fsrs4anki_optimizer.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# FSRS4Anki v3.19.0 Optimizer"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "lurCmW0Jqz3s"
      },
      "source": [
        "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/v3.19.0/fsrs4anki_optimizer.ipynb)\n",
        "\n",
        "↑ Click the above button to open the optimizer on Google Colab.\n",
        "\n",
        "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "wG7bBfGJFbMr"
      },
      "source": [
        "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
        "\n",
        "No need to include media. Need to include scheduling information. \n",
        "\n",
        "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
        "\n",
        "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
        "\n",
        "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
        "\n",
        "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
        "\n",
        "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
        "\n",
        "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "iqP70_-3EUhi"
      },
      "outputs": [],
      "source": [
        "# Here are some settings that you need to replace before running this optimizer.\n",
        "\n",
        "filename = \"collection-2022-09-18@13-21-58.colpkg\"\n",
        "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
        "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
        "\n",
        "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
        "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
        "timezone = 'Asia/Shanghai'\n",
        "\n",
        "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
        "next_day_starts_at = 4\n",
        "\n",
        "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
        "revlog_start_date = \"2006-10-05\"\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "bLFVNmG2qd06"
      },
      "source": [
        "## 1 Build dataset"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "EkzFeKawqgbs"
      },
      "source": [
        "### 1.1 Extract Anki collection & deck file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KD2js_wEr_Bs",
        "outputId": "42653d9e-316e-40bc-bd1d-f3a0e2b246c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Deck file extracted successfully!\n"
          ]
        }
      ],
      "source": [
        "%pip install -q 'fsrs4anki_optimizer @ git+https://github.com/open-spaced-repetition/fsrs4anki@v3.19.0#subdirectory=package'\n",
        "# for local development\n",
        "# import os\n",
        "# import sys\n",
        "# sys.path.insert(0, os.path.abspath('./package/fsrs4anki_optimizer'))\n",
        "import fsrs4anki_optimizer as optimizer\n",
        "optimizer = optimizer.Optimizer()\n",
        "optimizer.anki_extract(filename)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "dKpy4VfqGmaL"
      },
      "source": [
        "### 1.2 Create time-series feature & analysis\n",
        "\n",
        "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
        "\n",
        "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
        "\n",
        "Then it will generate a concise analysis for your review logs. \n",
        "\n",
        "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
        "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
        "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
        "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
        "- The `factor` is `stability / previous stability`.\n",
        "- The `group_cnt` is the number of review logs that have the same `r_history`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2IIaY3PDaaG",
        "outputId": "607916c9-da95-48dd-fdab-6bd83fbbbb40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "revlog.csv saved.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Trainset saved.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "04a732bf0ecc4389bdd7ae2c88b81ea3",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retention calculated.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c0aaa6b02c4d40d0a5667d4b40b71dd3",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Stability calculated.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b31434b437f544ee800a90a8956f7598",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1:again, 2:hard, 3:good, 4:easy\n",
            "\n",
            "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
            "              1           1.7          0.765        1.0     inf       7997\n",
            "            1,3           3.9          0.876        4.2    4.20       4176\n",
            "          1,3,3           8.6          0.883        9.2    2.19       2711\n",
            "        1,3,3,3          18.2          0.858       14.0    1.52       1616\n",
            "      1,3,3,3,3          37.5          0.835       23.2    1.66        822\n",
            "    1,3,3,3,3,3          78.8          0.850       35.6    1.53        384\n",
            "  1,3,3,3,3,3,3         122.3          0.903       39.3    1.10        171\n",
            "              2           1.0          0.901        1.1     inf        240\n",
            "            2,3           3.5          0.946        8.3    7.55        201\n",
            "          2,3,3          11.1          0.890        7.1    0.86        160\n",
            "              3           1.5          0.962        5.4     inf       9134\n",
            "            3,3           3.9          0.966       15.2    2.81       6589\n",
            "          3,3,3           9.0          0.960       23.7    1.56       5162\n",
            "        3,3,3,3          19.4          0.942       44.2    1.86       3519\n",
            "      3,3,3,3,3          39.1          0.926       54.5    1.23       1922\n",
            "    3,3,3,3,3,3          76.6          0.930      106.8    1.96       1074\n",
            "  3,3,3,3,3,3,3         118.7          0.949      155.3    1.45        480\n",
            "3,3,3,3,3,3,3,3         131.1          0.970      617.2    3.97        100\n",
            "              4           3.8          0.966       12.1     inf      11599\n",
            "            4,3           8.1          0.975       38.9    3.21       7517\n",
            "          4,3,3          18.0          0.963       56.8    1.46       5303\n",
            "        4,3,3,3          33.3          0.952       84.3    1.48       3012\n",
            "      4,3,3,3,3          48.3          0.953      128.3    1.52       1353\n",
            "    4,3,3,3,3,3          67.3          0.958      112.9    0.88        496\n",
            "  4,3,3,3,3,3,3          77.6          0.978      113.2    1.00        244\n",
            "4,3,3,3,3,3,3,3         112.9          0.984      177.8    1.57        168\n",
            "Analysis saved!\n"
          ]
        }
      ],
      "source": [
        "optimizer.create_time_series(timezone, revlog_start_date, next_day_starts_at)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "k_SgzC-auWmu"
      },
      "source": [
        "## 2 Optimize parameter"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "WrfBJjqCHEwJ"
      },
      "source": [
        "### 2.1 Define the model\n",
        "\n",
        "FSRS is a time-series model for predicting memory states."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "tdYp3GMLhTYm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1, 1, 5, -0.5, -0.5, 0.2, 1.4, -0.12, 0.8, 2, -0.2, 0.2, 1]\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "w[0]: initial_stability_for_again_answer\n",
        "w[1]: initial_stability_step_per_rating\n",
        "w[2]: initial_difficulty_for_good_answer\n",
        "w[3]: initial_difficulty_step_per_rating\n",
        "w[4]: next_difficulty_step_per_rating\n",
        "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
        "w[6]: next_stability_factor_after_success\n",
        "w[7]: next_stability_stabilization_decay_after_success\n",
        "w[8]: next_stability_retrievability_gain_after_success\n",
        "w[9]: next_stability_factor_after_failure\n",
        "w[10]: next_stability_difficulty_decay_after_success\n",
        "w[11]: next_stability_stability_gain_after_failure\n",
        "w[12]: next_stability_retrievability_gain_after_failure\n",
        "For more details about the parameters, please see: \n",
        "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
        "\"\"\"\n",
        "optimizer.define_model()\n",
        "print(optimizer.init_w)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "8E1dYfgQLZAC"
      },
      "source": [
        "### 2.2 Train the model\n",
        "\n",
        "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jht0gneShowU",
        "outputId": "aaa72b79-b454-483b-d746-df1a353b2c8f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ff2c360cf384404b92d00faa71934ff",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/223795 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tensorized!\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e517189d5d954a039f7621f6e95312dc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pre-train:   0%|          | 0/28970 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "w: [1.0185, 2.2626, 5.0, -0.5, -0.5, 0.2, 1.4, -0.12, 0.8, 2.0, -0.2, 0.2, 1.0]\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "80c5f559fdea475b98ab82c66e0416fd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "train:   0%|          | 0/194825 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "iteration: 1\n",
            "w: [1.0187, 2.2627, 4.9984, -0.5016, -0.4984, 0.2016, 1.4016, -0.1184, 0.8016, 2.0016, -0.1984, 0.2016, 1.0016]\n",
            "iteration: 19483\n",
            "w: [1.0203, 2.2632, 5.1648, -0.9074, -0.7747, 0.014, 1.3543, -0.0597, 0.7435, 1.8698, -0.3399, 0.4151, 0.8722]\n",
            "iteration: 38965\n",
            "w: [1.0203, 2.2632, 5.2586, -1.0906, -0.9228, 0.0148, 1.3227, -0.0655, 0.7095, 1.8229, -0.3864, 0.4873, 0.892]\n",
            "iteration: 58447\n",
            "w: [1.0203, 2.2632, 5.1696, -1.132, -0.8883, 0.0085, 1.3962, -0.01, 0.7753, 1.7884, -0.4183, 0.5274, 0.8805]\n",
            "iteration: 77929\n",
            "w: [1.0203, 2.2632, 5.1991, -1.142, -0.9768, 0.0075, 1.3677, -0.0319, 0.7445, 1.7792, -0.4226, 0.5996, 0.9057]\n",
            "iteration: 97411\n",
            "w: [1.0203, 2.2632, 5.1672, -1.101, -0.9791, 0.0004, 1.3694, -0.0311, 0.7434, 1.7485, -0.4489, 0.5889, 0.896]\n",
            "iteration: 116893\n",
            "w: [1.0203, 2.2632, 5.1934, -1.1402, -1.0148, 0.0117, 1.3552, -0.0391, 0.7267, 1.7593, -0.4366, 0.6027, 0.8885]\n",
            "iteration: 136375\n",
            "w: [1.0203, 2.2632, 5.1822, -1.1358, -1.0328, 0.0023, 1.3619, -0.0359, 0.7315, 1.7438, -0.451, 0.5927, 0.8798]\n",
            "iteration: 155857\n",
            "w: [1.0203, 2.2632, 5.1784, -1.139, -1.029, 0.0104, 1.3647, -0.0276, 0.7335, 1.7421, -0.4518, 0.6041, 0.881]\n",
            "iteration: 175339\n",
            "w: [1.0203, 2.2632, 5.1753, -1.1368, -1.031, 0.0099, 1.3672, -0.0266, 0.7358, 1.7395, -0.4539, 0.6013, 0.8789]\n",
            "iteration: 194821\n",
            "w: [1.0203, 2.2632, 5.1758, -1.1364, -1.0317, 0.0095, 1.3663, -0.0273, 0.7349, 1.7395, -0.4539, 0.6014, 0.8789]\n",
            "\n",
            "Training finished!\n"
          ]
        }
      ],
      "source": [
        "optimizer.train()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "BZ4S2l7BWfzr"
      },
      "source": [
        "### 2.3 Result\n",
        "\n",
        "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTnPSDA2QpUu",
        "outputId": "49f487b9-69a7-4e96-b35a-7e027f478fbd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1.0203, 2.2632, 5.1758, -1.1364, -1.0317, 0.0095, 1.3663, -0.0273, 0.7349, 1.7395, -0.4539, 0.6014, 0.8789]\n"
          ]
        }
      ],
      "source": [
        "print(optimizer.w)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2.4 Preview"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "I_zsoDyTaTrT"
      },
      "source": [
        "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iws4rtP1WKBT",
        "outputId": "890d0287-1a17-4c59-fbbf-ee54d79cd383"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1:again, 2:hard, 3:good, 4:easy\n",
            "\n",
            "first rating: 1\n",
            "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
            "interval history: 0,1,2,4,8,17,34,68,135,266,521\n",
            "difficulty history: 0,7.4,7.4,7.4,7.4,7.4,7.3,7.3,7.3,7.3,7.3\n",
            "\n",
            "first rating: 2\n",
            "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
            "interval history: 0,3,7,17,39,89,200,446,984,2147,4636\n",
            "difficulty history: 0,6.3,6.3,6.3,6.3,6.3,6.3,6.2,6.2,6.2,6.2\n",
            "\n",
            "first rating: 3\n",
            "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
            "interval history: 0,6,16,41,106,268,669,1644,3983,9514,22416\n",
            "difficulty history: 0,5.2,5.2,5.2,5.2,5.2,5.2,5.2,5.2,5.2,5.2\n",
            "\n",
            "first rating: 4\n",
            "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
            "interval history: 0,8,24,69,197,549,1504,4044,10682,27732,70797\n",
            "difficulty history: 0,4.0,4.1,4.1,4.1,4.1,4.1,4.1,4.1,4.1,4.1\n",
            "\n"
          ]
        }
      ],
      "source": [
        "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
        "\n",
        "optimizer.preview(requestRetention)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(tensor(5.5467), tensor(5.1758))\n",
            "(tensor(15.5041), tensor(5.1758))\n",
            "(tensor(41.3439), tensor(5.1758))\n",
            "(tensor(105.8517), tensor(5.1758))\n",
            "(tensor(268.3760), tensor(5.1758))\n",
            "(tensor(22.3620), tensor(7.2196))\n",
            "(tensor(4.4792), tensor(9.2440))\n",
            "(tensor(6.5431), tensor(9.2053))\n",
            "(tensor(10.1834), tensor(9.1671))\n",
            "(tensor(15.4353), tensor(9.1291))\n",
            "(tensor(23.3819), tensor(9.0916))\n",
            "(tensor(35.6618), tensor(9.0544))\n",
            "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
            "interval history: 0,6,16,41,106,268,22,4,7,10,15,23,36\n",
            "difficulty history: 0,5.2,5.2,5.2,5.2,5.2,7.2,9.2,9.2,9.2,9.1,9.1,9.1\n"
          ]
        }
      ],
      "source": [
        "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
        "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
        "easyBonus = 1.3\n",
        "hardInterval = 1.2\n",
        "\n",
        "optimizer.preview_sequence(test_rating_sequence, requestRetention, easyBonus, hardInterval)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2.5 Predict memory states and distribution of difficulty\n",
        "\n",
        "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
        "\n",
        "Meanwhile, it will count the distribution of difficulty."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c826a31d674481d8c6ac2e6c7ece6ab",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/117599 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "prediction.tsv saved.\n",
            "difficulty\n",
            "1     0.001850\n",
            "2     0.010956\n",
            "3     0.039505\n",
            "4     0.154588\n",
            "5     0.161130\n",
            "6     0.066244\n",
            "7     0.132523\n",
            "8     0.060573\n",
            "9     0.088094\n",
            "10    0.284537\n",
            "Name: count, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "optimizer.predict_memory_states()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3 Optimize retention to minimize the time of reviews\n",
        "\n",
        "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
        "\n",
        "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "average time for failed cards: 25.0s\n",
            "average time for recalled cards: 8.0s\n",
            "terminal stability:  100.18\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7beefc32ac4649b29325bc30edecb4a8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/15 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "expected_time.csv saved.\n",
            "\n",
            "-----suggested retention (experimental): 0.84-----\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "optimizer.find_optimal_retention()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4 Evaluate the model"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 4.1 Loss\n",
        "\n",
        "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
        "\n",
        "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "802d5274f61046efab84c1b283bb71c4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/223795 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss before training: 0.3403\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "31fb18d40bd4433fae9427bafca81fe5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/223795 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss after training: 0.3163\n"
          ]
        }
      ],
      "source": [
        "optimizer.evaluate()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 4.2 Calibration graph\n",
        "\n",
        "1. FSRS predicts the stability and retention for each review.\n",
        "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
        "3. Count the true retention of each bin.\n",
        "4. Plot (predicted retention, true retention) in the line graph.\n",
        "5. Plot (predicted retention, size of bin) in the bar graph.\n",
        "6. Combine these graphs to create the calibration graph.\n",
        "\n",
        "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
        "\n",
        "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
        "\n",
        "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R-squared: 0.8934\n",
            "RMSE: 0.0207\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "optimizer.calibration_graph()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyMnk8/Ih2JAJZJ1PBkXQUBC",
      "collapsed_sections": [],
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "dl",
      "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": "3.8.13"
    },
    "vscode": {
      "interpreter": {
        "hash": "46e27ede752268be201d36b7fbc2802b29a11b0bb095abacecc6c0428b93624a"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}