ds_split-CLAP\\train.txt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b8d64e3b2be4f218b31cc742ac613f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/5.46k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wout.decrop\\.pyenv\\pyenv-win\\versions\\3.8.10\\lib\\site-packages\\huggingface_hub\\file_download.py:149: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\wout.decrop\\.cache\\huggingface\\hub\\models--davidrrobinson--BioLingual. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88b22385799441dcbe9dd2c68455ed08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/615M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\wout.decrop\\.pyenv\\pyenv-win\\versions\\3.8.10\\lib\\site-packages\\torch\\_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed clap.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e874cfd3b0a43659b5e8dd27ccd4db2",
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e10b8a2389244e7da4aa0c19347c3c87",
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e76def90e232414498972e614930d2e6",
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      "text/plain": [
       "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "81767df0e0b04302b161b305330032da",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "a23179b172054b3b92e49347547524f9",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c1f048e29554369b89c6941e7da5cd4",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch = 0\n",
      "train: 100%|██████████| 735/735 [4:30:39<00:00, 22.09s/it]  \n",
      "valid: 100%|██████████| 90/90 [24:05<00:00, 16.06s/it]\n",
      "epoch = 1\n",
      "train: 100%|██████████| 735/735 [3:19:12<00:00, 16.26s/it]  \n",
      "valid: 100%|██████████| 90/90 [17:13<00:00, 11.48s/it]\n",
      "epoch = 2\n",
      "train: 100%|██████████| 735/735 [3:02:30<00:00, 14.90s/it]  \n",
      "valid: 100%|██████████| 90/90 [15:59<00:00, 10.66s/it]\n",
      "epoch = 3\n",
      "train: 100%|██████████| 735/735 [2:41:33<00:00, 13.19s/it]  \n",
      "valid: 100%|██████████| 90/90 [14:18<00:00,  9.54s/it]\n",
      "epoch = 4\n",
      "train: 100%|██████████| 735/735 [2:34:59<00:00, 12.65s/it]  \n",
      "valid: 100%|██████████| 90/90 [15:07<00:00, 10.08s/it]\n",
      "epoch = 5\n",
      "train: 100%|██████████| 735/735 [2:48:14<00:00, 13.73s/it]  \n",
      "valid: 100%|██████████| 90/90 [17:20<00:00, 11.56s/it]\n",
      "epoch = 6\n",
      "train:  30%|██▉       | 217/735 [51:03<2:14:32, 15.58s/it]"
     ]
    }
   ],
   "source": [
    "L.train_clap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "desired_fs = 48000\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "log_path = r'C:\\Users\\wout.decrop\\project_environment\\module_imagine\\underwater-noise-classification\\roi/runs/BioLingual/logs.log'\n",
    "log_file = open(log_path, mode='w')\n",
    "\n",
    "# detections = self.convert_raven_to_ae_format(labels_to_exclude=None)\n",
    "# detections = detections.loc[~detections.label.isna()]\n",
    "\n",
    "d_test_path= r\"\\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\ds_split-CLAP\\test.txt\"\n",
    "print(\"path \", d_test_path)\n",
    "split = np.genfromtxt(d_test_path, dtype='str', delimiter=' ')\n",
    "d_test_loc = np.array([os.path.join(wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "\n",
    "# split = np.genfromtxt(self.d_train_path, dtype='str', delimiter=' ')\n",
    "# d_train_loc = np.array([os.path.join(self.wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "# split = np.genfromtxt(self.d_valid_path, dtype='str', delimiter=' ')\n",
    "# d_valid_loc = np.array([os.path.join(self.wavs_folder, i) for i in split[:, 0]])\n",
    "\n",
    "d_test=u.process_filenames(d_test_loc)\n",
    "\n",
    "num_classes = len(d_test[\"label\"].unique())\n",
    "model = models.CLAPClassifier(model_path, num_classes, sr=esired_fs, device=device,  multi_label=False)\n",
    "\n",
    "dataloader_train = torch.utils.data.DataLoader(\n",
    "    dataset=u.DatasetWaveform(df=d_train, wavs_folder=wavs_folder, desired_fs=desired_fs,\n",
    "                                max_duration=sample_dur),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=not stop_shuffle)\n",
    "\n",
    "for x, y in tqdm(dataloader_train, desc='train'):\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    x = x.to(device)\n",
    "    y = y.to(device)\n",
    "\n",
    "    loss, logits = model(x, y)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    train_loss += loss.cpu()\n",
    "    train_steps += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['at-anchor', 'constrained-by-her-draught', 'moored',\n",
       "       'not-under-command', 'reserved-for-future-use',\n",
       "       'restricted-maneuverability', 'underway-sailing',\n",
       "       'underway-using-engine'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "def eval_pytorch_model(model, dataloader, metric_factory, device, desc):\n",
    "    model.eval()\n",
    "    total_loss = 0.\n",
    "    steps = 0\n",
    "    metric = metric_factory()\n",
    "    with torch.no_grad():\n",
    "        for x, y in tqdm(dataloader, desc=desc):\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "\n",
    "            loss, logits = model(x, y)\n",
    "            total_loss += loss.cpu().item()\n",
    "            steps += 1\n",
    "\n",
    "            metric.update(logits.to(\"cpu\"), y.to(\"cpu\"))\n",
    "\n",
    "    total_loss /= steps\n",
    "\n",
    "    return total_loss, metric.get_primary_metric()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wout.decrop\\AppData\\Local\\Temp\\ipykernel_18128\\2609558822.py:22: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(data=df, x='activity', order=activities, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sample DataFrame\n",
    "# df = pd.DataFrame({\n",
    "#     'filename': ['file1', 'file2', 'file3', 'file4', 'file5'],\n",
    "#     'distance': [868.0, 2169.0, 5666.0, 9623.0, 5290.0],\n",
    "#     'speed': [0.1, 0.0, 0.0, 10.1, 0.1],\n",
    "#     'activity': ['at-anchor', 'at-anchor', 'at-anchor', 'underway-using-engine', 'at-anchor']\n",
    "# })\n",
    "\n",
    "# Define activity categories\n",
    "activities = ['at-anchor', 'constrained-by-her-draught', 'moored',\n",
    "              'not-under-command', 'reserved-for-future-use',\n",
    "              'restricted-maneuverability', 'underway-sailing',\n",
    "              'underway-using-engine']\n",
    "\n",
    "# Set plot size\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot the distribution of activities\n",
    "sns.countplot(data=df, x='activity', order=activities, palette='viridis')\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Activity')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Activities')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Remove speeds above 40\n",
    "df = df[df['speed'] <= 40]\n",
    "\n",
    "grouped_df = df.groupby('activity')['speed'].mean().reset_index()\n",
    "\n",
    "# Plot the speed based on activity\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.boxplot([df[df['activity'] == 'at-anchor']['speed'],\n",
    "             df[df['activity'] == 'underway-using-engine']['speed']],\n",
    "            labels=['at-anchor', 'underway-using-engine'])\n",
    "plt.xlabel('Activity')\n",
    "plt.ylabel('Speed')\n",
    "plt.title('Boxplot of Speed Based on Activity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "whiskers: [<matplotlib.lines.Line2D object at 0x000002004E8A80A0>, <matplotlib.lines.Line2D object at 0x000002004E8A8340>, <matplotlib.lines.Line2D object at 0x000002004E8B1340>, <matplotlib.lines.Line2D object at 0x000002004E8B15E0>]\n",
      "caps: [<matplotlib.lines.Line2D object at 0x000002004E8A85E0>, <matplotlib.lines.Line2D object at 0x000002004E8A8880>, <matplotlib.lines.Line2D object at 0x000002004E8B1880>, <matplotlib.lines.Line2D object at 0x000002004E8B1B50>]\n",
      "boxes: [<matplotlib.lines.Line2D object at 0x000002004EA17DC0>, <matplotlib.lines.Line2D object at 0x000002004E8B10A0>]\n",
      "medians: [<matplotlib.lines.Line2D object at 0x000002004E8A8B20>, <matplotlib.lines.Line2D object at 0x000002004E8B1DF0>]\n",
      "fliers: [<matplotlib.lines.Line2D object at 0x000002004E8A8DC0>, <matplotlib.lines.Line2D object at 0x000002004E8C40D0>]\n",
      "means: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_data = [df[df['activity'] == 'at-anchor']['speed'],\n",
    "                df[df['activity'] == 'underway-using-engine']['speed']]\n",
    "boxplot_dict = plt.boxplot(grouped_data, labels=['at-anchor', 'underway-using-engine'])\n",
    "\n",
    "for key in boxplot_dict.keys():\n",
    "    data = boxplot_dict[key]\n",
    "    print(f\"{key}: {data}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df[df[\"activity\"]==\"at-anchor\"][\"speed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.1,  8.2])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxplot_dict[\"whiskers\"][2].get_ydata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0.])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxplot_dict[\"whiskers\"][1].get_ydata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.4, 13.3])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxplot_dict[\"whiskers\"][3].get_ydata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower Bound (2.5th percentile): 0.0\n",
      "Upper Bound (97.5th percentile): 13.7\n"
     ]
    }
   ],
   "source": [
    "lower_bound = df['speed'].quantile(0.025)\n",
    "upper_bound = df['speed'].quantile(0.975)\n",
    "\n",
    "print(\"Lower Bound (2.5th percentile):\", lower_bound)\n",
    "print(\"Upper Bound (97.5th percentile):\", upper_bound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dataset.LifeWatchDataset at 0x24947f4d8e0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LifeWatchDataset(config)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of frames: 461472\n"
     ]
    }
   ],
   "source": [
    "import wave\n",
    "\n",
    "def get_num_frames_wav(wav_file):\n",
    "    with wave.open(wav_file, 'rb') as wf:\n",
    "        num_frames = wf.getnframes()\n",
    "    return num_frames\n",
    "\n",
    "# Example usage:\n",
    "wav_file= r'\\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\data\\data_per_station_10_vessel_information_point\\GardenCity_15811_test\\GardenCity_15811_2022-03-25_00-05-28_272-0_Cargo_underway-using-engine_10-8_2022-03-25-00-10-00_3627.wav'\n",
    "num_frames = get_num_frames_wav(wav_file)\n",
    "print(\"Number of frames:\", num_frames)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file does not exist or the path is incorrect.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "if os.path.exists(wav_file):\n",
    "    print(\"The file exists.\")\n",
    "else:\n",
    "    print(\"The file does not exist or the path is incorrect.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The folder exists.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "folder_path = r'\\\\fs\\shared\\onderzoek\\6. Marine Observation Center\\Projects\\IMAGINE\\UC6\\data\\data_per_station_10_vessel_information_point\\GardenCity_15811_test\\GardenCity_15811_2022-03-25_00-05-28_272-0_Cargo_underway-using-engine_10-8_2022-03-25-00-10-00_3627.wav'\n",
    "\n",
    "if os.path.exists(folder_path):\n",
    "    print(\"The folder exists.\")\n",
    "else:\n",
    "    print(\"The folder does not exist or the path is incorrect.\")\n"
   ]
  }
 ],
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