Task2Q2.ipynb

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    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "from plotly.subplots import make_subplots\n",
    "import math\n"
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    "## 2 Task Outline\n",
    "Instead of blindly throwing that data at machine learning algorithms, a good data scientist will first do some exploratory data analysis. In your team, we would like you to analyze the data with respect to the following aspects:\n",
    "\n",
    "### 2. Label characteristics: \n",
    "### How are the class labels distributed? \n",
    "### Are the classes unbalanced, and how much? \n",
    "### What is the average duration of a species’ calls (or drumming)? \n",
    "### Are there large inter-/intra-class variations?"
   ]
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    "# Load the Dataset\n",
    "\n",
    "# Load the Dataset\n",
    "\n",
    "label_files = glob.glob(\"Dataset/**/**labels.npy\", recursive=True)\n",
    "label_files.sort()\n",
    "\n",
    "\"\"\"\n",
    "'Dataset/comcuc/133067.labels.npy',\n",
    "'Dataset/comcuc/182336.labels.npy',\n",
    "'Dataset/comcuc/189031.labels.npy',\n",
    "'Dataset/comcuc/240515.labels.npy',\n",
    "'Dataset/comcuc/25627.labels.npy',\n",
    "\"\"\"\n",
    "\n",
    "data_files = glob.glob(\"Dataset/**/**.npy\", recursive=True)\n",
    "data_files = [file for file in data_files if not \".label\" in file]\n",
    "data_files.sort()\n",
    "\n",
    "\"\"\"\n",
    "'Dataset/comcuc/133067.npy',\n",
    "'Dataset/comcuc/182336.npy',\n",
    "'Dataset/comcuc/189031.npy',\n",
    "'Dataset/comcuc/240515.npy',\n",
    "\"\"\"\n",
    "\n",
    "birds = ['comcuc', 'cowpig1', 'eucdov', 'eueowl1', 'grswoo', 'tawowl1']\n",
    "\n"
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   ],
   "source": [
    "def get_drumming_precent (file, bird):\n",
    "\n",
    "    birds = {'comcuc': 1,\n",
    "        'cowpig1': 2,\n",
    "        'eucdov': 3,\n",
    "        'eueowl1': 4,\n",
    "        'grswoo': 5,\n",
    "        'tawowl1': 6}\n",
    "\n",
    "    label = birds.get(bird)\n",
    "\n",
    "    # load the numpy file\n",
    "    file = np.load(file)\n",
    "\n",
    "    # select the first column with the majority voted labels\n",
    "    maj_labels = file[:, 0]\n",
    "\n",
    "    # compute the percentage where a drumming is labeled\n",
    "    percentage = np.count_nonzero(maj_labels == label) / len(maj_labels) * 100\n",
    "\n",
    "    return percentage\n",
    "\n",
    "bird_drumming_distributions = {'comcuc': 1,\n",
    "        'cowpig1': 2,\n",
    "        'eucdov': 3,\n",
    "        'eueowl1': 4,\n",
    "        'grswoo': 5,\n",
    "        'tawowl1': 6}\n",
    "\n",
    "for bird in birds:\n",
    "    percentages = []\n",
    "    for file in [file for file in label_files if bird in file]:\n",
    "        percentages.append(get_drumming_precent(file, bird))\n",
    "\n",
    "    bird_drumming_distributions.update({bird: np.mean(np.asarray(percentages))})\n",
    "\n",
    "\n",
    "bird_drumming_distributions\n",
    "\n",
    "# Create the figure with subplots\n",
    "fig = make_subplots(rows=len(bird_drumming_distributions), cols=1, subplot_titles=list(bird_drumming_distributions.keys()))\n",
    "\n",
    "# Loop through each key-value pair and add a bar chart subplot\n",
    "for i, (key, value) in enumerate(bird_drumming_distributions.items()):\n",
    "    fig.add_trace(\n",
    "        go.Bar(x=['drumming', 'not drumming'], y=[value, 100-value], name=key),\n",
    "        row=i+1, col=1\n",
    "    )\n",
    "\n",
    "# Set the y-axis range of each subplot to 0-100\n",
    "fig.update_yaxes(range=[0, 100])\n",
    "\n",
    "\n",
    "# Set the layout of the figure\n",
    "fig.update_layout(height=1200, width=800, title_text=\"Class Label Distribution by Bird\")\n",
    "\n",
    "# Show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comcuc': 29.165,\n",
       " 'cowpig1': 44.77,\n",
       " 'eucdov': 40.99,\n",
       " 'eueowl1': 11.62,\n",
       " 'grswoo': 25.555,\n",
       " 'tawowl1': 21.11}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bird_drumming_distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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    "def label_distributions(directory, bins):\n",
    "    label_distributions = []\n",
    "    files = glob.glob(os.path.join('Dataset', directory, '*.labels.npy'))\n",
    "\n",
    "    for file in files:\n",
    "        # load the current file\n",
    "        file_data = np.load(file)\n",
    "\n",
    "        # create an empty list to store the current label distribution\n",
    "        label_dist = []\n",
    "\n",
    "        # get the majority labels\n",
    "        majority_labels = file_data[:, 0]\n",
    "\n",
    "        # split the majority labels into bins\n",
    "        bin_size = len(majority_labels) // bins\n",
    "        bins_ = [(i * bin_size, (i + 1) * bin_size) for i in range(bins)]\n",
    "        bins_[-1] = (bins_[-1][0], len(majority_labels))\n",
    "\n",
    "        # compute the label distribution for each bin\n",
    "        for bin in bins_:\n",
    "            bin_labels = majority_labels[bin[0]:bin[1]]\n",
    "            label_dist.append(np.count_nonzero(bin_labels == bin_labels[0]))\n",
    "\n",
    "        label_distributions.append(label_dist)\n",
    "\n",
    "    return label_distributions\n",
    "\n",
    "bins = 10\n",
    "directories = ['comcuc', 'cowpig1', 'eucdov', 'eueowl1', 'grswoo', 'tawowl1']\n",
    "figs = []\n",
    "\n",
    "for directory in directories:\n",
    "    label_distributions_ = np.asarray(label_distributions(directory, bins))\n",
    "    heatmap = go.Heatmap(z=label_distributions_, showscale=True)\n",
    "    title = f'Heatmap of Annotations for {directory}'\n",
    "    layout = go.Layout(title=title, xaxis_title='Annotations', yaxis_title='Files')\n",
    "    fig = go.Figure(data=[heatmap], layout=layout)\n",
    "    figs.append(fig)\n",
    "\n",
    "fig = make_subplots(rows=2, cols=3, subplot_titles=directories)\n",
    "for i, f in enumerate(figs):\n",
    "    fig.add_trace(f.data[0], row=(i//3)+1, col=(i%3)+1)\n",
    "\n",
    "fig.update_layout(height=1200, title_text='Annotations Heatmaps')\n",
    "fig.show()\n"
   ]
  },
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   "execution_count": 6,
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   "source": [
    "def drumming_lengths (file):\n",
    "\n",
    "    # load the numpy file\n",
    "    file = np.load(file)\n",
    "\n",
    "    # select the first column with the majority voted labels\n",
    "    maj_labels = file[:, 0]\n",
    "\n",
    "    # find all consecutive annotations and store their length\n",
    "    consecutive_annotations = []\n",
    "\n",
    "    cons_labels = 0\n",
    "    for label in maj_labels:\n",
    "        if label != 0:\n",
    "            cons_labels += 1\n",
    "        elif label == 0 and cons_labels != 0:\n",
    "            consecutive_annotations.append(cons_labels)\n",
    "            cons_labels = 0\n",
    "\n",
    "    if consecutive_annotations == []:\n",
    "        return 0\n",
    "    else:\n",
    "        return np.mean(consecutive_annotations)\n",
    "\n",
    "bird_drumming_lengths = {'comcuc': 1,\n",
    "        'cowpig1': 2,\n",
    "        'eucdov': 3,\n",
    "        'eueowl1': 4,\n",
    "        'grswoo': 5,\n",
    "        'tawowl1': 6}\n",
    "\n",
    "for bird in birds:\n",
    "    drummings = []\n",
    "    for file in [file for file in label_files if bird in file]:\n",
    "        drummings.append(drumming_lengths(file))\n",
    "\n",
    "    bird_drumming_lengths.update({bird: np.mean(np.asarray(drummings))})\n",
    "\n",
    "bird_drumming_lengths = dict(sorted(bird_drumming_lengths.items(), key=lambda x: x[1]))\n",
    "\n",
    "\n",
    "# Create a list of the keys and values in the dictionary\n",
    "x_values = list(bird_drumming_lengths.keys())\n",
    "y_values = list(bird_drumming_lengths.values())\n",
    "\n",
    "# Create a bar plot using Plotly\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(x=x_values, y=y_values))\n",
    "\n",
    "# Set the title and axis labels\n",
    "fig.update_layout(title='Average Drumming Length',\n",
    "                  xaxis_title='Categories', yaxis_title='Values')\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  }
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