performance_metrics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "peformance_metrics.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6TurZRru-Dz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3db0b29f-0a32-4af5-fe22-6f0805e441a9"
      },
      "source": [
        "import numpy as np\n",
        "from numpy.testing import assert_allclose\n",
        "from keras.models import Sequential, load_model\n",
        "from keras.layers import LSTM, Dropout, Dense\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "!pip install plotly --upgrade  # make sure your plotly is up to date\n",
        "\n",
        "import plotly.express as px\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy.io import loadmat\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: plotly in /usr/local/lib/python3.7/dist-packages (4.14.3)\n",
            "Requirement already satisfied, skipping upgrade: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly) (1.3.3)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unpjoHPavUAr",
        "outputId": "05b8acb1-912b-4845-c634-20819b19adba"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "spectra = loadmat('/content/drive/MyDrive/Spectra_3s_25c.mat')\n",
        "#more_spectra = loadmat('/content/drive/MyDrive/5_more_spectra.mat')\n",
        "spectra.keys()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['__header__', '__version__', '__globals__', 'AmpSAmp', 'Dm_total_save', 'GammaAmp', 'GammaSAmp', 'GammaS_total_save', 'Gamma_total_save', 'M', 'NmaxS', 'dG', 'dGS', 'frequencies', 'n_int', 'noiseAmpScaled', 'omegaS_total_save', 'omega_total_save'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsE4MPuSvJ8Y"
      },
      "source": [
        "new_model = load_model('/content/drive/MyDrive/model_complete.h5')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukTj6sjlvZqM"
      },
      "source": [
        "all_channels = pd.DataFrame(np.transpose(np.transpose(spectra['Dm_total_save'])[0]))\n",
        "modes = [x[0]*np.shape(spectra['Dm_total_save'])[0] for x in spectra['omega_total_save']]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eDgXTcPvlld",
        "outputId": "4fc3fc77-ad46-4dd0-c4f5-d4e8fd555f3b"
      },
      "source": [
        "labeled_modes=[]\n",
        "for i in range(3):\n",
        "  print(\"ITERATION: \"+str(i))\n",
        "  amp=[]\n",
        "  length = np.shape(spectra['Dm_total_save'])[0]\n",
        "  modes = [x[i]*length for x in spectra['omega_total_save']]\n",
        "  seeker = 0\n",
        "  modes = np.sort(modes)\n",
        "  for j in range(length):\n",
        "    all_amp = [0]\n",
        "    while seeker < len(modes) and modes[seeker] <= j+100:\n",
        "        # print(np.exp(np.square((j-modes[seeker])/100)))\n",
        "      all_amp.append(1/(np.exp(np.square((j-modes[seeker])/10))))\n",
        "      seeker += 1\n",
        "    while seeker < len(modes) and seeker >= 0 and modes[seeker] >= j-100:\n",
        "      # print(modes[seeker])\n",
        "      seeker -= 1\n",
        "    seeker = max(seeker,0)\n",
        "    amp.append(max(all_amp))\n",
        "    if j %100000 ==0: print(j)\n",
        "  for i in range(5):\n",
        "    labeled_modes.extend(amp)\n",
        "  print(len(labeled_modes))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ITERATION: 0\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: overflow encountered in exp\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "100000\n",
            "200000\n",
            "300000\n",
            "400000\n",
            "500000\n",
            "600000\n",
            "700000\n",
            "800000\n",
            "900000\n",
            "5000000\n",
            "ITERATION: 1\n",
            "0\n",
            "100000\n",
            "200000\n",
            "300000\n",
            "400000\n",
            "500000\n",
            "600000\n",
            "700000\n",
            "800000\n",
            "900000\n",
            "10000000\n",
            "ITERATION: 2\n",
            "0\n",
            "100000\n",
            "200000\n",
            "300000\n",
            "400000\n",
            "500000\n",
            "600000\n",
            "700000\n",
            "800000\n",
            "900000\n",
            "15000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rn9hG2b_vnZS"
      },
      "source": [
        "# training set generation\n",
        "x = []\n",
        "for i in range(3):\n",
        "  all_channels = pd.DataFrame(np.transpose(np.transpose(spectra['Dm_total_save'])[i]))\n",
        "  x.extend(np.array(all_channels[[0,1,2,3,4]]))\n",
        "  x.extend(np.array(all_channels[[5,6,7,8,9]]))\n",
        "  x.extend(np.array(all_channels[[10,11,12,13,14]]))\n",
        "  x.extend(np.array(all_channels[[15,16,17,18,19]]))\n",
        "  x.extend(np.array(all_channels[[20,21,22,23,24]]))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oGtQjC0y83f",
        "outputId": "ba15cb75-819a-4690-b788-6d176bf8b0f4"
      },
      "source": [
        "!pip freeze | grep plotly"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "plotly==4.14.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_Jan4r0czzX"
      },
      "source": [
        "pd.options.plotting.backend = \"plotly\" \n",
        "\n",
        "def show_spectra(view_range,spect,modes, spectra_sample=True):\n",
        "  # take subset of plot\n",
        "  t_modes = np.sort(modes)\n",
        "  temp_modes = pd.Series(t_modes)[(pd.Series(t_modes) < view_range[1])]\n",
        "  temp_modes = pd.Series(temp_modes)[(pd.Series(temp_modes) > view_range[0])]\n",
        "  fig = []\n",
        "  if spectra_sample:\n",
        "    fig = pd.Series(spect).iloc[range(view_range[0],view_range[1])].plot(kind='line')\n",
        "  else:\n",
        "    fig = pd.Series(spect).plot(template='plotly_dark', kind='line')\n",
        "    temp_modes -= view_range[0]\n",
        "  for mode in temp_modes:\n",
        "    fig.add_vline(x=mode, line_width=1, line_color=\"green\") #requires plotly 4.12 and above\n",
        "  fig.show()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luNAdATjbAVc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "cb82b2e2-e4db-4cf2-aab7-3e56ed0f7398"
      },
      "source": [
        "modes = [x[0]*length for x in spectra['omega_total_save']]\n",
        "show_spectra((int(500000), int(501000)), labeled_modes, modes)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vu7igS8c9gQ"
      },
      "source": [
        "def stop():\n",
        "  spectra =  loadmat('Spectra.mat')#['Dm_total_save']\n",
        "\n",
        "  labeled_modes=[]\n",
        "  for i in range(1):\n",
        "    print(\"ITERATION: \"+str(i))\n",
        "    amp=[]\n",
        "    length = np.shape(spectra['Dm_total_save'])[0]\n",
        "    modes = [x[i]*length for x in spectra['omega_total_save']]\n",
        "    seeker = 0\n",
        "    modes = np.sort(modes)\n",
        "    for j in range(length):\n",
        "      all_amp = [0]\n",
        "      while seeker < len(modes) and modes[seeker] <= j+100:\n",
        "          # print(np.exp(np.square((j-modes[seeker])/100)))\n",
        "        all_amp.append(1/(np.exp(np.square((j-modes[seeker])/10))))\n",
        "        seeker += 1\n",
        "      while seeker < len(modes) and seeker >= 0 and modes[seeker] >= j-100:\n",
        "        # print(modes[seeker])\n",
        "        seeker -= 1\n",
        "      seeker = max(seeker,0)\n",
        "      amp.append(max(all_amp))\n",
        "      if j %100000 ==0: print(j)\n",
        "    labeled_modes.extend(amp)\n",
        "    print(len(labeled_modes))\n",
        "\n",
        "#y = labeled_modes\n",
        "#x = spectra['Dm_total_save']\n",
        "#window_len = 1000\n",
        "#reshaped_y = np.reshape(y, (int(len(y)/window_len), window_len))\n",
        "#reshaped_x = np.reshape(np.array(x), (int(len(x)/window_len), window_len, 5))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGoDYuwbbd7h"
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVfWdcBJwuNN"
      },
      "source": [
        "y = labeled_modes\n",
        "window_len = 1000\n",
        "reshaped_y = np.reshape(y, (int(len(y)/window_len), window_len))\n",
        "reshaped_x = np.reshape(np.array(x), (int(len(x)/window_len), window_len, 5))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxBi7csU_igM"
      },
      "source": [
        "first_spectra = reshaped_x[0:1000]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvhFHAkBwxnN"
      },
      "source": [
        "y_pred = new_model.predict(first_spectra)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ZC68cmncxAae",
        "outputId": "305d7b19-ae36-482a-d152-67106783e96c"
      },
      "source": [
        "np.reshape(y_pred, (1, 1000000))\n",
        "\n",
        "#modes = [x[0]*length for x in spectra['omega_total_save']]\n",
        "show_spectra((int(500000), int(501000)), np.reshape(y_pred, (1, 1000000))[0], modes)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "QM2t1cbq7i1r",
        "outputId": "2e4ae72a-aa83-4cd0-bc72-d0f8c47f2696"
      },
      "source": [
        "show_spectra((int(500000), int(501000)), y, modes)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qA6_BS7dyK5q"
      },
      "source": [
        "import seaborn as sns\n",
        "from scipy.signal import savgol_filter\n",
        "from scipy.signal import find_peaks\n",
        "\n",
        "reshaped_y_pred = np.reshape(y_pred, (1, 1000000))[0]\n",
        "#pd.Series(np.reshape(reshaped_y, (1, 1000000))[0][0:10000]).plot()\n",
        "#pd.Series(savgol_filter(y_pred, 51, 3)[0:10000]).plot()\n",
        "predicted_modes = find_peaks(reshaped_y_pred, height=0.65)[0]\n",
        "#print(len(modes))\n",
        "#sns.lineplot(data = pd.Series(np.reshape(reshaped_y, (1, 1000000))[0][0:10000]))"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-9mg6FcAhdF",
        "outputId": "70030b96-7248-4c70-d5ec-af5594f2b4de"
      },
      "source": [
        "print(len(predicted_modes))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10085\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vd4ScmEGyvQ4"
      },
      "source": [
        "modes = [x[0]*length for x in spectra['omega_total_save']]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ri4lExmWkmF",
        "outputId": "04f49c1a-dc3b-43a7-b28b-cdbe8c434d5f"
      },
      "source": [
        "peaks_detected_count = []\n",
        "peaks_detected = []\n",
        "for s in range(10):\n",
        "  window_peaks = [i for i in predicted_modes if (i >= s * 100000) and (i < (s+1) * 100000)]#find_peaks(smooth_density[s*100000 : (s+1)*100000], prominence=density_prominence)\n",
        "  peaks_detected_count.append(len(window_peaks))\n",
        "  window_peaks = [i for i in modes if (i >= s * 100000) and (i < (s+1) * 100000)]\n",
        "  peaks_detected.append(len(window_peaks))\n",
        "  #peaks_detected.extend(window_peaks+s*100000)\n",
        "print(\"number of peaks detected:\", str(np.sum(peaks_detected_count)))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of peaks detected: 10085\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGDfQQ0Jb57Y",
        "outputId": "9bef5c0a-47a9-46ca-b067-3c119a6b53c2"
      },
      "source": [
        "peaks_detected_count"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[122, 323, 434, 768, 938, 1146, 1274, 1494, 1748, 1838]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "SNV81fYG2kwR",
        "outputId": "b6e7f7a5-c00b-4583-f275-1e5aaeb51095"
      },
      "source": [
        "pd.Series(predicted_modes).plot.hist()\n",
        "# pd.Series(modes).plot.hist()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaO7vHa2P5jA"
      },
      "source": [
        "# a_l = [0]*len(a)\n",
        "# b_l = [0]*len(b)\n",
        "# threshold = .1\n",
        "# for i in range(len(dists)):\n",
        "#   while dist < threshold:\n",
        "#     if a[i] == 0:\n",
        "#       row_min = np.argmin(dist)//dist.shape[1]\n",
        "#       col_min = np.argmin(dist)%dist.shape[1]\n",
        "#       a[i] = col_min\n",
        "#       b[i] = row_min\n",
        "      \n",
        "    "
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSeuO9fn8Fpg"
      },
      "source": [
        "modes = np.array(modes)\n",
        "modes.sort()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xkAuVvgK7iL",
        "outputId": "ced40aa1-2870-4dbd-a3a5-ae6ee487feb1"
      },
      "source": [
        "modes"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  1190.20695012,   3183.28463774,   3444.60805029, ...,\n",
              "       999582.11096774, 999721.85571522, 999893.73555854])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lb6AwY2zGRAU"
      },
      "source": [
        "tp = num_pairs\n",
        "\n",
        "fp = in pred modes but not in modes = len(predicted_modes) - num_pairs\n",
        "\n",
        "tn = does not exist\n",
        "\n",
        "fn = in modes but not in pred modes = len(modes) - num_pairs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OB80CDgXIgX8"
      },
      "source": [
        "# Insert sorted np.arrays of modes and predicted modes\n",
        "def get_correct_preds(modes, predicted_modes, threshold = 50):\n",
        "  mode_dists = np.abs(predicted_modes[:, np.newaxis] - modes)\n",
        "  dist_df = pd.DataFrame(mode_dists.flatten(), columns = ['distance'])\n",
        "  dist_df['predicted_index'] = np.indices((mode_dists.shape[0], mode_dists.shape[1]))[0].flatten()\n",
        "  dist_df['actual_index'] = np.indices((mode_dists.shape[0], mode_dists.shape[1]))[1].flatten()\n",
        "\n",
        "  filter_df = dist_df[dist_df['distance']<50]\n",
        "  sort_df = filter_df.sort_values(filter_df.columns[0])\n",
        "  dist_arr = sort_df.to_numpy()\n",
        "\n",
        "  p_actual= []\n",
        "  p_pred = []\n",
        "  num_pairs = 0\n",
        "  for item in dist_arr:\n",
        "    if item[1] not in p_pred and item[2] not in p_actual:\n",
        "      p_pred.append(item[1])\n",
        "      p_actual.append(item[2])\n",
        "      num_pairs += 1\n",
        "\n",
        "  return num_pairs, p_actual, p_pred"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tARCZFARPns"
      },
      "source": [
        "def get_precision_recall_f1(num_pairs, modes, predicted_modes):\n",
        "  fp = len(predicted_modes) - num_pairs\n",
        "  fn = len(modes) - num_pairs\n",
        "  precision = num_pairs / (num_pairs + fp)\n",
        "  recall = num_pairs / (num_pairs + fn)\n",
        "  f1 = 2 * precision * recall / (precision + recall)\n",
        "  return precision, recall, f1"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CT50qj0JJFv6",
        "outputId": "218ce4e1-f5f8-4028-edaf-5e103eec5031"
      },
      "source": [
        "final_neighbors = get_correct_preds(modes, predicted_modes, 50)\n",
        "tp = final_neighbors[0]\n",
        "tp"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6589"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjyqxV9wRopN",
        "outputId": "29326933-37c3-4d11-fa94-87de9bee4a86"
      },
      "source": [
        "get_precision_recall_f1(tp, modes, predicted_modes)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6533465542885474, 0.6589, 0.6561115260144386)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vad-Ain9R4Gq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}