{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "977a48a109b443f3b078b1af334f90bf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_6768da55292f4b12aa256cfd0db17cc3",
              "IPY_MODEL_354d5d63242f4ee5a3781317970da845",
              "IPY_MODEL_5f88666f6435479aae49b1fe7459a9c1"
            ],
            "layout": "IPY_MODEL_e678d4e5bf3a4354bb13a83724ee93a7"
          }
        },
        "6768da55292f4b12aa256cfd0db17cc3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ff0e13e864414d56b952196bf34d0754",
            "placeholder": "​",
            "style": "IPY_MODEL_ccb2991b94bc4af2bd0cad8cd09fa4ce",
            "value": "Downloading data: 100%"
          }
        },
        "354d5d63242f4ee5a3781317970da845": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e676a01c7aa748fe9259b9bf9a5221c6",
            "max": 17043993,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e160b8dc3736476c96f74af86b52a6b0",
            "value": 17043993
          }
        },
        "5f88666f6435479aae49b1fe7459a9c1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2fd7d9e9510343e9b41003b2f5fdb8a2",
            "placeholder": "​",
            "style": "IPY_MODEL_c19241d62b4d4b62b7bb87356f3b347b",
            "value": " 17.0M/17.0M [00:00<00:00, 11.8MB/s]"
          }
        },
        "e678d4e5bf3a4354bb13a83724ee93a7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ff0e13e864414d56b952196bf34d0754": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ccb2991b94bc4af2bd0cad8cd09fa4ce": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e676a01c7aa748fe9259b9bf9a5221c6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e160b8dc3736476c96f74af86b52a6b0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "2fd7d9e9510343e9b41003b2f5fdb8a2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c19241d62b4d4b62b7bb87356f3b347b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4d374753b37944159c55668a7df62c61": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_fd744661d0644d758360059748b9e3e8",
              "IPY_MODEL_3ed1011a3c93403ca2f5d3e72d34d72b",
              "IPY_MODEL_1d480c61b4b645a294160b8eeafbbad8"
            ],
            "layout": "IPY_MODEL_c323b7d1570f404fb5471c6d85f17e29"
          }
        },
        "fd744661d0644d758360059748b9e3e8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_51b67df390104effabc0aa65ffcd5f16",
            "placeholder": "​",
            "style": "IPY_MODEL_3d08b925d38b43028dc89f946a899868",
            "value": "Downloading data: 100%"
          }
        },
        "3ed1011a3c93403ca2f5d3e72d34d72b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1dd275a6ae5b4a1c95402ae7b0801cf2",
            "max": 4274359,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_9998e68804464bb8af6c0bc40d3124ff",
            "value": 4274359
          }
        },
        "1d480c61b4b645a294160b8eeafbbad8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ca1ed947d7c342fba636f99d7751491c",
            "placeholder": "​",
            "style": "IPY_MODEL_7cffde51fc21483baf73d8266b9ad992",
            "value": " 4.27M/4.27M [00:00<00:00, 15.4MB/s]"
          }
        },
        "c323b7d1570f404fb5471c6d85f17e29": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "51b67df390104effabc0aa65ffcd5f16": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3d08b925d38b43028dc89f946a899868": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "1dd275a6ae5b4a1c95402ae7b0801cf2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9998e68804464bb8af6c0bc40d3124ff": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "ca1ed947d7c342fba636f99d7751491c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7cffde51fc21483baf73d8266b9ad992": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "42a76c8530d14ac4b703d29be80a5adc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_5618cda78e34474dbb19a3e305295a82",
              "IPY_MODEL_2429ed37cfc24cf9890a9fc96a22270f",
              "IPY_MODEL_13e9c43b8c4649749de7d01accab256d"
            ],
            "layout": "IPY_MODEL_81e1789b0c0f42a196b4391918867366"
          }
        },
        "5618cda78e34474dbb19a3e305295a82": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6779e0265e9445bab4d866c4b407daed",
            "placeholder": "​",
            "style": "IPY_MODEL_9e3ae90630e642088853aaafe18db7a0",
            "value": "Generating train split: 100%"
          }
        },
        "2429ed37cfc24cf9890a9fc96a22270f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5afa2000583b4b4686127d47262a197e",
            "max": 863,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_46de9b0af80a4c78aa85a7440c3fabf1",
            "value": 863
          }
        },
        "13e9c43b8c4649749de7d01accab256d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ebd95d978fef421da8aa466b3c12e559",
            "placeholder": "​",
            "style": "IPY_MODEL_11e48a2606a244a796ac9e4f50117042",
            "value": " 863/863 [00:00<00:00, 1632.79 examples/s]"
          }
        },
        "81e1789b0c0f42a196b4391918867366": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6779e0265e9445bab4d866c4b407daed": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9e3ae90630e642088853aaafe18db7a0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5afa2000583b4b4686127d47262a197e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "46de9b0af80a4c78aa85a7440c3fabf1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "ebd95d978fef421da8aa466b3c12e559": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "11e48a2606a244a796ac9e4f50117042": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e3e2a3fb44264c82bc678394d4e64e4c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_4e990bac6c5940539dece669051e9925",
              "IPY_MODEL_399be7a9f9554349ba07267db1b06000",
              "IPY_MODEL_c57334b858a44649b75024bf8488a312"
            ],
            "layout": "IPY_MODEL_1f966e33624a4ef9b5bced1d8b0dc895"
          }
        },
        "4e990bac6c5940539dece669051e9925": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c5be0c2bb45c4107948d6a222e97db34",
            "placeholder": "​",
            "style": "IPY_MODEL_bae51ff1adf94765ac3db50717c24957",
            "value": "Generating test split: 100%"
          }
        },
        "399be7a9f9554349ba07267db1b06000": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_266e2a2db5a74478af6145a7d57c7aeb",
            "max": 216,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_11a0a9cfba8f4531b07485982bff0c49",
            "value": 216
          }
        },
        "c57334b858a44649b75024bf8488a312": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cf1b9722501e4d48a6292e6d8e1c2245",
            "placeholder": "​",
            "style": "IPY_MODEL_4de5e8a1104244a3a66a983abaa71659",
            "value": " 216/216 [00:00<00:00, 1441.72 examples/s]"
          }
        },
        "1f966e33624a4ef9b5bced1d8b0dc895": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c5be0c2bb45c4107948d6a222e97db34": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bae51ff1adf94765ac3db50717c24957": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "266e2a2db5a74478af6145a7d57c7aeb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "11a0a9cfba8f4531b07485982bff0c49": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "cf1b9722501e4d48a6292e6d8e1c2245": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4de5e8a1104244a3a66a983abaa71659": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Get data from HuggingFace"
      ],
      "metadata": {
        "id": "K8U5Lhdh-KKQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IzfzBH_y-FKs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95e72e0c-d37a-45b1-8289-becd0dcd46ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.19.0-py3-none-any.whl (542 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (14.0.2)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n",
            "Collecting huggingface-hub>=0.21.2 (from datasets)\n",
            "  Downloading huggingface_hub-0.22.2-py3-none-any.whl (388 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, dill, multiprocess, huggingface-hub, datasets\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "Successfully installed datasets-2.19.0 dill-0.3.8 huggingface-hub-0.22.2 multiprocess-0.70.16 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from google.colab import userdata\n",
        "userdata.get('HF_TOKEN')\n",
        "\n",
        "iNat = load_dataset('ba188/iNaturalist_v2')"
      ],
      "metadata": {
        "id": "ogoytut1-h0S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "977a48a109b443f3b078b1af334f90bf",
            "6768da55292f4b12aa256cfd0db17cc3",
            "354d5d63242f4ee5a3781317970da845",
            "5f88666f6435479aae49b1fe7459a9c1",
            "e678d4e5bf3a4354bb13a83724ee93a7",
            "ff0e13e864414d56b952196bf34d0754",
            "ccb2991b94bc4af2bd0cad8cd09fa4ce",
            "e676a01c7aa748fe9259b9bf9a5221c6",
            "e160b8dc3736476c96f74af86b52a6b0",
            "2fd7d9e9510343e9b41003b2f5fdb8a2",
            "c19241d62b4d4b62b7bb87356f3b347b",
            "4d374753b37944159c55668a7df62c61",
            "fd744661d0644d758360059748b9e3e8",
            "3ed1011a3c93403ca2f5d3e72d34d72b",
            "1d480c61b4b645a294160b8eeafbbad8",
            "c323b7d1570f404fb5471c6d85f17e29",
            "51b67df390104effabc0aa65ffcd5f16",
            "3d08b925d38b43028dc89f946a899868",
            "1dd275a6ae5b4a1c95402ae7b0801cf2",
            "9998e68804464bb8af6c0bc40d3124ff",
            "ca1ed947d7c342fba636f99d7751491c",
            "7cffde51fc21483baf73d8266b9ad992",
            "42a76c8530d14ac4b703d29be80a5adc",
            "5618cda78e34474dbb19a3e305295a82",
            "2429ed37cfc24cf9890a9fc96a22270f",
            "13e9c43b8c4649749de7d01accab256d",
            "81e1789b0c0f42a196b4391918867366",
            "6779e0265e9445bab4d866c4b407daed",
            "9e3ae90630e642088853aaafe18db7a0",
            "5afa2000583b4b4686127d47262a197e",
            "46de9b0af80a4c78aa85a7440c3fabf1",
            "ebd95d978fef421da8aa466b3c12e559",
            "11e48a2606a244a796ac9e4f50117042",
            "e3e2a3fb44264c82bc678394d4e64e4c",
            "4e990bac6c5940539dece669051e9925",
            "399be7a9f9554349ba07267db1b06000",
            "c57334b858a44649b75024bf8488a312",
            "1f966e33624a4ef9b5bced1d8b0dc895",
            "c5be0c2bb45c4107948d6a222e97db34",
            "bae51ff1adf94765ac3db50717c24957",
            "266e2a2db5a74478af6145a7d57c7aeb",
            "11a0a9cfba8f4531b07485982bff0c49",
            "cf1b9722501e4d48a6292e6d8e1c2245",
            "4de5e8a1104244a3a66a983abaa71659"
          ]
        },
        "outputId": "efbfe5bd-ba62-442f-fb04-0e4cada273fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/17.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "977a48a109b443f3b078b1af334f90bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/4.27M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d374753b37944159c55668a7df62c61"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/863 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42a76c8530d14ac4b703d29be80a5adc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/216 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3e2a3fb44264c82bc678394d4e64e4c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "np.array(list(iNat['train']['embedding'][0].split(',')))\n",
        "\n",
        "inat_train = pd.DataFrame(iNat['train'])\n",
        "inat_train['embedding'] = [np.array(list(x.split(','))) for x in inat_train['embedding']]\n",
        "\n",
        "inat_test = pd.DataFrame(iNat['test'])\n",
        "inat_test['embedding'] = [np.array(list(x.split(','))) for x in inat_test['embedding']]"
      ],
      "metadata": {
        "id": "PUAz9C3hyJ_n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inat_train['embedding'][1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vKIPpufZAt4",
        "outputId": "6cd59e4a-b121-436b-901b-ae88347c27cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2048,)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define Goal and Create/Train Model(s)\n",
        "\n",
        "Originally, wanted to try to replicate their model that can classify at the species level but because of API constraints and filtering, only have 1,079 observations with 413 unique species represented so may change the goal to be classifying at a higher taxonomic level (plant/animal/fungi/aves).\n"
      ],
      "metadata": {
        "id": "UTpQxFlh-Syt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def count_unique_strings(input_list):\n",
        "    # Convert the list to a set to remove duplicates\n",
        "    unique_strings = set(input_list)\n",
        "\n",
        "    # Return the number of unique strings\n",
        "    return len(unique_strings)\n",
        "\n",
        "count_unique_strings(inat_train['species_guess'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROeBRg6KPwpG",
        "outputId": "1e374d33-8b8f-4c54-9f64-f121b7ad9032"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "413"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from operator import le\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "7sVQ0dj9TD01"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Try species-level classification"
      ],
      "metadata": {
        "id": "Xq8HtRYLbwEM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inat_train = inat_train.dropna(subset=['location'])\n",
        "inat_test = inat_test.dropna(subset=['location'])\n",
        "\n",
        "# try to do species-level classification\n",
        "X_train = inat_train['embedding']\n",
        "X_train_array = np.array(X_train.tolist())\n",
        "X_train_numeric = pd.to_numeric(X_train_array.flatten())\n",
        "X_train_numeric = X_train_numeric.reshape(X_train_array.shape)\n",
        "\n",
        "X_test = inat_test['embedding']\n",
        "X_test_array = np.array(X_test.tolist())\n",
        "X_test_numeric = pd.to_numeric(X_test_array.flatten())\n",
        "X_test_numeric = X_test_numeric.reshape(X_test_array.shape)\n",
        "\n",
        "# encode all possible ys\n",
        "y_train_species = inat_train['species_guess'].to_numpy()\n",
        "y_test_species = inat_test['species_guess'].to_numpy()\n",
        "\n",
        "ys_species = y_train_species.tolist() + y_test_species.tolist()\n",
        "ys_species = list(set(ys_species))\n",
        "le = LabelEncoder()\n",
        "le.fit(ys_species)\n",
        "ys_species_encoded = le.transform(ys_species)\n",
        "\n",
        "# split back into train and test by matching indices\n",
        "y_train_species = [ys_species_encoded[ys_species.index(label)] for label in y_train_species]\n",
        "y_test_species = [ys_species_encoded[ys_species.index(label)] for label in y_test_species]"
      ],
      "metadata": {
        "id": "LR06rUNfT38t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ResNet50 Embedding + Logistic Regression\n"
      ],
      "metadata": {
        "id": "VYiicUHhbzVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ResNet50 Embedding + Logistic Regression\n",
        "# train logistic regression model\n",
        "logreg_embed_species = LogisticRegression(max_iter=1000, multi_class='multinomial', solver='lbfgs', random_state=42)\n",
        "logreg_embed_species.fit(X_train_numeric, y_train_species)\n",
        "\n",
        "# evaluate accuracy\n",
        "y_pred_train = logreg_embed_species.predict(X_train_numeric)\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_species, y_pred_train))\n",
        "\n",
        "y_pred_test = logreg_embed_species.predict(X_test_numeric)\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_species, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o-lOWPsQbr9A",
        "outputId": "adf1c5c4-c548-4e60-edea-dcbfe270c279"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.9456906729634003\n",
            "Accuracy on testing data: 0.7242990654205608\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test.iloc[2]['photo'])\n",
        "print(inat_test.iloc[2]['species_guess'])\n",
        "print(le.inverse_transform(logreg_embed_species.predict(X_test_numeric[2].reshape(1,-1))))\n",
        "print(le.inverse_transform(y_test_species[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lkNcYjqrCeY",
        "outputId": "8a7c03fc-6ef4-49bf-b940-38c0b36d2fb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://inaturalist-open-data.s3.amazonaws.com/photos/33149746/square.jpg\n",
            "Simulium\n",
            "['Simulium']\n",
            "['Simulium']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ResNet50 Embedding + Random Forest Classifier"
      ],
      "metadata": {
        "id": "OTRv1ZSPb2c5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest\n",
        "# create and train a random forest model\n",
        "rf_embed_species = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_embed_species.fit(X_train_numeric, y_train_species)\n",
        "\n",
        "# evaluate accuracy\n",
        "y_pred_train = rf_embed_species.predict(X_train_numeric)\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_species, y_pred_train))\n",
        "\n",
        "y_pred_test = rf_embed_species.predict(X_test_numeric)\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_species, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NHSV8WCdAZt",
        "outputId": "a6eba1f1-41ee-4d2a-c904-3205c8cbfec3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.9456906729634003\n",
            "Accuracy on testing data: 0.719626168224299\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test.iloc[2]['photo'])\n",
        "print(inat_test.iloc[2]['species_guess'])\n",
        "print(le.inverse_transform(rf_embed_species.predict(X_test_numeric[2].reshape(1,-1))))\n",
        "print(le.inverse_transform(y_test_species[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s19oUIw9qp6J",
        "outputId": "d255a00e-c4df-4798-84e5-03d2db8feb14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://inaturalist-open-data.s3.amazonaws.com/photos/33149746/square.jpg\n",
            "Simulium\n",
            "['Simulium']\n",
            "['Simulium']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Kingdom-level Classification"
      ],
      "metadata": {
        "id": "Wuo9bQcAb76O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# try to do kingdom-level classification\n",
        "# encode all possible ys\n",
        "y_train_taxa = inat_train['taxon'].to_numpy()\n",
        "y_test_taxa = inat_test['taxon'].to_numpy()\n",
        "\n",
        "ys_taxa = y_train_taxa.tolist() + y_test_taxa.tolist()\n",
        "ys_taxa = list(set(ys_taxa))\n",
        "le = LabelEncoder()\n",
        "le.fit(ys_taxa)\n",
        "ys_taxa_encoded = le.transform(ys_taxa)\n",
        "\n",
        "y_train_taxa = [ys_taxa_encoded[ys_taxa.index(label)] for label in y_train_taxa]\n",
        "y_test_taxa = [ys_taxa_encoded[ys_taxa.index(label)] for label in y_test_taxa]"
      ],
      "metadata": {
        "id": "A7-p_cyBc7dT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ResNet50 Embedding + Logistic Regression"
      ],
      "metadata": {
        "id": "nde3UKctb_AP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ResNet50 Embedding + Logistic Regression\n",
        "# train logistic regression model\n",
        "logreg_embed_taxa = LogisticRegression(max_iter=1000, multi_class='multinomial', solver='lbfgs', random_state=42)\n",
        "logreg_embed_taxa.fit(X_train_numeric, y_train_taxa)\n",
        "\n",
        "# evaluate accuracy\n",
        "y_pred_train = logreg_embed_taxa.predict(X_train_numeric)\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_taxa, y_pred_train))\n",
        "\n",
        "y_pred_test = logreg_embed_taxa.predict(X_test_numeric)\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_taxa, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrIJyOVrivW0",
        "outputId": "7738f24f-0d83-42ab-dea2-f0ab2e1789c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 1.0\n",
            "Accuracy on testing data: 0.9813084112149533\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test.iloc[2]['photo'])\n",
        "print(inat_test.iloc[2]['taxon'])\n",
        "print(le.inverse_transform(logreg_embed_taxa.predict(X_test_numeric[2].reshape(1,-1))))\n",
        "print(le.inverse_transform(y_test_taxa[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CxmwmF26vDES",
        "outputId": "03f4238f-3a49-4ada-e2bc-23bd6b10af7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://inaturalist-open-data.s3.amazonaws.com/photos/33149746/square.jpg\n",
            "Insecta\n",
            "['Insecta']\n",
            "['Insecta']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ResNet50 Embedding + Random Forest Classifier"
      ],
      "metadata": {
        "id": "V_COx4kHcIZC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest\n",
        "# create and train a random forest model\n",
        "rf_embed_taxa = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_embed_taxa.fit(X_train_numeric, y_train_taxa)\n",
        "\n",
        "# evaluate accuracy\n",
        "y_pred_train = rf_embed_taxa.predict(X_train_numeric)\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_taxa, y_pred_train))\n",
        "\n",
        "y_pred_test = rf_embed_taxa.predict(X_test_numeric)\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_taxa, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YhVg9SO6iwhW",
        "outputId": "fdb7331c-a352-4dd4-94b6-58fbd6ba1528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 1.0\n",
            "Accuracy on testing data: 0.9626168224299065\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test.iloc[2]['taxon'])\n",
        "print(le.inverse_transform(rf_embed_taxa.predict(X_test_numeric[2].reshape(1,-1))))\n",
        "print(le.inverse_transform(y_test_taxa[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QP7q9_GKvbtP",
        "outputId": "a0c9f53b-f910-411e-bb0d-360d40a389f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Insecta\n",
            "['Insecta']\n",
            "['Insecta']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Improve Model? - Weight by location\n"
      ],
      "metadata": {
        "id": "KaMFnhH-0eYq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Location Model(s)\n"
      ],
      "metadata": {
        "id": "bHeM2-2KsTrX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inat_train = inat_train.dropna(subset=['location'])\n",
        "inat_test = inat_test.dropna(subset=['location'])\n",
        "\n",
        "# reformat locations\n",
        "locX_train = inat_train['location'].str.split(',', expand=True)\n",
        "locX_train.columns = ['latitude', 'longitude']\n",
        "locX_train['latitude'] = pd.to_numeric(locX_train['latitude'])\n",
        "locX_train['longitude'] = pd.to_numeric(locX_train['longitude'])\n",
        "\n",
        "locX_test = inat_test['location'].str.split(',', expand=True)\n",
        "locX_test.columns = ['latitude', 'longitude']\n",
        "locX_test['latitude'] = pd.to_numeric(locX_test['latitude'])\n",
        "locX_test['longitude'] = pd.to_numeric(locX_test['longitude'])\n"
      ],
      "metadata": {
        "id": "Y-RIQHaLGx-L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Species-level classification"
      ],
      "metadata": {
        "id": "09qhXjtTNL2j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Logistic Regression\n"
      ],
      "metadata": {
        "id": "jyhkrJtLwWP2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# logistic regression model based on locations\n",
        "logreg_location_species = LogisticRegression(max_iter=100000, multi_class='multinomial', solver='lbfgs', random_state=42)\n",
        "logreg_location_species.fit(locX_train, y_train_species)\n",
        "\n",
        "# evaluate accuracy\n",
        "y_pred_train = logreg_location_species.predict(locX_train)\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_species, y_pred_train))\n",
        "\n",
        "y_pred_test = logreg_location_species.predict(locX_test)\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_species, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7QnEd00lGha",
        "outputId": "12082829-cab2-4a24-c42d-ca48c54d02e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.2632821723730815\n",
            "Accuracy on testing data: 0.17289719626168223\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test.iloc[2]['species_guess'])\n",
        "print(le.inverse_transform(logreg_location_species.predict(locX_test.iloc[2].values.reshape(1,-1))))\n",
        "print(le.inverse_transform(y_test_species[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfZpDIRYw-nH",
        "outputId": "406be36a-ac6c-4704-c429-f5edaddf1299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Simulium\n",
            "['Limnephilus']\n",
            "['Simulium']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Forest"
      ],
      "metadata": {
        "id": "6syO3cOYwZKP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random forest model based on locations\n",
        "rf_location_species = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_location_species.fit(locX_train, y_train_species)\n",
        "\n",
        "# evaluate accuracy\n",
        "y_pred_train = rf_location_species.predict(locX_train)\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_species, y_pred_train))\n",
        "\n",
        "y_pred_test = rf_location_species.predict(locX_test)\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_species, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaDlY2gtmavq",
        "outputId": "8ea90d5e-074a-4a7c-c3ed-20eaca1640bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.910271546635183\n",
            "Accuracy on testing data: 0.6915887850467289\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test.iloc[2]['species_guess'])\n",
        "print(le.inverse_transform(rf_location_species.predict(locX_test.iloc[2].values.reshape(1,-1))))\n",
        "print(le.inverse_transform(y_test_species[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lGInd8gxGf6",
        "outputId": "002eeac4-8bea-45ca-d9ae-9caef443a23d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Simulium\n",
            "['Simulium']\n",
            "['Simulium']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Kindgom-level classification\n"
      ],
      "metadata": {
        "id": "C9zkSgtjNQR1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Logistic Regression"
      ],
      "metadata": {
        "id": "LgqTGoLbwf8i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# logistic regression model based on locations\n",
        "logreg_location_taxa = LogisticRegression(max_iter=10000, multi_class='multinomial', solver='lbfgs', random_state=42)\n",
        "logreg_location_taxa.fit(locX_train, y_train_taxa)\n",
        "\n",
        "# evaluate accuracy\n",
        "y_pred_train = logreg_location_taxa.predict(locX_train)\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_taxa, y_pred_train))\n",
        "\n",
        "y_pred_test = logreg_location_taxa.predict(locX_test)\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_taxa, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WMPWfbjyNZ70",
        "outputId": "774f4bf6-d2f9-4570-fc9f-f9feb575880f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.40731995277449823\n",
            "Accuracy on testing data: 0.40654205607476634\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test.iloc[2]['taxon'])\n",
        "print(le.inverse_transform(logreg_location_taxa.predict(locX_test.iloc[2].values.reshape(1,-1))))\n",
        "print(le.inverse_transform(y_test_taxa[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nclis04exbzY",
        "outputId": "eeb0bb68-08a9-4493-d728-f5e237eb4077"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Insecta\n",
            "['Plantae']\n",
            "['Insecta']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Forest"
      ],
      "metadata": {
        "id": "bPMBl1ALwisb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random forest model based on locations\n",
        "rf_location_taxa = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_location_taxa.fit(locX_train, y_train_taxa)\n",
        "\n",
        "# evaluate accuracy\n",
        "y_pred_train = rf_location_taxa.predict(locX_train)\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_taxa, y_pred_train))\n",
        "\n",
        "y_pred_test = rf_location_taxa.predict(locX_test)\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_taxa, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOEi6CgHNdK1",
        "outputId": "a42a4916-3ef8-4275-89ca-9a60455d5fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.9881936245572609\n",
            "Accuracy on testing data: 0.9205607476635514\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test.iloc[2]['taxon'])\n",
        "print(le.inverse_transform(rf_location_taxa.predict(locX_test.iloc[2].values.reshape(1,-1))))\n",
        "print(le.inverse_transform(y_test_taxa[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcnglas4xf3p",
        "outputId": "8f9d3320-ae05-4b3f-ed8b-173926b7dffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Insecta\n",
            "['Insecta']\n",
            "['Insecta']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Combine Embedding and Location Models\n"
      ],
      "metadata": {
        "id": "JsIUDkkxsWvU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def combined_model(embedding_model, location_model, data):\n",
        "    # Embedding Model\n",
        "    embeddings = data['embedding_numeric'].apply(pd.Series)\n",
        "    embeddings = embeddings.to_numpy()\n",
        "    embedding_predictions = embedding_model.predict(embeddings)\n",
        "\n",
        "    if type (embedding_model) == LogisticRegression:\n",
        "        embedding_weights = embedding_model.coef_\n",
        "    elif type (embedding_model) == RandomForestClassifier:\n",
        "        embedding_weights = embedding_model.feature_importances_\n",
        "    else:\n",
        "        embedding_weights = embedding_model.get_weights()\n",
        "\n",
        "\n",
        "    # Location Model\n",
        "    locations = data[['latitude', 'longitude']]\n",
        "    location_predictions = location_model.predict(locations)\n",
        "\n",
        "    if type (location_model) == LogisticRegression:\n",
        "        location_weights = location_model.coef_\n",
        "    elif type (location_model) == RandomForestClassifier:\n",
        "        location_weights = location_model.feature_importances_\n",
        "        #location_weights = np.tile(location_model.feature_importances_, (embedding_weights.shape[0], 1))\n",
        "    else:\n",
        "        location_weights = location_model.get_weights()\n",
        "\n",
        "\n",
        "    # Combine using weighted average\n",
        "    embedding_predictions = embedding_predictions.reshape(-1,1)\n",
        "    expanded_embedding_predictions = np.repeat(embedding_predictions, embedding_weights.shape[0], axis=1)\n",
        "    embedding_piece = np.matmul(expanded_embedding_predictions,embedding_weights)\n",
        "\n",
        "    location_predictions = location_predictions.reshape(-1,1)\n",
        "    expanded_location_predictions = np.repeat(location_predictions, location_weights.shape[0], axis=1)\n",
        "    location_weights = location_weights.reshape(-1,1)\n",
        "    expanded_location_weights = np.repeat(location_weights, embedding_weights.shape[1], axis=1)\n",
        "    location_piece = np.matmul(expanded_location_predictions,expanded_location_weights)\n",
        "\n",
        "    combined_predictions = embedding_piece + location_piece\n",
        "    combined_predictions = np.around(combined_predictions[:, 0], 0).astype(int)\n",
        "    combined_predictions = list(combined_predictions)\n",
        "\n",
        "    return combined_predictions"
      ],
      "metadata": {
        "id": "8dkFWGf7_3yM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluate Combined Model"
      ],
      "metadata": {
        "id": "gYjec2K2sfV8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Get data in right format for the combined model"
      ],
      "metadata": {
        "id": "pyYvYx8IbWm5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get data in the right format for the combined model\n",
        "# Training Data\n",
        "inat_train = inat_train.dropna(subset=['location'])\n",
        "\n",
        "inat_train[['latitude', 'longitude']] = inat_train['location'].str.split(',', expand=True)\n",
        "inat_train['latitude'] = pd.to_numeric(inat_train['latitude'])\n",
        "inat_train['longitude'] = pd.to_numeric(inat_train['longitude'])\n",
        "\n",
        "inat_train['embedding_array'] = inat_train['embedding'].apply(lambda x: np.array(eval(x)) if isinstance(x, str) else x)\n",
        "inat_train['embedding_numeric'] = inat_train['embedding_array'].apply(lambda x: x.flatten() if isinstance(x, np.ndarray) else x)\n",
        "inat_train['embedding_numeric'] = inat_train['embedding_numeric'].apply(lambda x: pd.to_numeric(x))\n",
        "\n",
        "embedding_numeric_array = inat_train['embedding_numeric'].values\n",
        "embedding_numeric_reshaped = embedding_numeric_array.reshape(inat_train['embedding_array'].shape)\n",
        "inat_train['embedding_numeric'] = embedding_numeric_reshaped\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBrvkIPgZh2c",
        "outputId": "ef38bd19-2393-49e7-f43f-c96ea523c2c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.194838  , 0.        , 0.1844602 , ..., 0.18166535, 1.1895064 ,\n",
              "       0.29901576])"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing Data\n",
        "inat_test = inat_test.dropna(subset=['location'])\n",
        "\n",
        "inat_test[['latitude', 'longitude']] = inat_test['location'].str.split(',', expand=True)\n",
        "inat_test['latitude'] = pd.to_numeric(inat_test['latitude'])\n",
        "inat_test['longitude'] = pd.to_numeric(inat_test['longitude'])\n",
        "\n",
        "inat_test['embedding_array'] = inat_test['embedding'].apply(lambda x: np.array(eval(x)) if isinstance(x, str) else x)\n",
        "inat_test['embedding_numeric'] = inat_test['embedding_array'].apply(lambda x: x.flatten() if isinstance(x, np.ndarray) else x)\n",
        "inat_test['embedding_numeric'] = inat_test['embedding_numeric'].apply(lambda x: pd.to_numeric(x))\n",
        "\n",
        "embedding_numeric_array = inat_test['embedding_numeric'].values\n",
        "embedding_numeric_reshaped = embedding_numeric_array.reshape(inat_test['embedding_array'].shape)\n",
        "inat_test['embedding_numeric'] = embedding_numeric_reshaped\n"
      ],
      "metadata": {
        "id": "jx08dkb3aDH4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluate for species level"
      ],
      "metadata": {
        "id": "m6_qFoXxbb32"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image_model = logreg_embed_species\n",
        "location_model = rf_location_species\n",
        "\n",
        "combined_train_predictions = combined_model(image_model, location_model, inat_train)\n",
        "combined_test_predictions = combined_model(image_model, location_model, inat_test)\n",
        "\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_species, combined_train_predictions))\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_species, combined_test_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "irRtXkU6rKye",
        "outputId": "a77d280f-6a07-4c58-f709-610484970178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.910271546635183\n",
            "Accuracy on testing data: 0.6915887850467289\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example case\n",
        "print(inat_test['photo'][2])\n",
        "print(inat_test['species_guess'][2])\n",
        "print(le.inverse_transform(combined_test_predictions[2].reshape(1,-1)))\n",
        "print(le.inverse_tranform(y_test_species[2].reshape(1,-1)))"
      ],
      "metadata": {
        "id": "xpxi4jV0sj9w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "9bd4f622-9413-40ae-afe5-f470e9ee59d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://inaturalist-open-data.s3.amazonaws.com/photos/33149746/square.jpg\n",
            "Simulium\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "y contains previously unseen labels: [340]",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-322-76e52d886874>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minat_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'photo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\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      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minat_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'species_guess'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\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[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombined_test_predictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\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[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_tranform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test_species\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\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[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py\u001b[0m in \u001b[0;36minverse_transform\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdiff1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\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    161\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff\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[0;32m--> 162\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y contains previously unseen labels: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff\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[0m\u001b[1;32m    163\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: y contains previously unseen labels: [340]"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluate for higher order"
      ],
      "metadata": {
        "id": "PvUjt1P9Pnji"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image_model = logreg_embed_taxa\n",
        "location_model = rf_location_taxa\n",
        "\n",
        "combined_train_predictions = combined_model(image_model, location_model, inat_train)\n",
        "combined_test_predictions = combined_model(image_model, location_model, inat_test)\n",
        "\n",
        "print(\"Accuracy on training data:\", accuracy_score(y_train_taxa, combined_train_predictions))\n",
        "print(\"Accuracy on testing data:\", accuracy_score(y_test_taxa, combined_test_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MiSlIVGYQPJm",
        "outputId": "089827c3-8811-4544-a57e-d896bef45801"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data: 0.9881936245572609\n",
            "Accuracy on testing data: 0.9205607476635514\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inat_test['photo'][2])\n",
        "print(inat_test['taxon'][2])\n",
        "print(le.inverse_transform(combined_test_predictions[2].reshape(1,-1)))\n",
        "print(le.inverse_transform(y_test_taxa[2].reshape(1,-1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spp8X0meQe2U",
        "outputId": "d7925585-67aa-4656-bda1-5f6b79c4f446"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://inaturalist-open-data.s3.amazonaws.com/photos/33149746/square.jpg\n",
            "Insecta\n",
            "['Insecta']\n",
            "['Insecta']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:155: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    }
  ]
}