IF_anomaly_detection_VAEmodel_GRU2x32_encoding16_psDR5_SDSSQSO_balanced.ipynb

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    "colab": {
      "name": "IF-anomaly-detection-VAEmodel-GRU2x32-encoding16-psDR5-SDSSQSO-balanced.ipynb",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "toXKzHH0oTgB"
      },
      "source": [
        "import datetime\n",
        "import sys\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn \n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import IsolationForest\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuW76jYod6n4",
        "outputId": "bdf635f4-48b2-4da3-d09f-dcafa8e74572"
      },
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "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"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBgc03CeeAbS"
      },
      "source": [
        "## Reading encoding features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMpCM8hCeAFi"
      },
      "source": [
        "data_train = pd.read_csv('/content/drive/MyDrive/CSAGNs/results_DR5/VAE_psDR5_SDSS_QSO_balanced_detprop_GRU2x32-encoding16_gband/DR5_balanced_training_sample_with_prederr10_encoding_features_chi2_turbofats_gband.csv')\n",
        "data_train.set_index('oid_alerce',inplace=True)\n",
        "#data = pd.read_csv('/content/drive/MyDrive/CSAGNs/results_DR5/VAE_psDR5_SDSS_QSO_balanced_detprop_GRU2x32-encoding16_gband/DR5_final_sample_with_prederr10_encoding_features_chi2_turbofats_gband.csv')\n",
        "data = pd.read_csv('/content/drive/MyDrive/CSAGNs/results_DR5/VAE_psDR5_SDSS_QSO_balanced_detprop_GRU2x32-encoding16_gband/DR5_Frederick_Ricci_sample_with_prederr10_encoding_features_chi2_gband.csv')\n",
        "data.set_index('oid',inplace=True)\n",
        "#data.set_index('oid_alerce',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "752zUEBCj1HP"
      },
      "source": [
        "#filtering data by ps_score\n",
        "#data = data[(data.chi2_gband>3)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "BK1SAAwgeblN",
        "outputId": "e2cdef06-258a-4219-a6c9-d3b81de5dbfc"
      },
      "source": [
        "#defining features for the anomaly detection algorithm \n",
        "feat_list = [\n",
        "             \"e1\", \"e2\", \"e3\", \"e4\", \"e5\", \"e6\", \"e7\", \"e8\",\"e9\", \"e10\",\"e11\",\n",
        "               \"e12\", \"e13\", \"e14\", \"e15\", \"e16\",\n",
        "             #\"chi2_gband\",\n",
        "             #\"mhps_ratio\",\"mhps_low\",\"mhps_high\",\"Amplitude\",\"Autocor_length\",\"Meanvariance\",\"GP_DRW_sigma\",\"GP_DRW_tau\",\"SF_ML_amplitude\",\"SF_ML_gamma\",\"IAR_phi\",\"LinearTrend\"\n",
        "             ]\n",
        "\n",
        "#saving features in df\n",
        "df_feat = data[feat_list]\n",
        "df_feat_train = data_train[feat_list]\n",
        "df_feat.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                       e1        e2        e3  ...       e14       e15       e16\n",
              "oid                                            ...                              \n",
              "405113200012094 -0.019421 -0.036640  1.555295  ...  0.754415 -1.031383  0.012401\n",
              "464105200007063  0.013039  0.040348 -2.174314  ... -0.264444  0.098123  0.002398\n",
              "516105400003751 -0.004812 -0.022248  0.782761  ...  1.983115  2.298911  0.036256\n",
              "629115200005576  0.005211  0.002847  0.081205  ...  2.637215 -1.224063  0.019422\n",
              "634110400007229 -0.026129 -0.045747  2.656521  ...  0.058569  0.104327  0.017647\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UVmJGVIf3cx"
      },
      "source": [
        "## Anomaly detection with Isolation forest\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnO6Fqe8f9mG"
      },
      "source": [
        "#Isolation model definition\n",
        "model_isof = IsolationForest(\n",
        "                n_estimators  = 500,\n",
        "                max_samples   ='auto',\n",
        "                contamination = 0.02,\n",
        "                n_jobs        = -1,\n",
        "                random_state  = 123,\n",
        "            )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvEL387WgCuD",
        "outputId": "551ccc36-9873-48ed-a862-6a20a2ea5641"
      },
      "source": [
        "#fitting the model\n",
        "model_isof.fit(X=df_feat_train.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest(behaviour='deprecated', bootstrap=False, contamination=0.02,\n",
              "                max_features=1.0, max_samples='auto', n_estimators=500,\n",
              "                n_jobs=-1, random_state=123, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSZunmzYgCim",
        "outputId": "5ddb3a6e-09ba-44d8-a6d7-686bc267a2fa"
      },
      "source": [
        "#predicting anomaly classification, considering contamination fraction\n",
        "pred_anomaly = model_isof.predict(X=df_feat.values)\n",
        "print(pred_anomaly)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1  1  1 -1 -1  1  1  1  1 -1  1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qoKVtUYhEEV",
        "outputId": "7624526a-b4ad-43eb-cdf3-a0592b6b9f02"
      },
      "source": [
        "#getting anomaly score\n",
        "score_anomaly = model_isof.score_samples(X=df_feat.values)\n",
        "print(score_anomaly)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-0.6105813  -0.55965611 -0.51567573 -0.58563561 -0.63976809 -0.43123427\n",
            " -0.39830504 -0.50715231 -0.52114523 -0.59504564 -0.57439741]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "zkLl2CPfhbJ0",
        "outputId": "8e3cdea4-f9c9-4bb4-d9f6-67735d002c3d"
      },
      "source": [
        "#plotting histogram of anomaly score\n",
        "plt.hist(score_anomaly,bins=30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0w5f+S1IjfKWoJDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXi/wHW3ONMlbpqMAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "dtFoUiZSibUv",
        "outputId": "53bf5cd6-b569-4f88-e496-e202736dddf2"
      },
      "source": [
        "# Plotting distribution of anomaly scores\n",
        "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 3.5))\n",
        "sns.distplot(\n",
        "    score_anomaly,\n",
        "    hist    = False,\n",
        "    rug     = True,\n",
        "    color   = 'blue',\n",
        "    kde_kws = {'shade': True, 'linewidth': 1},\n",
        "    ax      = ax\n",
        ")\n",
        "\n",
        "ax.axvline(model_isof.offset_, c='red', linestyle='--', label='cuantil 0.05')\n",
        "ax.set_xlabel('Anomaly score')\n",
        "print(model_isof.offset_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2056: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "-0.5763301566355188\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x252 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJ-kTGM8i5Nq"
      },
      "source": [
        "## Saving anomaly detection results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "hyWP81jdi8v8",
        "outputId": "ad8449b3-db5a-4efc-df02-f432ddf11fd7"
      },
      "source": [
        "df_feat['if_score'] = score_anomaly\n",
        "df_feat.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "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>e1</th>\n",
              "      <th>e2</th>\n",
              "      <th>e3</th>\n",
              "      <th>e4</th>\n",
              "      <th>e5</th>\n",
              "      <th>e6</th>\n",
              "      <th>e7</th>\n",
              "      <th>e8</th>\n",
              "      <th>e9</th>\n",
              "      <th>e10</th>\n",
              "      <th>e11</th>\n",
              "      <th>e12</th>\n",
              "      <th>e13</th>\n",
              "      <th>e14</th>\n",
              "      <th>e15</th>\n",
              "      <th>e16</th>\n",
              "      <th>if_score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oid</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>405113200012094</th>\n",
              "      <td>-0.019421</td>\n",
              "      <td>-0.036640</td>\n",
              "      <td>1.555295</td>\n",
              "      <td>1.281783</td>\n",
              "      <td>-1.968677</td>\n",
              "      <td>-0.006785</td>\n",
              "      <td>-0.034409</td>\n",
              "      <td>0.006346</td>\n",
              "      <td>-0.036378</td>\n",
              "      <td>0.034309</td>\n",
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            "text/plain": [
              "                       e1        e2        e3  ...       e15       e16  if_score\n",
              "oid                                            ...                              \n",
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              "[5 rows x 17 columns]"
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          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
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      "metadata": {
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          "height": 286
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        "id": "Ibg4WzXBjF5h",
        "outputId": "bd637e1a-836e-4b85-a797-e968bb2dbb67"
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      "source": [
        "sel_df = df_feat[df_feat.if_score<-0.54]\n",
        "sel_df.head(50)"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "                       e1        e2        e3  ...       e15       e16  if_score\n",
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          "metadata": {
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    {
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          "height": 272
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        "id": "U1d9MRcHyr6_",
        "outputId": "fc42682c-acbd-4e62-d677-580c18068c00"
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      "source": [
        "data['if_score'] = score_anomaly\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>0.782761</td>\n",
              "      <td>-0.180979</td>\n",
              "      <td>0.173506</td>\n",
              "      <td>0.012976</td>\n",
              "      <td>-0.016303</td>\n",
              "      <td>0.020130</td>\n",
              "      <td>0.005639</td>\n",
              "      <td>0.009286</td>\n",
              "      <td>-0.018461</td>\n",
              "      <td>0.024875</td>\n",
              "      <td>-0.007340</td>\n",
              "      <td>1.983115</td>\n",
              "      <td>2.298911</td>\n",
              "      <td>0.036256</td>\n",
              "      <td>-0.005318</td>\n",
              "      <td>-0.012752</td>\n",
              "      <td>-1.784626</td>\n",
              "      <td>-3.190440</td>\n",
              "      <td>-1.022637</td>\n",
              "      <td>-0.005378</td>\n",
              "      <td>0.006961</td>\n",
              "      <td>0.001216</td>\n",
              "      <td>-0.029050</td>\n",
              "      <td>0.013703</td>\n",
              "      <td>-0.026502</td>\n",
              "      <td>-0.020044</td>\n",
              "      <td>-0.007896</td>\n",
              "      <td>-2.590686</td>\n",
              "      <td>-1.527835</td>\n",
              "      <td>0.006367</td>\n",
              "      <td>-0.515676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629115200005576</th>\n",
              "      <td>3</td>\n",
              "      <td>3.435884</td>\n",
              "      <td>0.005211</td>\n",
              "      <td>0.002847</td>\n",
              "      <td>0.081205</td>\n",
              "      <td>2.559672</td>\n",
              "      <td>0.830689</td>\n",
              "      <td>0.049800</td>\n",
              "      <td>-0.053007</td>\n",
              "      <td>0.017275</td>\n",
              "      <td>-0.012348</td>\n",
              "      <td>0.007815</td>\n",
              "      <td>-0.058085</td>\n",
              "      <td>0.009515</td>\n",
              "      <td>-0.046890</td>\n",
              "      <td>2.637215</td>\n",
              "      <td>-1.224063</td>\n",
              "      <td>0.019422</td>\n",
              "      <td>0.006163</td>\n",
              "      <td>-0.030562</td>\n",
              "      <td>-2.279156</td>\n",
              "      <td>-3.221613</td>\n",
              "      <td>-1.234595</td>\n",
              "      <td>-0.018163</td>\n",
              "      <td>-0.005264</td>\n",
              "      <td>-0.010095</td>\n",
              "      <td>-0.034713</td>\n",
              "      <td>0.000614</td>\n",
              "      <td>-0.018131</td>\n",
              "      <td>-0.020311</td>\n",
              "      <td>-0.012826</td>\n",
              "      <td>-3.256318</td>\n",
              "      <td>-1.950540</td>\n",
              "      <td>-0.009690</td>\n",
              "      <td>-0.585636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>634110400007229</th>\n",
              "      <td>4</td>\n",
              "      <td>20.912769</td>\n",
              "      <td>-0.026129</td>\n",
              "      <td>-0.045747</td>\n",
              "      <td>2.656521</td>\n",
              "      <td>2.428572</td>\n",
              "      <td>-2.103093</td>\n",
              "      <td>-0.015175</td>\n",
              "      <td>-0.031090</td>\n",
              "      <td>-0.007545</td>\n",
              "      <td>-0.034387</td>\n",
              "      <td>0.037459</td>\n",
              "      <td>-0.053233</td>\n",
              "      <td>0.017111</td>\n",
              "      <td>-0.031956</td>\n",
              "      <td>0.058569</td>\n",
              "      <td>0.104327</td>\n",
              "      <td>0.017647</td>\n",
              "      <td>-0.007873</td>\n",
              "      <td>-0.068008</td>\n",
              "      <td>-3.477855</td>\n",
              "      <td>-4.640034</td>\n",
              "      <td>-2.646352</td>\n",
              "      <td>-0.042791</td>\n",
              "      <td>-0.019814</td>\n",
              "      <td>-0.018822</td>\n",
              "      <td>-0.051326</td>\n",
              "      <td>-0.012941</td>\n",
              "      <td>-0.022380</td>\n",
              "      <td>-0.037205</td>\n",
              "      <td>-0.029547</td>\n",
              "      <td>-5.061471</td>\n",
              "      <td>-3.178742</td>\n",
              "      <td>-0.021160</td>\n",
              "      <td>-0.639768</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Unnamed: 0  chi2_gband  ...  logsig_e16  if_score\n",
              "oid                                      ...                      \n",
              "405113200012094           0   13.702934  ...   -0.009109 -0.610581\n",
              "464105200007063           1   11.994704  ...   -0.022732 -0.559656\n",
              "516105400003751           2   10.672771  ...    0.006367 -0.515676\n",
              "629115200005576           3    3.435884  ...   -0.009690 -0.585636\n",
              "634110400007229           4   20.912769  ...   -0.021160 -0.639768\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9NnvpRXyvjW"
      },
      "source": [
        "#data.to_csv('/content/drive/MyDrive/CSAGNs/results_DR5/VAE_psDR5_SDSS_QSO_balanced_detprop_GRU2x32-encoding16_gband/DR5_final_sample_features_chi2_prederr10_isolation_forest_score_training_with_balqsosample_gband.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HNMM-vjI5q4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "7d0c6bd7-b422-4276-f62a-2193473078b1"
      },
      "source": [
        "fig = plt.scatter(data.if_score.values,data.n_good_det.values,c=np.log10(data.Autocor_length.values),cmap='gist_heat')\n",
        "plt.colorbar(fig)\n",
        "plt.xlabel('IF score')\n",
        "plt.ylabel('number epochs')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-d2483a5c0c02>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mif_score\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_good_det\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAutocor_length\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gist_heat'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'IF score'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'number epochs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5139\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5140\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5141\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5143\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'n_good_det'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ow1w2C605RoB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}