02_prep_training_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51290fdf-0a70-4d38-9712-18a2601caddc",
   "metadata": {},
   "source": [
    "# Prepare training and validation data from labeled dataset\n",
    "\n",
    "In this notebook, we will take the annotated dataset containing Raven-formatted annotation files, and prepare a table of labels that we can use to train a Convolutional Nerual Network (CNN) in OpenSoundscape. The format of the labels we want to create is a table with the audio file path, start and end time of 2-second audio segments (with 1.5 seconds of overlap between segments), and 0 (absent) or 1 (present) labels for each clip and each annotation 'A', 'B', 'C', 'D', 'E', or 'X'. \n",
    "\n",
    "To do this, we will use the BoxedAnnotations class in OpenSoundscape to load Raven-formatted annotations and convert them to one-hot labels. \n",
    "\n",
    "Then, we'll split our labeled data into training and validation sets for the CNN training phase.\n",
    "\n",
    "\n",
    "This notebook is part of a series of notebooks and scripts in the [repository](https://github.com/kitzeslab/rana-sierrae-cnn):\n",
    "\n",
    "- `01_explore_annotated_data.ipynb` Explore annotated dataset of Rana sierrae call types\n",
    "\n",
    "- `02_prep_training_data.ipynb` Prepare annotated files for training a CNN machine learning model\n",
    "\n",
    "- `03_train_cnn.ipynb` Train a CNN to recognize Rana sierrae vocaliztaions\n",
    "\n",
    "- `04_cnn_prediction.ipynb` Use the cnn to detect Rana sierrae in audio recordings\n",
    "\n",
    "- `05_cnn_validation.ipynb` Analyze the accuracy and performance of the CNN\n",
    "\n",
    "- `06_aggregate_scores.py` Aggregate scores from CNN prediction across dates and times of day\n",
    "\n",
    "- `07_explore_results.ipynb` Analyze temporal patterns of vocal activity using the CNN detections\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "803b1895-f5a1-40d0-a014-dfbd019ab121",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from opensoundscape.annotations import BoxedAnnotations\n",
    "from glob import glob\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935b4c3e-fd6a-437b-8bfb-77a434de5bca",
   "metadata": {},
   "source": [
    "## Download the dataset\n",
    "<!-- Running this cell will download the annotated dataset and save it to the same directory as this notebook. \n",
    " -->\n",
    " \n",
    "<!--  %%bash\n",
    "curl -L https://datadryad.org/stash/downloads/download_resource/0?share=aR1YcRD1JbxzYe3oRLRCHH60_C6QBjWq9lKthC0ODr4 -->\n",
    "\n",
    "\n",
    "Visit this link to download the dataset:  https://datadryad.org/stash/share/aR1YcRD1JbxzYe3oRLRCHH60_C6QBjWq9lKthC0ODr4\n",
    "\n",
    "The size of the dataset is approximately 40 Mb. Download time will depend on the speed of your internet connection.\n",
    "\n",
    "After downloading the data from the link:\n",
    "\n",
    "(1) unzip the downloaded folder `doi_10.5061_dryad.9s4mw6mn3__v1` (double click or right click->unzip)\n",
    "\n",
    "(2) unzip the inner folder `rana_sierrae_2022`\n",
    "\n",
    "(3) change the path in the following cell to the location of the unzipped `rana_seirrae_2022` folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3bfadeb0-6657-47b2-809b-6e7efb75e608",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir='./rana_sierrae_2022'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d507457-c269-48f6-a217-0c37cebb3b9d",
   "metadata": {},
   "source": [
    "The table `audio_and_raven_files.csv` in the dataset pairs each audio file in the dataset with the corresponding annotation file. \n",
    "\n",
    "We'll load this table into a DataFrame called `file_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0d61562-34ed-4c41-90f1-16bd71c5cc76",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_df=pd.read_csv(f'{data_dir}/audio_and_raven_files.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f0a9da-e055-4265-9cb1-ca8f4c5779eb",
   "metadata": {},
   "source": [
    "## Load Raven annotations using OpenSoundscape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54ecba3-b496-491f-9a84-50ba6d26aee5",
   "metadata": {},
   "source": [
    "Raven annotations are delimited in time with a start and end time. When creating labels for two-second clips, we will consider a clip to contain the annotation if either (1) at least 0.2 seconds of the annotation overlap with the clip (`min_label_overlap` argument), or (2) more than 50% of the annotation falls within this 2 second clip (`min_label_fraction` parameter). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b19531a8-1e27-4e15-98f4-baf6760cf93c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "annotation_types = ['A','B','C','D','E','X']\n",
    "all_annotations = []\n",
    "for af, rf in zip(file_df.audio,file_df.raven):\n",
    "    raven_path = f\"{data_dir}/{rf}\"\n",
    "    ba = BoxedAnnotations.from_raven_file(raven_path,annotation_column='annotation',audio_file=af) \n",
    "    ba.df = ba.df.dropna() \n",
    "    # if len(ba.df)>0:\n",
    "    clip_df = ba.one_hot_clip_labels(\n",
    "                           class_subset=annotation_types,\n",
    "                           full_duration=10,\n",
    "                           clip_duration=2,\n",
    "                           clip_overlap=1.5,\n",
    "                           min_label_overlap=0.2,\n",
    "                           min_label_fraction=0.5,\n",
    "                        ).astype(int)\n",
    "    #assert len(clip_df)==17\n",
    "    #we will add the full path of the audio file for eas of use later\n",
    "    clip_df['file']=f\"{data_dir}/{af}\"\n",
    "    clip_df = clip_df.reset_index().set_index(['file','start_time','end_time'])\n",
    "    all_annotations.append(clip_df)\n",
    "labels = pd.concat(all_annotations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c39f5e-b177-4e04-ac49-c334691f187a",
   "metadata": {},
   "source": [
    "We now have a table of one-hot labels for 2-second audio segments. Let's check the number of clips containing each annotation type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33dd7ff9-a6bb-4939-b69f-4a9c23d0c7e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2678\n",
       "B     117\n",
       "C     506\n",
       "D     355\n",
       "E     726\n",
       "X     621\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c3c287-acf5-498a-94dc-a590f23eb1cb",
   "metadata": {},
   "source": [
    "Let's look at a random selection of 5 rows from our one-hot label dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05651987-a8cb-4fd4-bc8b-e9cae8e94bf0",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>file</th>\n",
       "      <th>start_time</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>./rana_sierrae_2022/mp3/sine2022a_MSD-0558_20220622_134500_0-10s.mp3</th>\n",
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       "      <th>./rana_sierrae_2022/mp3/sine2022a_MSD-0558_20220621_011500_0-10s.mp3</th>\n",
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      "text/plain": [
       "                                                                        A  B  \\\n",
       "file                                               start_time end_time         \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 0.5        2.5       1  0   \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 0.0        2.0       0  0   \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 8.0        10.0      1  0   \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 1.0        3.0       1  0   \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 4.0        6.0       0  0   \n",
       "\n",
       "                                                                        C  D  \\\n",
       "file                                               start_time end_time         \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 0.5        2.5       0  0   \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 0.0        2.0       0  0   \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 8.0        10.0      0  0   \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 1.0        3.0       0  0   \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 4.0        6.0       0  0   \n",
       "\n",
       "                                                                        E  X  \n",
       "file                                               start_time end_time        \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 0.5        2.5       0  0  \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 0.0        2.0       0  0  \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 8.0        10.0      0  0  \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 1.0        3.0       0  0  \n",
       "./rana_sierrae_2022/mp3/sine2022a_MSD-0558_2022... 4.0        6.0       0  0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.sample(5,random_state=2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d334ca7b-33a5-484b-9f19-64ffa250e6ab",
   "metadata": {},
   "source": [
    "## Prepare training data\n",
    "We want to train a CNN to detect vocalization types A and E. If a 2-second clip has the X (uncertain) label and does not have an A or E label, we will exclude the clip from training and validation (since we are uncertain if it contains the target classes). \n",
    "\n",
    "We create a table of 0/1 labels, with 1 for A or E annotations and 0 otherwise. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "783c7e12-85e2-4c4f-8886-31de2902361f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#choose training and validation labels: clips labeled with A or E\n",
    "labels['rana_sierrae']=np.logical_or(labels.A,labels.E).astype(int)\n",
    "\n",
    "# drop clips with 0 for rana_sierrae and 1 for 'X' unsure class\n",
    "labels = labels.drop(labels[(labels['rana_sierrae']==0) & (labels.X==1)].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5062308-cb28-4355-b757-9f57c3ffb22c",
   "metadata": {},
   "source": [
    "## Split labeled files into train and validation sets\n",
    "\n",
    "We want to make sure a 10 second file is included entirely in the training or validation set, rather than being split across the two sets. Therefore, we perform a random split on the list of audio files, retaining 10% as validation and using the other 90% for training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be4d4143-e72b-4b4e-859a-a39060362d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels2 = pd.read_csv('./resources/labels_2s.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9be2b64c-2c88-47c5-b337-c0a3da0e7b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_files,val_files = train_test_split(labels.reset_index().file.unique(),test_size=0.1,random_state=20221208)\n",
    "\n",
    "# subset the labels for training and validation based on the file lists\n",
    "train_df = labels.reset_index().set_index('file').loc[train_files][['start_time','end_time','rana_sierrae']]\n",
    "val_df = labels.reset_index().set_index('file').loc[val_files][['start_time','end_time','rana_sierrae']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a356dfd3-d50b-4a52-91de-d2378a8d1fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv('./resources/training_set.csv')\n",
    "val_df.to_csv('./resources/validation_set.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca45822d-014e-42db-ac4e-85ba138c1c9a",
   "metadata": {},
   "source": [
    "# Sanity check: look at a labeled clip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366fb38a-8686-4904-9d66-414f56bbc2fd",
   "metadata": {},
   "source": [
    "Let's check out the audio and spectrogram of a a clip that contains the label 'E'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8ece976-b3dd-4327-8233-41f339c5421f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from opensoundscape import Audio, Spectrogram\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize']=[15,5] #for big visuals\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "from opensoundscape.annotations import BoxedAnnotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4b1a62c1-f01a-4c06-aa73-54ce4fecf6be",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 450,
       "width": 1247
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels:\n",
      "A               0\n",
      "B               0\n",
      "C               0\n",
      "D               0\n",
      "E               1\n",
      "X               0\n",
      "rana_sierrae    1\n",
      "Name: (./rana_sierrae_2022/mp3/sine2022a_MSD-0558_20220625_080000_0-10s.mp3, 2.0, 4.0), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "row = labels.loc[('./rana_sierrae_2022/mp3/sine2022a_MSD-0558_20220625_080000_0-10s.mp3',2.0,4.0)]\n",
    "audio_path = row.name[0]\n",
    "audio=Audio.from_file(audio_path,offset=row.name[1],duration=2)\n",
    "Spectrogram.from_audio(audio,window_samples=1024,overlap_fraction=0.8).bandpass(0,2000).plot()\n",
    "print('labels:')\n",
    "print(random_row)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "opso080",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}