2. Main Models.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of COVID-19 LSTM",
      "provenance": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "b5ddd1c428544473b760875f4e5b4ddf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_22ce51d9c5794078b6fc7e8f2b2170eb",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_20e46508bcfe4fa5a44882430231c24c",
              "IPY_MODEL_358399cdd9704469ba4b4ebc8b67a227"
            ]
          }
        },
        "22ce51d9c5794078b6fc7e8f2b2170eb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "20e46508bcfe4fa5a44882430231c24c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_fdc8abe93221438d85aca6709d2cc419",
            "_dom_classes": [],
            "description": "",
            "_model_name": "IntProgressModel",
            "bar_style": "success",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_a0d12f705b3d44a5ae1a8d81686c1e98"
          }
        },
        "358399cdd9704469ba4b4ebc8b67a227": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_1ad8ef3aeff047b6b3e7432ab1154b36",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 2196018/? [03:31&lt;00:00, 10370.65it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_1c7dd575137544859d4889265575bb71"
          }
        },
        "fdc8abe93221438d85aca6709d2cc419": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "a0d12f705b3d44a5ae1a8d81686c1e98": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "1ad8ef3aeff047b6b3e7432ab1154b36": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "1c7dd575137544859d4889265575bb71": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "7288266837ae4de7bdc0059ffa05aa38": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_b88f6d850fdc4e68873574c68126e6ff",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_cc3a1bb0ef1a4e26a36c41e2bd476908",
              "IPY_MODEL_1ac5a1501bf24017bf8be7968c743746"
            ]
          }
        },
        "b88f6d850fdc4e68873574c68126e6ff": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "cc3a1bb0ef1a4e26a36c41e2bd476908": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_96b65db84ee2487589c496a29b482713",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "IntProgressModel",
            "bar_style": "success",
            "max": 403877,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 403877,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_65a3994a78234e05b48a6646c7523124"
          }
        },
        "1ac5a1501bf24017bf8be7968c743746": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_6fbe051f03554432ab21881815c17ff9",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 403877/403877 [00:00&lt;00:00, 442198.75it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_6c26ea794e4b4005ab5bf4aadf3c603f"
          }
        },
        "96b65db84ee2487589c496a29b482713": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "65a3994a78234e05b48a6646c7523124": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "6fbe051f03554432ab21881815c17ff9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "6c26ea794e4b4005ab5bf4aadf3c603f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "738759a5b2384e16978018c450bd9782": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_af456a960d49409da2b6db62a8826440",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_80b29585b9b34c059e3862e6930e7bc6",
              "IPY_MODEL_6ee9c3a93210411bb67aac4e3089128f"
            ]
          }
        },
        "af456a960d49409da2b6db62a8826440": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "80b29585b9b34c059e3862e6930e7bc6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_2bbd9e502fb94176bd7d651df8021e6f",
            "_dom_classes": [],
            "description": "",
            "_model_name": "IntProgressModel",
            "bar_style": "success",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_43ba7c6ab27a43dc9085c9738a9b4813"
          }
        },
        "6ee9c3a93210411bb67aac4e3089128f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_2380781d63894dcd82ddd5db3ce1d58e",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 2196018/? [02:58&lt;00:00, 12285.93it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_72b0ab742a024555bf334f6dea8deea2"
          }
        },
        "2bbd9e502fb94176bd7d651df8021e6f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "43ba7c6ab27a43dc9085c9738a9b4813": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "2380781d63894dcd82ddd5db3ce1d58e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "72b0ab742a024555bf334f6dea8deea2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "08f614013a194e8f8c137c35c7bfba3c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_9dfdc370edc3477eb177e824dc1e5ff1",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_f17d8cac742449c0b118442b08c1f218",
              "IPY_MODEL_d8cb857d2ecf4d0f9229aba1c129e45b"
            ]
          }
        },
        "9dfdc370edc3477eb177e824dc1e5ff1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "f17d8cac742449c0b118442b08c1f218": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_7ffb9b239d8d4777ab6eefe3a8e7294d",
            "_dom_classes": [],
            "description": "100%",
            "_model_name": "IntProgressModel",
            "bar_style": "success",
            "max": 403877,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 403877,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_4b08a55b8e1f4a09b8ad308fc3e51f68"
          }
        },
        "d8cb857d2ecf4d0f9229aba1c129e45b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_f8b20d0aa2564af084c6f65bde745658",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 403877/403877 [00:00&lt;00:00, 534921.98it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_ea2d56754c5a47dd8394235abba729ab"
          }
        },
        "7ffb9b239d8d4777ab6eefe3a8e7294d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "4b08a55b8e1f4a09b8ad308fc3e51f68": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "f8b20d0aa2564af084c6f65bde745658": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "ea2d56754c5a47dd8394235abba729ab": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EZYmHlTxEGy",
        "colab_type": "code",
        "outputId": "b6c7d436-3233-4357-f770-c04769b6fe29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sun Apr  5 09:56:06 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.64.00    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D662RMVMIC7S",
        "colab_type": "code",
        "outputId": "86398514-82fa-4f53-abea-e2f49067c8f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0q0HGr8mHgQt",
        "colab_type": "code",
        "outputId": "ad268e84-cf3d-4d21-e598-ef11f9c31fa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import sqlite3\n",
        "import os\n",
        "import re\n",
        "%tensorflow_version 1.x\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "np.random.seed(1)\n",
        "tf.set_random_seed(2)\n",
        "\n",
        "import pandas as pd\n",
        "import keras\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.utils import class_weight\n",
        "from sklearn.metrics import f1_score, classification_report, log_loss\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, LSTM, Dense, SpatialDropout1D, Bidirectional, Flatten\n",
        "from keras.layers import Dropout, Conv1D, GlobalMaxPool1D, GRU, GlobalAvgPool1D\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mljqjBn-ZB9W",
        "colab_type": "text"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjfbaN_5TotP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conn = sqlite3.connect('/content/drive/My Drive/Colab Notebooks/data/corona_tweets_1M.db')\n",
        "c = conn.cursor()\n",
        "df_pie = pd.read_sql(\"SELECT * FROM sentiment\", conn)\n",
        "total_tweets = df_pie.shape[0]\n",
        "p_tweets = df_pie.apply(lambda x: True if x['sentiment'] > 0 else False , axis=1)\n",
        "positive_tweets = len(p_tweets[p_tweets == True].index)\n",
        "n_tweets = df_pie.apply(lambda x: True if x['sentiment'] < 0 else False , axis=1)\n",
        "negative_tweets = len(n_tweets[n_tweets == True].index)\n",
        "neutral_tweets = total_tweets - positive_tweets - negative_tweets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCOPXlzwYmKk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def assign_label(df):\n",
        "    if df['sentiment'] > 0.3:\n",
        "        return 'Positve'\n",
        "    elif df['sentiment'] < -0.3:\n",
        "        return 'Negative'\n",
        "    else:\n",
        "      return 'Neutral'\n",
        "df_pie['label'] = df_pie.apply(assign_label, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5otDsu9dM9B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#training constants\n",
        "MAX_SEQ_LEN = 25 #this is based on a quick analysis of the len of sequences train['text'].apply(lambda x : len(x.split(' '))).quantile(0.95)\n",
        "DEFAULT_BATCH_SIZE = 128"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jkBWZxUHbB7e",
        "colab_type": "code",
        "outputId": "f1288049-f0f7-4b40-f3cf-1b1260f4a844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "train, test = train_test_split(df_pie, random_state = 42, test_size=0.1)\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1421061, 4)\n",
            "(157896, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZUuQbMvbAr5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def clean_text(text, mapping):\n",
        "    replace_white_space = [\"\\n\"]\n",
        "    for s in replace_white_space:\n",
        "        text = text.replace(s, \" \")\n",
        "    replace_punctuation = [\"’\", \"‘\", \"´\", \"`\", \"\\'\", r\"\\'\"]\n",
        "    for s in replace_punctuation:\n",
        "        text = text.replace(s, \"'\")\n",
        "    \n",
        "    # Random note: removing the URL's slightly degraded performance, it's possible the model learned that certain URLs were positive/negative\n",
        "    # And was able to extrapolate that to retweets. Could also explain why re-training the Embeddings improves performance.\n",
        "    # remove twitter url's\n",
        "#     text = re.sub(r\"http[s]?://t.co/[A-Za-z0-9]*\",\"TWITTERURL\",text)\n",
        "    mapped_string = []\n",
        "    for t in text.split(\" \"):\n",
        "        if t in mapping:\n",
        "            mapped_string.append(mapping[t])\n",
        "        elif t.lower() in mapping:\n",
        "            mapped_string.append(mapping[t.lower()])\n",
        "        else:\n",
        "            mapped_string.append(t)\n",
        "    return ' '.join(mapped_string)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAtDtSwcckhh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "CONTRACTION_MAPPING = { \n",
        "\"ain't\": \"am not / are not / is not / has not / have not\",\n",
        "\"aren't\": \"are not / am not\",\n",
        "\"can't\": \"cannot\",\n",
        "\"can't've\": \"cannot have\",\n",
        "\"'cause\": \"because\",\n",
        "\"could've\": \"could have\",\n",
        "\"couldn't\": \"could not\",\n",
        "\"couldn't've\": \"could not have\",\n",
        "\"didn't\": \"did not\",\n",
        "\"doesn't\": \"does not\",\n",
        "\"don't\": \"do not\",\n",
        "\"hadn't\": \"had not\",\n",
        "\"hadn't've\": \"had not have\",\n",
        "\"hasn't\": \"has not\",\n",
        "\"haven't\": \"have not\",\n",
        "\"he'd\": \"he had / he would\",\n",
        "\"he'd've\": \"he would have\",\n",
        "\"he'll\": \"he shall / he will\",\n",
        "\"he'll've\": \"he shall have / he will have\",\n",
        "\"he's\": \"he has / he is\",\n",
        "\"how'd\": \"how did\",\n",
        "\"how'd'y\": \"how do you\",\n",
        "\"how'll\": \"how will\",\n",
        "\"how's\": \"how has / how is / how does\",\n",
        "\"I'd\": \"I had / I would\",\n",
        "\"I'd've\": \"I would have\",\n",
        "\"I'll\": \"I shall / I will\",\n",
        "\"I'll've\": \"I shall have / I will have\",\n",
        "\"I'm\": \"I am\",\n",
        "\"I've\": \"I have\",\n",
        "\"isn't\": \"is not\",\n",
        "\"it'd\": \"it had / it would\",\n",
        "\"it'd've\": \"it would have\",\n",
        "\"it'll\": \"it shall / it will\",\n",
        "\"it'll've\": \"it shall have / it will have\",\n",
        "\"it's\": \"it has / it is\",\n",
        "\"let's\": \"let us\",\n",
        "\"ma'am\": \"madam\",\n",
        "\"mayn't\": \"may not\",\n",
        "\"might've\": \"might have\",\n",
        "\"mightn't\": \"might not\",\n",
        "\"mightn't've\": \"might not have\",\n",
        "\"must've\": \"must have\",\n",
        "\"mustn't\": \"must not\",\n",
        "\"mustn't've\": \"must not have\",\n",
        "\"needn't\": \"need not\",\n",
        "\"needn't've\": \"need not have\",\n",
        "\"o'clock\": \"of the clock\",\n",
        "\"oughtn't\": \"ought not\",\n",
        "\"oughtn't've\": \"ought not have\",\n",
        "\"shan't\": \"shall not\",\n",
        "\"sha'n't\": \"shall not\",\n",
        "\"shan't've\": \"shall not have\",\n",
        "\"she'd\": \"she had / she would\",\n",
        "\"she'd've\": \"she would have\",\n",
        "\"she'll\": \"she shall / she will\",\n",
        "\"she'll've\": \"she shall have / she will have\",\n",
        "\"she's\": \"she has / she is\",\n",
        "\"should've\": \"should have\",\n",
        "\"shouldn't\": \"should not\",\n",
        "\"shouldn't've\": \"should not have\",\n",
        "\"so've\": \"so have\",\n",
        "\"so's\": \"so as / so is\",\n",
        "\"that'd\": \"that would / that had\",\n",
        "\"that'd've\": \"that would have\",\n",
        "\"that's\": \"that has / that is\",\n",
        "\"there'd\": \"there had / there would\",\n",
        "\"there'd've\": \"there would have\",\n",
        "\"there's\": \"there has / there is\",\n",
        "\"they'd\": \"they had / they would\",\n",
        "\"they'd've\": \"they would have\",\n",
        "\"they'll\": \"they shall / they will\",\n",
        "\"they'll've\": \"they shall have / they will have\",\n",
        "\"they're\": \"they are\",\n",
        "\"they've\": \"they have\",\n",
        "\"to've\": \"to have\",\n",
        "\"wasn't\": \"was not\",\n",
        "\"we'd\": \"we had / we would\",\n",
        "\"we'd've\": \"we would have\",\n",
        "\"we'll\": \"we will\",\n",
        "\"we'll've\": \"we will have\",\n",
        "\"we're\": \"we are\",\n",
        "\"we've\": \"we have\",\n",
        "\"weren't\": \"were not\",\n",
        "\"what'll\": \"what shall / what will\",\n",
        "\"what'll've\": \"what shall have / what will have\",\n",
        "\"what're\": \"what are\",\n",
        "\"what's\": \"what has / what is\",\n",
        "\"what've\": \"what have\",\n",
        "\"when's\": \"when has / when is\",\n",
        "\"when've\": \"when have\",\n",
        "\"where'd\": \"where did\",\n",
        "\"where's\": \"where has / where is\",\n",
        "\"where've\": \"where have\",\n",
        "\"who'll\": \"who shall / who will\",\n",
        "\"who'll've\": \"who shall have / who will have\",\n",
        "\"who's\": \"who has / who is\",\n",
        "\"who've\": \"who have\",\n",
        "\"why's\": \"why has / why is\",\n",
        "\"why've\": \"why have\",\n",
        "\"will've\": \"will have\",\n",
        "\"won't\": \"will not\",\n",
        "\"won't've\": \"will not have\",\n",
        "\"would've\": \"would have\",\n",
        "\"wouldn't\": \"would not\",\n",
        "\"wouldn't've\": \"would not have\",\n",
        "\"y'all\": \"you all\",\n",
        "\"y'all'd\": \"you all would\",\n",
        "\"y'all'd've\": \"you all would have\",\n",
        "\"y'all're\": \"you all are\",\n",
        "\"y'all've\": \"you all have\",\n",
        "\"you'd\": \"you had / you would\",\n",
        "\"you'd've\": \"you would have\",\n",
        "\"you'll\": \"you shall / you will\",\n",
        "\"you'll've\": \"you shall have / you will have\",\n",
        "\"you're\": \"you are\",\n",
        "\"you've\": \"you have\"\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbqPcG6ebeEX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get tweets from Data frame and convert to list of \"texts\" scrubbing based on clean_text function\n",
        "# CONTRACTION_MAPPING is a map of common contractions(e.g don't => do not)\n",
        "train_text_vec = [clean_text(text, CONTRACTION_MAPPING) for text in train['tweet'].values]\n",
        "test_text_vec = [clean_text(text, CONTRACTION_MAPPING) for text in test['tweet'].values]\n",
        "\n",
        "\n",
        "# tokenize the sentences\n",
        "tokenizer = Tokenizer(lower=False)\n",
        "tokenizer.fit_on_texts(train_text_vec)\n",
        "train_text_vec = tokenizer.texts_to_sequences(train_text_vec)\n",
        "test_text_vec = tokenizer.texts_to_sequences(test_text_vec)\n",
        "\n",
        "# pad the sequences\n",
        "train_text_vec = pad_sequences(train_text_vec, maxlen=MAX_SEQ_LEN)\n",
        "test_text_vec = pad_sequences(test_text_vec, maxlen=MAX_SEQ_LEN)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hSUZbLvdl0p",
        "colab_type": "code",
        "outputId": "5c57db1d-7428-416f-e67d-edc0c73486af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print('Number of Tokens:', len(tokenizer.word_index))\n",
        "print(\"Max Token Index:\", train_text_vec.max(), \"\\n\")\n",
        "\n",
        "print('Sample Tweet Before Processing:', train[\"tweet\"].values[10000])\n",
        "print('Sample Tweet After Processing:', tokenizer.sequences_to_texts([train_text_vec[10000]]), '\\n')\n",
        "\n",
        "print('What the model will interpret:', train_text_vec[10000].tolist())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Tokens: 403877\n",
            "Max Token Index: 403877 \n",
            "\n",
            "Sample Tweet Before Processing: RT @MarkYoungTruth: As usual @BernieSanders is lying. He said the Democrats are using it as a hoax never said the corona virus is a hoax.\n",
            "Sample Tweet After Processing: ['RT MarkYoungTruth As usual BernieSanders is lying He said the Democrats are using it as a hoax never said the corona virus is a hoax'] \n",
            "\n",
            "What the model will interpret: [2, 1780, 493, 2498, 2014, 4, 1195, 262, 115, 1, 307, 8, 639, 12, 69, 7, 182, 158, 115, 1, 11, 9, 4, 7, 182]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1D0WvsoeGQK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# One Hot Encode Y values:\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "y_train = encoder.fit_transform(train['label'].values)\n",
        "y_train = to_categorical(y_train) \n",
        "\n",
        "y_test = encoder.fit_transform(test['label'].values)\n",
        "y_test = to_categorical(y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYKh5TP8ft3u",
        "colab_type": "code",
        "outputId": "5261bd87-a65a-4d0a-d1da-8b87fd065815",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# get an idea of the distribution of the text values\n",
        "from collections import Counter\n",
        "ctr = Counter(train['label'].values)\n",
        "print('Distribution of Classes:', ctr)\n",
        "\n",
        "# get class weights for the training data, this will be used data\n",
        "y_train_int = np.argmax(y_train,axis=1)\n",
        "cws = class_weight.compute_class_weight('balanced', np.unique(y_train_int), y_train_int)\n",
        "print(cws)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Distribution of Classes: Counter({'Neutral': 1130113, 'Positve': 154675, 'Negative': 136273})\n",
            "[3.47601506 0.41915012 3.06246646]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlg7pVlDgnZI",
        "colab_type": "code",
        "outputId": "fd13d196-fca6-4e1f-a1c3-65b4df2b68d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print('Dominant Class: ', ctr.most_common(n = 1)[0][0])\n",
        "print('Baseline Accuracy Dominant Class', (ctr.most_common(n = 1)[0][0] == test['label'].values).mean())\n",
        "\n",
        "preds = np.zeros_like(y_test)\n",
        "preds[:, 0] = 1\n",
        "preds[0] = 1 #done to suppress warning from numpy for f1 score\n",
        "print('F1 Score:', f1_score(y_test, preds, average='weighted'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dominant Class:  Neutral\n",
            "Baseline Accuracy Dominant Class 0.7954033034402391\n",
            "F1 Score: 0.016838638849731427\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ea4NKlSBgwMw",
        "colab_type": "code",
        "outputId": "1aa32d61-d9cb-4768-b5a3-9ceadc18abc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Naive Bayse Baseline\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "text_clf = Pipeline([\n",
        "    ('vect', CountVectorizer()),\n",
        "    ('tfidf', TfidfTransformer()),\n",
        "    ('nb', MultinomialNB()),\n",
        "])\n",
        "text_clf.fit(tokenizer.sequences_to_texts_generator(train_text_vec), y_train.argmax(axis=1))\n",
        "predictions = text_clf.predict(tokenizer.sequences_to_texts_generator(test_text_vec)) \n",
        "print('Baseline Accuracy Using Naive Bayes: ', (predictions == y_test.argmax(axis = 1)).mean())\n",
        "print('F1 Score:', f1_score(y_test.argmax(axis = 1), predictions, average='weighted'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Baseline Accuracy Using Naive Bayes:  0.9050957592339262\n",
            "F1 Score: 0.8940796028146756\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Szf_Kr6prJd9",
        "colab_type": "code",
        "outputId": "d83f78c2-bf77-4951-a14b-ae30e27992c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,plot_confusion_matrix\n",
        "\n",
        "#print('Confusion Matrix :', confusion_matrix(y_test.argmax(axis = 1),predictions))\n",
        "#print('Accuracy Score :', accuracy_score(y_test.argmax(axis = 1),predictions))\n",
        "#print(test_text_vec)\n",
        "for a in ['true','pred','all']:\n",
        "  print(plot_confusion_matrix(text_clf,tokenizer.sequences_to_texts_generator(test_text_vec),y_test.argmax(axis = 1),normalize=a).confusion_matrix)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.69120169e-01 4.30220288e-01 6.59543596e-04]\n",
            " [7.08649505e-04 9.97420197e-01 1.87115319e-03]\n",
            " [1.16665694e-04 4.74012717e-01 5.25870618e-01]]\n",
            "[[9.89564220e-01 4.66208296e-02 1.07991361e-03]\n",
            " [1.02064220e-02 8.95301467e-01 2.53779698e-02]\n",
            " [2.29357798e-04 5.80777038e-02 9.73542117e-01]]\n",
            "[[5.46498961e-02 4.13120028e-02 6.33328267e-05]\n",
            " [5.63662157e-04 7.93351320e-01 1.48832143e-03]\n",
            " [1.26665653e-05 5.14642550e-02 5.70945432e-02]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATUAAAEGCAYAAAAE8QIHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQU1EQuX/A2aaktFatVFxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATUAAAEGCAYAAAAE8QIHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATUAAAEGCAYAAAAE8QIHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nO3deXhU1fnA8e87WQmQIIQkJICC7IIL\nsogLooKAWml/tYrWtmotAuK+UbXVYotL6y7uVetutSpYkaBUQUUQ3NmJqCGQkA2SQPbM+/vjTmAS\nQjIDM5nJ8H6e5z7OvffMuedeJy/n3nPuOaKqGGNMpHCFugDGGBNIFtSMMRHFgpoxJqJYUDPGRBQL\nasaYiBId6gJ4i3W103bRHUNdjPBlLdUt0tq6UBchrFWyi2qtkgPJY/wp7bWo2Lfr/MW3VZmqOuFA\njuevsApq7aI7cnzyuaEuRtjSmppQFyHs1RUVh7oIYW25LjrgPIqK6/g8s6dPaaO6bUw+4AP6KayC\nmjEm/Cngxh3qYuyTBTVjjF8UpUbD9zbfgpoxxm9WUzPGRAxFqQvjRisLasYYv7mxoGaMiRAK1FlQ\nM8ZEEqupGWMihgI19kzNGBMpFLXbT2NMBFGoC9+YZkHNGOMf542C8GVBzRjjJ6GOA3onPqgsqBlj\n/OI0FFhQM8ZECKefmgU1Y0wEcVtNzRgTKaymZoyJKIpQF8YzAVhQM8b4zW4/jTERQxGqNSrUxdgn\nC2rGGL84nW/t9tMYE0HCuaEgfMOtMSYsqQp16vJpaYmITBCR9SKSJSIzm9h/v4h87Vk2iMiOlvK0\nmpoxxm/uANTURCQKmAOMA3KAFSIyT1XX1KdR1Wu80l8BHNNSvhbUjDF+cRoKAhI6RgBZqroJQERe\nBSYBa/aR/nzgtpYytaBmjPGLnw0FySKy0mv9SVV90vM5A9jstS8HGNlUJiJyKNAL+F9LB7SgZozx\nW53v/dQKVXVYAA45GXhDteUJRy2oGWP8EsA3CrYAPbzWu3u2NWUycLkvmVpQM8b4ze1Dy6YPVgB9\nRaQXTjCbDFzQOJGIDAAOAT7zJVMLasYYvzgvtB94UFPVWhGZAWQCUcAzqrpaRGYBK1V1nifpZOBV\nVd9me7GgZozxiyLUBOg1KVWdD8xvtO3PjdZv9yfPgyaoHXt8IVOuX4crSln4Vndef65Xg/3RMW6u\nu+M7+gwspWxHDHfNPIr83HakdKvg8f98ypaf2gOw7rsk5sweBMCdT66gc3IV1VXO/+Bbpw+lZHtc\n655YAB17QhGX3bQRVxRkvtmN1/95aIP90TFurp+9lj6DyijbEc2dNxxB/tZ2u/d3Tavk8bmf89Kj\nh/Hmv3oCcPWstYwYXcSO4lim/9+IVj2fA9E+sY5r/rGZwwZUogr3XduDtV+0371/1PgSfntDHqpQ\nVys8fls6qz/v4McRlGl3bGXEqaVUVri495oeZH2XAMD8zd/w47p4APK3xHL7Rb2ay6jVqeJTx9pQ\nCWpQE5EJwIM4VcunVfWuYB5vX1wuZdpNa7l1+rEUbovn/heXsWxxVzb/sOdHOP7nOewsjeEPk05i\n9Om5XHzVBu6eeRQAuTntuOL8UU3m/fdbhpC1NqlVziOYXC5l+i0buGXK0RTmxfHAqytZ9mEymzft\n+UMe/3+57CyN5tIzj2P0hG1ccs0m7rrhiN37/3BDFis/6dwg3w/mduOdV7pz3d/Wttq5BMK0WVtY\n+VFH/jrlMKJj3MS1a3jn89XHHfgssx8g9BpYwS1P/MSlowf4nP/wU8vI6FXFxScMYMDQcq64cwtX\nndUXgOpKF9PH9Q/k6QSYBKTzbbAELdx69RaeCAwCzheRQcE6XnP6DS5ha04CeVsSqK11sSQzjePG\n5DdIM3JMAYv+mw7AJ4tSOWp4MYTx3IaB1m9IKVuz25GX0865Ru+lMuqUwgZpjjulgA/mpQHwyftd\nOWrkduqv0ahTC8jbEk92VvsG31n1RSfKStrWDUFCxzqGHLeLBS87Abq2xsWu0oa3W5XlUeD5w45P\ncOP9tOecafk8NH8Dj32wnt9cn9fkMUaNL+GDNw4BhHVftqd9Uh2dU2qCcToBpxCw16SCIZhH3d1b\nWFWrgfrewq2uS9dKCvPid68X5sfTJaVqrzQFnjTuOhflO6NJ7OT8yNIyKnjo5c+466kVHHHM9gbf\nu+b21Tz8ymdMvvR72nIQ7JJS1fAabYujS2qja5RSTUGec3vtXKMoEjvVEN+ulnMuyeblxw5rzSIH\nTVrPakqKorju/s3MWbieq/+xmbh2e3ePOn5CCU8vWccdz//Afdc6PROGnuzUwK48oy/Tx/Wj75By\nBo/cudd3k9NqKNgas3u9cGsMXdKc31tsnJuH39vAA+9sZNSEkiCd5YGpw+XTEgrB/CfU597C4ay4\nMI6LzhhNWUksfQaWcuu9XzHtVydQsSuaf9wyhKKCeNol1HLz37/h1DNz+d+76aEucqv79fQfefuF\nHlRWtK0a2b5ERSl9hlQw59YM1n/VnqmztnDejHye/3u3BumWLkhi6YIkBo/cye9uzGPmeYdz7Mll\nDD25jEff3wBAuwQ3Gb2rWLXc9+dtvxkxiKK8GNJ6VnH369/z49p4cn8Kn2e1itggkc0RkSnAFID4\nKH8etPquqCCe5LTK3evJKZUU5cftlaZrWiVF+fG4otwkdKildEcMIJSVxAKQtTaR3JwEMnruImtt\nEkUFTs2mojyaxQvS6De4pM0GtaL8uIbXKLWKom2NrlF+LF3TqijaVn+N6ijdEUP/IaWcOK6AS675\nnvYda1GF6moX/32le2ufRkAU5sZQkBvD+q+cW+lP/pvEuTPy95l+1fIOpPXcTGLnWgR47eFU5r/Y\npUGan11UyMRfFwFw64W9KcyLoWv6ntvN5PQaivKcmlv9f/Oy4/h2aQcOH1wRZkENagLz7mdQBLN+\n6FNvYVV9UlWHqeqwWFe7xrsDYsPqRDJ6lJOaXk50tJvR4/NYvjilQZrli7ty2llbATjxtG18u6Iz\nICR2qsblcm4r0zLKSe9ZTt6WBFxRbhI7VQMQFe1m+EkF/JQVnKDcGjas6kj6oRWkZlQ412jiNpZ9\nlNwgzfKPkhl7tvOM6MRxBXz7eSdAuPGioVw8YRQXTxjF3Be789pTh7bZgAawvSCGwq2xdD/cCfJH\nn7ST7I3xDdKkH1ZF/eOGPkPKiYl1U1ocxcrFHRk/uZj4BOd2tUtaDUldanjnuWSmj+vP9HH9Kd4W\nw7KFSYw9x3kmOWDoLspLXRTnx9AhqZaYWGf+88TOtRwxfBfZGxoeO/ScyYx9WUIhmOHWp97CrcFd\n5+Kxuwdwx5wvcbmU9+dlkL2pAxdOzWLjmkSWL0lh4dsZXH/HKp6a+zFlJTHc88cjARg8dDsXTsui\nrtaF2w1zZg9kZ2kMcfG13DHnC6KiFZdL+Xp5FzLfart/yO46F4/N7sdfH//G0+2lG9nft+fCyzex\ncXUiyz9KJvPNblx/51qefncZZSXR3H3jES3me+Pdqzly+A4SO9Xw/AdLeXHOYSx8K/xrs3NuzeCm\nR7KJjlHysmO595oenPkbp+Hk3ReSOfHMEsaeU0xtrVBV4WL2tEMB4cvFHenZp5IH3skCoGKXi3uu\n6ElJUcP8P1/UkeGnlfLs0nVUebp0APTsW8WVd+egbhAXvDYnZa+AGmpKwN4oCArxsZPu/mUucgbw\nAHt6C/+tufRJsSl6fPK5QStPW6c1baN1LJTqiopDXYSwtlwXUarFB1SF6j44SS//9wk+pb35iPe+\nCNAL7T4L6o1xU72FjTFtm6qEdU0tfJ/2GWPCktNQYLNJGWMihhy8r0kZYyKP01Bg/dSMMREkVG8L\n+MKCmjHGL/ZGgTEm4tgM7caYiKEKNW4LasaYCOHcfoZvUAvfkhljwlag3v0UkQkisl5EskRk5j7S\nnCsia0RktYi83FKeVlMzxvglUF06vAaSHYczNNkKEZmnqmu80vQF/gicoKrbRSSl6dz2sKBmjPFT\nwG4/dw8kCyAi9QPJrvFK8wdgjqpuB1DVfY8B5WG3n8YYv7k98xS0tADJIrLSa5nilU1TA8lmNDpU\nP6CfiHwqIss88540y2pqxhi/OK2fPr/7WXiAo3REA32BMThjMi4RkSGquqO5LxhjjM8C2PnWl4Fk\nc4DlqloD/CAiG3CC3Ip9ZWq3n8YYv/lx+9mc3QPJikgszkCy8xqleRunloaIJOPcjm5qLlOrqRlj\n/BKo1k9VrRWRGUAmewaSXS0is4CVqjrPs+90EVkD1AE3qGrRvnO1oGaM2Q+B6nzb1ECyqvpnr88K\nXOtZfGJBzRjjF1WhNozfKLCgZozxm43SYYyJGDZIpDEm4lhQM8ZEDBsk0hgTcXzogxYyFtSMMX5R\nhVobJNIYE0ns9tMYEzHsmZoxJuKoBTVjTCSxhgJjTMRQtWdqxpiIItRZ66cxJpLYMzUfaU0ttXnb\nQl2MsJW59etQFyHsjU8/OtRFiHj27qcxJrKo81wtXFlQM8b4zVo/jTERQ62hwBgTacL59jN8w60x\nJmypik9LS0RkgoisF5EsEZnZxP6LRKRARL72LJe2lKfV1IwxflENTJcOEYkC5gDjcOb3XCEi81R1\nTaOkr6nqDF/ztZqaMcZvbhWflhaMALJUdZOqVgOvApMOtGwW1IwxflP1bQGSRWSl1zLFK5sMYLPX\neo5nW2O/FJFvReQNEenRxP4G7PbTGOMXRXD73vpZqKrDDuBw7wCvqGqViFwG/As4tbkvWE3NGOM3\n9XFpwRbAu+bV3bNtz3FUi1S1yrP6NHBsS5laUDPG+EcD1vq5AugrIr1EJBaYDMzzTiAi3bxWzwbW\ntpSp3X4aY/wXgH5qqlorIjOATCAKeEZVV4vILGClqs4DrhSRs4FaoBi4qKV8LagZY/wWqFE6VHU+\nML/Rtj97ff4j8Ed/8txnUBORh2kmHqvqlf4cyBgTGRRwu9vmu58rW60Uxpi2Q4G2OPSQqv7Le11E\nElS1PPhFMsaEuzb97qeIjBKRNcA6z/pRIvJo0EtmjAlfAerTEQy+dOl4ABgPFAGo6jfA6GAWyhgT\nznzrzhGqIb99av1U1c0iDQpYF5ziGGPahDC+/fQlqG0WkeMBFZEY4Cp86ABnjIlQChrGrZ++3H5O\nBS7HedF0K3C0Z90Yc9ASH5fW12JNTVULgV+3QlmMMW1FGN9++tL62VtE3vGMPpkvInNFpHdrFM4Y\nE6baeOvny8C/gW5AOvA68EowC2WMCWP1nW99WULAl6CWoKovqGqtZ3kRiA92wYwx4cuPQSJbXXPv\nfnb2fHzPMyHCqzgx+jwavYBqjDnIhHHrZ3MNBV/gBLH60l/mtU/x8815Y0zkkDBuKGju3c9erVkQ\nY0wbEcJGAF/49EaBiAwGBuH1LE1Vnw9WoYwx4Sx0jQC+aDGoichtwBicoDYfmAh8AlhQM+ZgFcY1\nNV9aP88BTgPyVPVi4CggKailMsaEN7ePSwj4cvtZoapuEakVkUQgn4YzwISVYWNKmXrHVqJcynuv\ndObfj6Q22B8T6+aGh7LpO6SC0u3RzJ56KNtyYgE4b8Y2JpxfTJ1beOzWdL5YnNhsntfdn82Ro3ax\nq8z5t+EfV/dk0+p2ABw5aidTZ20hOlopKY7mhl/2aa1LEBArPuzI43/KoM4tTDy/iPOuyG+w//Hb\n0vnm044AVFUKOwpjeHPddwA8/ddufL7IuXYXXL2NMZN2tG7hAygYv6dr78tm5NgydhRGc9mp/Xfn\ndeF1eUy8oIiSYufP8tk7u7Hif4mtdKZ+COAgkSIyAXgQZ46Cp1X1rn2k+yXwBjBcVZsdwNaXoLZS\nRDoBT+G0iO4EPvOhsM8AZwH5qjrYh+McMJdLuXz2Fv44uTeFuTE8PH8jyzKTyN64p1vd+POL2bkj\nmotPGMjJk7bz+1u3MnvqYfTsW8mYSTuYckp/OqfWcNdrm/j9ic4fbXN5PnVHNz55t1ODcrRPrGPG\nnTnc8uveFGyJJalLTWucfsDU1cGcm7tz56vfk9ythivO6Mdx40s4tF/V7jRT/7J19+e5/0wma5UT\nzJd/kEjWdwk89v56aqpd3PDLPgw/tZT2HUP0z/YBCMbvye0WFr7WmXnPJnPDg5v3OuZbT3XljcdT\nWvM090sgWj9FJAqYA4zDmch4hYjMU9U1jdJ1xBlIY7kv+bZ4+6mq01V1h6o+7jn47zy3oS15Dpjg\nSyECpf8x5Wz9MZa87Dhqa1x8NLcTo8aXNEgzanwJ779+CAAf/7cTR5+4E1BGjS/ho7mdqKl2sW1z\nHFt/jKX/MeU+5dnYKb/YzqfzkyjY4vyLXVIUE5TzDZb1XyWQflgV3Q6tJiZWGTNpO59l7vuJw4dv\nH8KYn28HIHtDHEOO20lUNMQnuOk1sIKVH4ZhbcMHwfg9Aaxa3oGy7W18zqPAvCY1AshS1U2qWo3T\nF3ZSE+nuAO4GKn0p2j6DmogMbbwAnYFoz+dmqeoSnCmtWk2XtBoKtsbuXi/MjSG5W8NaUnJaLQVb\nnSDjrhN2lUaR2LmO5G6NvxtLl7SaFvO8aGYej32wnstu30JMrFMb6d67ig6d6rjnjSweWbCBsee0\n6mU4YEV5MXRN33OOyd1qKMxtOjBvy4lh2+ZYzx8z9B5UycoPO1JZLpQURfHN0g67r3dbE4zfU0t+\ndnEhj32wnmvvy6ZDUm2AziSkkkVkpdcyxWtfBuBdXc3xbNvNE2t6qOq7vh6wuX8u7m1mn9LC1O++\n8pzkFIB4EgKRZat59s5uFOdHExOrXHVPDudens9L96cRFa30HVLBTef2Jq6d8sC8jaz9sj1bNsWF\nusgB99Hbh3DimTuIinLWjx1TxvpvErjm7H4kdall4LG7cEWFtoxtxX//1YWX709FFX53Yx5TbtvK\nfdf2DHWxmuTH7Wehqg7br2OIuID78GGuT2/Ndb49ZX8K4i9VfRJ4EiBROh/QnbpTw6jevd5UDaMw\nL5qu6TUU5sbiilLaJ9ZRWhxFYW7j71ZTlOd8d195Fuc7/62pdp6TnDPVeZhekBtD6fZoqiqiqKqA\n75Z3oPegijYT1Jwayp7r1lQNpd7iuZ24fHZOg20XXLWNC67aBsCd0w+le2+f7hrCTrB+T/uyo3DP\n/vde6sKs538I0JkEmBKo16S20LDRsbtnW72OwGDgI8/I22nAPBE5u7nGAl+6dLQZ679OIKNXNak9\nqoiOcTNm0g6WLWz4LGjZwiTG/cp5/nPSWTv45pMOgLBsYRJjJu0gJtZNao8qMnpVs/6rhGbz7JxS\n/4euHD+hhB/XOw+QP1uQxBHDd+GKUuLauRlwTDnZG9tGQAPof3Q5W36IIy87lppq4aO5h3Dc6aV7\npcveGMfOkmgGDdszyVhdHZQWO1WzTWvi+WFtPMeeXNZqZQ+kYPyemrPn9wTHT9zzewpLgXmmtgLo\nKyK9RCQWmAzM230I1RJVTVbVw1T1MGAZ0GxAgwibod1dJ8y5JYPZL2/CFQULX+3MTxvi+e0NeWz4\nph3LFiax4JXO3PhQNs9+upayHVHMnnYoAD9tiGfJO5148qP11NUJj9ycsXvC1qbyBLjpkWySutQi\nAt+vjuehm7oDsDkrnpUfdeTxRetRt7Dg5c78tL5daC7KfoiKhsv/lsPNF/TGXSecPrmYw/pX8q97\n0uh3VDmjxjsBbvHcQzh50na8p6+oqxGu+0VfABI61nHTw9lEtdFfWbB+TzMf/YkjR+0kqXMtL65c\nwwv3ppL5Shd+f2suhx9RgSpsy4nloRu7h/L0mxWI1k9VrRWRGUAmTpeOZ1R1tYjMAlaq6rzmc9hX\n2YI0PoiIvILzJkIysA24TVX/2dx3EqWzjpTTglKeSJC59etQFyHsjU8/OtRFCGvLdRGlWnxA945x\nPXpo96uv8Sntpuuv+2J/n6ntL19ekxKc4bx7q+osEekJpKnq5819T1XPD1AZjTHhpo2/JvUoMAqo\nD1JlOB3mjDEHIVHfl1Dw5WnHSFUdKiJfAajqds9DPWPMwaqNDhJZr8bzOoMCiEhXQvaqqjEmHITz\nIJG+3H4+BLwFpIjI33CGHZod1FIZY8JbGM8m5cu8ny+JyBc4ww8J8HNVtRnajTlYhfB5mS98af3s\nCZQD73hvU9XsYBbMGBPG2nJQA95lzwQs8UAvYD1wRBDLZYwJYxLGT9V9uf0c4r3ueWt+etBKZIwx\nB8DvF1hU9UsRGRmMwhhj2oi2fPspItd6rbqAocDWfSQ3xkS6tt5QgDP8R71anGds/wlOcYwxbUJb\nDWqeTrcdVfX6ViqPMaYtaItBTUSiPUODnNCaBTLGhDeh7bZ+fo7z/OxrEZkHvA7sqt+pqm8GuWzG\nmHAUAc/U4oEinDkJ6vurKWBBzZiDVRsNaimels9V7Alm9cL4lIwxQRfGEaC5oBYFOAOu7y2MT8kY\nE2xt9fYzV1VntVpJjDFtR4CCmohMAB7EqUQ9rap3Ndo/FbgcqAN2AlMaz+DeWHNDD4XvKHDGmNBR\np/XTl6U5ni5jc4CJwCDgfBEZ1CjZy6o6RFWPBu7BmQe0Wc0FNZsBxRjTtMCMpzYCyFLVTapaDbwK\nTGpwGFXvuRnb+5Jrc5MZF7dYJGPMQSlAz9QygM1e6znAXu+Vi8jlwLVALE4vjGZF1GTGxphW4ntN\nLVlEVnotU/w+lOocVT0cuAm4taX0bXSaWWNMyPg3VHdhM/N+bgF6eK1392zbl1eBx1o6oNXUjDF+\nEQI2Rd4KoK+I9PLMUDcZaDAru4j09Vo9E9jYUqZWUzPG+C0Qz9Q875bPADJxunQ8o6qrRWQWsFJV\n5wEzRGQsUANsB37XUr4W1Iwx/gtQPzVVnQ/Mb7Ttz16fr/I3Twtqxhj/tdE3CowxZm8RMEqHMcY0\nZEHNGBNJ2uogkSbMnHHM6aEuQtiL7ma9lJojBYH5k7fbT2NM5PCv822rs6BmjPGfBTVjTKSof6Mg\nXFlQM8b4TdzhG9UsqBlj/GPP1IwxkcZuP40xkcWCmjEmklhNzRgTWSyoGWMihtprUsaYCGL91Iwx\nkUfDN6pZUDPG+M1qasaYyBHmnW9tnBZjjN/E7dvSYj4iE0RkvYhkicjMJvZfKyJrRORbEVkkIoe2\nlKcFNWOM3wIR1EQkCpgDTAQGAeeLyKBGyb4ChqnqkcAbwD0tlc2CmjHGP4rTUODL0rwRQJaqblLV\napzJiic1OJTqh6pa7lldhjPhcbMsqBlj/ObHZMbJIrLSa5nilU0GsNlrPcezbV9+D7zXUtmsocAY\n4z/fGwoKVXXYgR5ORC4EhgEnt5TWgpoxxi8B7Hy7Bejhtd7ds63h8ZwZ2m8BTlbVqpYytaBmjPGP\naqAGiVwB9BWRXjjBbDJwgXcCETkGeAKYoKr5vmRqz9SMMf5TH5fmslCtBWYAmcBa4N+qulpEZonI\n2Z5kfwc6AK+LyNciMq+lollNzRjjt0C9UaCq84H5jbb92evzWH/ztKBmjPGPAjZHgTEmooRvTLOg\nZozxn73QboyJKDZFnjEmcoT5KB0W1IwxfnE634ZvVLOgZozxn81RYIyJJFZTa2XX3pfNyLFl7CiM\n5rJT+++1f9T4En57Qx6qUFcrPH5bOqs/7+DHEZRpd2xlxKmlVFa4uPeaHmR9lwDA/M3f8OO6eADy\nt8Ry+0W9AnFKreLY4wu57Ib1uFxK5tsZvP5sw7JHx7i5/o5V9BlYSllJDHfedCT5ue1I6VbBE28u\nJecn5xqs/y6JR/7mDIv128uzOO2srXRIrOWXJ5za6ucUSMeOKmDK9etwRSkL3+7O68/1brA/OsbN\ndbO+o8/AEspKYrlr5lG7r8/jb3zClp/aA7DuuyTm3HkE7RJquefpz3d/v0tqJR/O78ZT9w5s1fPy\n28H6TE1EegDPA6k4l+BJVX0wWMfztvC1zsx7NpkbHtzc5P6vPu7AZ5n9AKHXwApueeInLh09wOf8\nh59aRkavKi4+YQADhpZzxZ1buOqsvgBUV7qYPm7vQBruXC5l+sx13DJtKIXb4nngpeUsW9yVzZv2\nBPvxP9/CzrJoLp10IqPH53HJVRu5a+aRAOTmtOOKyaP2ynf5kmTeea0HT8/9tNXOJRhcLmXazLXc\nOn0Yhdviuf+Fz1i2OIXNP3hfnxx2lkbzh5+PZvTpuVx85Qbu/uNRAOTmJHDFBcc3yLOiPLrBtgdf\n/Iyl/0ttnRM6IAF79zMogvnuZy1wnaoOAo4DLm9iVMugWLW8A2Xb9x2vK8ujcB53QnyCu8FYdudM\ny+eh+Rt47IP1/Ob6vCa/P2p8CR+8cQggrPuyPe2T6uicUhPAM2h9/QaXsHVzAnlbEqitdbEkM41R\nYwoapDluTAEfvJMOwCcfpHDUiGJa+id7/Xed2F4YF6xit5p+RzS6Pgu7cdyYhu9Xjzw5n0X/dYYD\n+2RRKkeNKMLXKk16z10kHVLN6q8OCXTRgyMwg0QGRdBqaqqaC+R6PpeJyFqcAeDWBOuY/jh+QgmX\n3JxLpy61/Om3zm3W0JOdGtiVZ/RFBP7y3A8MHrmTVcsb3pomp9VQsDVm93rh1hi6pNVQnB9DbJyb\nh9/bQF2t8NqcFD5bkNSq57W/uqRUUbhtT/Ap3BZH/8GljdJUUpDn3Fq761yU74wmsZMTzNMyKnj4\nlWWU74ri+Tl92s4fp4+6pFRSuC1+93rhtnj6D97RME3XKgq27fv6PPTSUsp3RfPCo31Z/XXD63Py\n+Fw+fj+N+n9sw5pNZgwichhwDLC8NY7ni6ULkli6IInBI3fyuxvzmHne4Rx7chlDTy7j0fc3ANAu\nwU1G76q9glpzfjNiEEV5MaT1rOLu17/nx7Xx5P7U9msqzSkujON3E0+irCSWPgNL+dN9XzP1nOOp\n2BWRj2z9VlwYx0Vnjnauz4ASbr33a6ade0KD6zP69Dzu/dOQEJbSTwdzQ4GIdAD+A1ytqqVN7J8C\nTAGIJyHYxdnLquUdSOu5mcTOtQjw2sOpzH+xS4M0P7uokIm/LgLg1gt7U5gXQ9f0Pbebyek1FOU5\nNbf6/+Zlx/Ht0g4cPriiTQS1ovw4klP3jL+XnFpFUUFcozTxdE2rpCg/HleUm4QOtZTuiAGEspJY\nALLWJpKbk0D3Q3excU3bqH5epRwAAAlCSURBVKX6oig/nuTUyt3ryamVFBXEN0xTEEfX1Bauz7ok\ncnPakdFzF1lrnevTq28pUVFK1ro2dL3CN6YFdzw1EYnBCWgvqeqbTaVR1SdVdZiqDouhdf740w+r\nov7/Sp8h5cTEuiktjmLl4o6Mn1xMfEIdAF3SakjqUsM7zyUzfVx/po/rT/G2GJYtTGLsOdsBZcDQ\nXZSXuijOj6FDUi0xsU69PLFzLUcM30X2hvh9lCK8bFidSHrPclLTK4iOdjN6fB7LPuraIM3yxV0Z\n+7OtAJw4Np9vV3QGhMRDqnG5nOuZllFOes9ycnNa/x+oYNqwJpGMHuWkppc71+f0XJYvTmmQZvni\nFE47yxm49cTTtu25Pp32vj55W9rt/t7JE/JYnNmt1c4lEMTt9mkJhWC2fgrwT2Ctqt4XrOM0Zeaj\nP3HkqJ0kda7lxZVreOHeVKKjnR/Vuy8kc+KZJYw9p5jaWqGqwsXsaYcCwpeLO9KzTyUPvJMFQMUu\nF/dc0ZOSoob5f76oI8NPK+XZpeuo8nTpAOjZt4or785B3SAueG1OCtkb20ZQc9e5eOzu/vz10S9x\nuZSFc9PJ3tSBC6dlsXFNIssXp5D5djrX/3UVT8/9hLLSGO6e6dwuDRm6nQunfU9traBu4ZG/DWRn\nqVNjveSqDYyZmEdcfB3PL1hC5lsZvPTE4aE81f3irnPx2D0DueORL3BFKe/PzXCuz9SNbFyTxPIl\nKSycm8H1d3zHU28voawkhntudlo+Bw8t5sKpWdTVunArzJk9iJ2lsbvzPmlsHrddNTRUp+Y/Jaw7\n34oG6d5YRE4EPga+Y88luNkzKFyTEqWzjpTTglKeSBCVmtJyooOcuGww5+YsLXiNkur8A2qNSGqf\nrscNusyntAtX3v5FICZe8UcwWz8/oU005Rhj/BbGDQX2z5oxxn8B6qcmIhNEZL2IZInIzCb2jxaR\nL0WkVkTO8aVoFtSMMf6pf6bmy9IMEYkC5gATgUHA+U100M8GLgJe9rV41pHIGOO3ALVsjgCyVHUT\ngIi8CkzCq4O+qv7o2efzAa2mZozxk4+3ns7tZ7KIrPRapnhllAF4v6Cd49l2QKymZozxj+JPQ0Fh\nxLR+GmMiWGD6qW0Benitd/dsOyB2+2mM8Zuo+rS0YAXQV0R6iUgsMBlocQb2llhQM8b4LwBdOlS1\nFpgBZAJrgX+r6moRmSUiZwOIyHARyQF+BTwhIqtbKprdfhpj/KMKdYG5//S8YTS/0bY/e31egXNb\n6jMLasYY/4XxGwUW1Iwx/rOgZoyJGAqE8RwFFtSMMX5S0PAde8iCmjHGP0rAGgqCwYKaMcZ/9kzN\nGBNRLKgZYyJH6Ob09IUFNWOMfxQI0aQqvrCgZozxn9XUjDGRI3CvSQWDBTVjjH8U1PqpGWMiir1R\nYIyJKPZMzRgTMVSt9dMYE2GspmaMiRyK1tWFuhD7ZEHNGOMfG3rIGBNxrEuHMSZSKKBWUzPGRAy1\nQSKNMREmnBsKRMOoaVZECoCfQl0OL8lAYagLEcbs+rQs3K7Roara9UAyEJEFOOfli0JVnXAgx/NX\nWAW1cCMiK1V1WKjLEa7s+rTMrlHrsxnajTERxYKaMSaiWFBr3pOhLkCYs+vTMrtGrcyeqRljIorV\n1IwxEcWCmjEmolhQa4KITBCR9SKSJSIzQ12ecCMiz4hIvoisCnVZwpGI9BCRD0VkjYisFpGrQl2m\ng4k9U2tERKKADcA4IAdYAZyvqmtCWrAwIiKjgZ3A86o6ONTlCTci0g3opqpfikhH4Avg5/Ybah1W\nU9vbCCBLVTepajXwKjApxGUKK6q6BCgOdTnClarmquqXns9lwFogI7SlOnhYUNtbBrDZaz0H+0Ga\n/SQihwHHAMtDW5KDhwU1Y4JERDoA/wGuVtXSUJfnYGFBbW9bgB5e690924zxmYjE4AS0l1T1zVCX\n52BiQW1vK4C+ItJLRGKBycC8EJfJtCEiIsA/gbWqel+oy3OwsaDWiKrWAjOATJwHvP9W1dWhLVV4\nEZFXgM+A/iKSIyK/D3WZwswJwG+AU0Xka89yRqgLdbCwLh3GmIhiNTVjTESxoGaMiSgW1IwxEcWC\nmjEmolhQM8ZEFAtqbYiI1Hm6B6wSkddFJOEA8npORM7xfH5aRAY1k3aMiBy/H8f4UUT2mnVoX9sb\npdnp57FuF5Hr/S2jiTwW1NqWClU92jMyRjUw1XuniOzXPK6qemkLI0iMAfwOasaEggW1tutjoI+n\nFvWxiMwD1ohIlIj8XURWiMi3InIZOL3cReQRzzhxHwAp9RmJyEciMszzeYKIfCki34jIIs8L2VOB\nazy1xJNEpKuI/MdzjBUicoLnu11EZKFnDLGnAWnpJETkbRH5wvOdKY323e/ZvkhEunq2HS4iCzzf\n+VhEBgTiYprIYTO0t0GeGtlEYIFn01BgsKr+4AkMJao6XETigE9FZCHOSBH9gUFAKrAGeKZRvl2B\np4DRnrw6q2qxiDwO7FTVf3jSvQzcr6qfiEhPnLcvBgK3AZ+o6iwRORPw5U2DSzzHaAesEJH/qGoR\n0B5YqarXiMifPXnPwJnIZKqqbhSRkcCjwKn7cRlNhLKg1ra0E5GvPZ8/xnm/8Hjgc1X9wbP9dODI\n+udlQBLQFxgNvKKqdcBWEflfE/kfByypz0tV9zVm2lhgkPOKIwCJnhEpRgP/5/nuuyKy3YdzulJE\nfuH53MNT1iLADbzm2f4i8KbnGMcDr3sdO86HY5iDiAW1tqVCVY/23uD5497lvQm4QlUzG6UL5LuH\nLuA4Va1soiw+E5ExOAFylKqWi8hHQPw+kqvnuDsaXwNjvNkztciTCUzzDH2DiPQTkfbAEuA8zzO3\nbsApTXx3GTBaRHp5vtvZs70M6OiVbiFwRf2KiNQHmSXABZ5tE4FDWihrErDdE9AG4NQU67mA+trm\nBTi3taXADyLyK88xRESOauEY5iBjQS3yPI3zvOxLcSZGeQKnRv4WsNGz73mcUTYaUNUCYArOrd43\n7Ln9ewf4RX1DAXAlMMzTELGGPa2wf8EJiqtxbkOzWyjrAiBaRNYCd+EE1Xq7gBGeczgVmOXZ/mvg\n957yrcaGWjeN2CgdxpiIYjU1Y0xEsaBmjIkoFtSMMRHFgpoxJqJYUDPGRBQLasaYiGJBzRgTUf4f\nWq8B2k0qUmoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOTWp3pEg_ab",
        "colab_type": "code",
        "outputId": "088bf154-8b47-48ce-9ee8-fe48b238504f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "# Random Forest Baseline\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "text_rf_clf = Pipeline([\n",
        "    ('vect', CountVectorizer()),\n",
        "    ('tfidf', TfidfTransformer()),\n",
        "    ('clf', RandomForestClassifier(class_weight='balanced', n_estimators=20)), \n",
        "])\n",
        "text_rf_clf.fit(tokenizer.sequences_to_texts_generator(train_text_vec), y_train.argmax(axis=1))\n",
        "predictions = text_rf_clf.predict(tokenizer.sequences_to_texts_generator(test_text_vec)) \n",
        "print('Baseline Accuracy Using RFC: ', (predictions == y_test.argmax(axis = 1)).mean())\n",
        "print('F1 Score:', f1_score(y_test.argmax(axis = 1), predictions, average='weighted'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-fa9cf17835f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'clf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'balanced'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\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     11\u001b[0m ])\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mtext_rf_clf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequences_to_texts_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_text_vec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\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[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext_rf_clf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequences_to_texts_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_text_vec\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     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Baseline Accuracy Using RFC: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\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[0mmean\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;31mNameError\u001b[0m: name 'tokenizer' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Awc12Y4tC4k_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "_ = plot_confusion_matrix(text_rf_clf,y_test.argmax(axis = 1), predictions, display_labels=encoder.classes_)\n",
        "disp.ax_.set_title('Confusion matrix, without normalization')\n",
        "_ = plot_confusion_matrix(text_rf_clf,y_test.argmax(axis = 1), predictions, display_labels=encoder.classes_, normalize=True)\n",
        "disp.ax_.set_title('Confusion matrix, with normalization')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dardy7vBhWhL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " def threshold_search(y_true, y_proba, average = None):\n",
        "    best_threshold = 0\n",
        "    best_score = 0\n",
        "    for threshold in [i * 0.01 for i in range(100)]:\n",
        "        score = f1_score(y_true=y_true, y_pred=y_proba > threshold, average=average)\n",
        "        if score > best_score:\n",
        "            best_threshold = threshold\n",
        "            best_score = score\n",
        "    search_result = {'threshold': best_threshold, 'f1': best_score}\n",
        "    return search_result\n",
        "\n",
        "\n",
        "def train(model, \n",
        "          X_train, y_train, X_test, y_test, \n",
        "          checkpoint_path='model.hdf5', \n",
        "          epcohs = 25, \n",
        "          batch_size = DEFAULT_BATCH_SIZE, \n",
        "          class_weights = None, \n",
        "          fit_verbose=2,\n",
        "          print_summary = True\n",
        "         ):\n",
        "    m = model()\n",
        "    if print_summary:\n",
        "        print(m.summary())\n",
        "    m.fit(\n",
        "        X_train, \n",
        "        y_train, \n",
        "        #this is bad practice using test data for validation, in a real case would use a seperate validation set\n",
        "        validation_data=(X_test, y_test),  \n",
        "        epochs=epcohs, \n",
        "        batch_size=batch_size,\n",
        "        class_weight=class_weights,\n",
        "         #saves the most accurate model, usually you would save the one with the lowest loss\n",
        "        callbacks= [\n",
        "            ModelCheckpoint(checkpoint_path, monitor='val_acc', verbose=1, save_best_only=True),\n",
        "            EarlyStopping(patience = 2)\n",
        "        ],\n",
        "        verbose=fit_verbose\n",
        "    ) \n",
        "    print(\"\\n\\n****************************\\n\\n\")\n",
        "    print('Loading Best Model...')\n",
        "    m.load_weights(checkpoint_path)\n",
        "    predictions = m.predict(X_test, verbose=1)\n",
        "    print('Validation Loss:', log_loss(y_test, predictions))\n",
        "    print('Test Accuracy', (predictions.argmax(axis = 1) == y_test.argmax(axis = 1)).mean())\n",
        "    print('F1 Score:', f1_score(y_test.argmax(axis = 1), predictions.argmax(axis = 1), average='weighted'))\n",
        "    plot_confusion_matrix(y_test.argmax(axis = 1), predictions.argmax(axis = 1), classes=encoder.classes_)\n",
        "    plt.show()    \n",
        "    return m #returns best performing model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABIfXkZ_Xg1W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_coefs(word,*arr): \n",
        "    return word, np.asarray(arr, dtype='float32')\n",
        "\n",
        "def get_embdedings_matrix(embeddings_index, word_index, nb_words = None):\n",
        "    all_embs = np.stack(embeddings_index.values())\n",
        "    print('Shape of Full Embeddding Matrix', all_embs.shape)\n",
        "    embed_dims = all_embs.shape[1]\n",
        "    emb_mean,emb_std = all_embs.mean(), all_embs.std()\n",
        "\n",
        "    #best to free up memory, given the size, which is usually ~3-4GB in memory\n",
        "    del all_embs\n",
        "    if nb_words is None:\n",
        "        nb_words = len(word_index)\n",
        "    else:\n",
        "        nb_words = min(nb_words, len(word_index))\n",
        "    \n",
        "    embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_dims))\n",
        "    found_vectors = 0\n",
        "    words_not_found = []\n",
        "    for word, i in tqdm(word_index.items()):\n",
        "        if i >= nb_words: \n",
        "            continue\n",
        "        embedding_vector = None\n",
        "        if word in embeddings_index:\n",
        "            embedding_vector = embeddings_index.get(word)\n",
        "        elif word.lower() in embeddings_index:\n",
        "            embedding_vector = embeddings_index.get(word.lower())\n",
        "        # for twitter check if the key is a hashtag\n",
        "        elif '#'+word.lower() in embeddings_index:\n",
        "            embedding_vector = embeddings_index.get('#'+word.lower())\n",
        "            \n",
        "        if embedding_vector is not None: \n",
        "            found_vectors += 1\n",
        "            embedding_matrix[i] = embedding_vector\n",
        "        else:\n",
        "            words_not_found.append((word, i))\n",
        "\n",
        "    print(\"% of Vectors found in Corpus\", found_vectors / nb_words)\n",
        "    return embedding_matrix, words_not_found"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPFzuWArXZwm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_glove(word_index):\n",
        "#     print('Loading Glove')\n",
        "    embed_file_path = '/content/drive/My Drive/Colab Notebooks/models/glove.840B.300d.txt'\n",
        "    embeddings_index = dict(get_coefs(*o.split(\" \")) for o in tqdm(open(embed_file_path)))\n",
        "    print(\"Built Embedding Index:\", len(embeddings_index))\n",
        "    return get_embdedings_matrix(embeddings_index, word_index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9Lv5sP9DiWr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def model_3(embed_matrix):\n",
        "    \"\"\"\n",
        "    Extends model 1c, will be trained with multiple embeddings\n",
        "    \"\"\"\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(input_dim = embed_matrix.shape[0], output_dim = embed_matrix.shape[1], input_length = MAX_SEQ_LEN,  weights=[embed_matrix], trainable=False))\n",
        "    model.add(SpatialDropout1D(0.3))\n",
        "    model.add(Bidirectional(LSTM(128, dropout=0.25, recurrent_dropout=0.25, return_sequences=True)))\n",
        "    model.add(Conv1D(64, 4))\n",
        "    model.add(GlobalMaxPool1D())\n",
        "    model.add(Dense(64, activation='relu'))\n",
        "    model.add(Dense(3, activation='softmax'))\n",
        "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPxxcKYvWEa-",
        "colab_type": "code",
        "outputId": "55162117-4954-4107-b7e4-64a3606717ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "b5ddd1c428544473b760875f4e5b4ddf",
            "22ce51d9c5794078b6fc7e8f2b2170eb",
            "20e46508bcfe4fa5a44882430231c24c",
            "358399cdd9704469ba4b4ebc8b67a227",
            "fdc8abe93221438d85aca6709d2cc419",
            "a0d12f705b3d44a5ae1a8d81686c1e98",
            "1ad8ef3aeff047b6b3e7432ab1154b36",
            "1c7dd575137544859d4889265575bb71",
            "7288266837ae4de7bdc0059ffa05aa38",
            "b88f6d850fdc4e68873574c68126e6ff",
            "cc3a1bb0ef1a4e26a36c41e2bd476908",
            "1ac5a1501bf24017bf8be7968c743746",
            "96b65db84ee2487589c496a29b482713",
            "65a3994a78234e05b48a6646c7523124",
            "6fbe051f03554432ab21881815c17ff9",
            "6c26ea794e4b4005ab5bf4aadf3c603f"
          ]
        }
      },
      "source": [
        "print('Loading Glove Model...')\n",
        "glove_embed_matrix, words_not_found =  load_glove(tokenizer.word_index)\n",
        "print(\"Model 3 w/ Glove Embedding\")\n",
        "_ = train(lambda : model_3(glove_embed_matrix), \n",
        "           train_text_vec,\n",
        "           y_train,\n",
        "           test_text_vec,\n",
        "           y_test,\n",
        "           class_weights=cws,\n",
        "           fit_verbose=0,\n",
        "           print_summary = False\n",
        "\n",
        "          )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading Glove Model...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b5ddd1c428544473b760875f4e5b4ddf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Built Embedding Index: 2196017\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Shape of Full Embeddding Matrix (2196017, 300)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7288266837ae4de7bdc0059ffa05aa38",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=403877), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "% of Vectors found in Corpus 0.3088762172641671\n",
            "Model 3 w/ Glove Embedding\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.96320, saving model to model.hdf5\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.96320 to 0.97037, saving model to model.hdf5\n",
            "\n",
            "Epoch 00003: val_acc improved from 0.97037 to 0.97044, saving model to model.hdf5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jj9wrJFwDgw",
        "colab_type": "code",
        "outputId": "1de84f39-a252-4d54-a35b-76bbaba77e6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "738759a5b2384e16978018c450bd9782",
            "af456a960d49409da2b6db62a8826440",
            "80b29585b9b34c059e3862e6930e7bc6",
            "6ee9c3a93210411bb67aac4e3089128f",
            "2bbd9e502fb94176bd7d651df8021e6f",
            "43ba7c6ab27a43dc9085c9738a9b4813",
            "2380781d63894dcd82ddd5db3ce1d58e",
            "72b0ab742a024555bf334f6dea8deea2",
            "08f614013a194e8f8c137c35c7bfba3c",
            "9dfdc370edc3477eb177e824dc1e5ff1",
            "f17d8cac742449c0b118442b08c1f218",
            "d8cb857d2ecf4d0f9229aba1c129e45b",
            "7ffb9b239d8d4777ab6eefe3a8e7294d",
            "4b08a55b8e1f4a09b8ad308fc3e51f68",
            "f8b20d0aa2564af084c6f65bde745658",
            "ea2d56754c5a47dd8394235abba729ab"
          ]
        }
      },
      "source": [
        "print('Loading Glove Model...')\n",
        "glove_embed_matrix, words_not_found =  load_glove(tokenizer.word_index)\n",
        "print(\"Model 3 w/ Glove Embedding\")\n",
        "_ = train(lambda : model_3(glove_embed_matrix), \n",
        "           train_text_vec,\n",
        "           y_train,\n",
        "           test_text_vec,\n",
        "           y_test,\n",
        "           class_weights=cws,\n",
        "           fit_verbose=0,\n",
        "           print_summary = False\n",
        "\n",
        "          )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading Glove Model...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "738759a5b2384e16978018c450bd9782",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Built Embedding Index: 2196017\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Shape of Full Embeddding Matrix (2196017, 300)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "08f614013a194e8f8c137c35c7bfba3c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=403877), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "% of Vectors found in Corpus 0.3088762172641671\n",
            "Model 3 w/ Glove Embedding\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.96303, saving model to model.hdf5\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.96303 to 0.97041, saving model to model.hdf5\n",
            "\n",
            "Epoch 00003: val_acc improved from 0.97041 to 0.97066, saving model to model.hdf5\n",
            "\n",
            "Epoch 00004: val_acc improved from 0.97066 to 0.97367, saving model to model.hdf5\n",
            "\n",
            "Epoch 00005: val_acc improved from 0.97367 to 0.97446, saving model to model.hdf5\n",
            "\n",
            "Epoch 00006: val_acc improved from 0.97446 to 0.97515, saving model to model.hdf5\n",
            "\n",
            "Epoch 00007: val_acc improved from 0.97515 to 0.97582, saving model to model.hdf5\n",
            "\n",
            "Epoch 00008: val_acc improved from 0.97582 to 0.97612, saving model to model.hdf5\n",
            "\n",
            "Epoch 00009: val_acc improved from 0.97612 to 0.97614, saving model to model.hdf5\n",
            "\n",
            "\n",
            "****************************\n",
            "\n",
            "\n",
            "Loading Best Model...\n",
            "157896/157896 [==============================] - 153s 967us/step\n",
            "Validation Loss: 0.0738601441234075\n",
            "Test Accuracy 0.9761425241931397\n",
            "F1 Score: 0.9760744268090268\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-4777782e5657>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m            \u001b[0mclass_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcws\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m            \u001b[0mfit_verbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m            \u001b[0mprint_summary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m           )\n",
            "\u001b[0;32m<ipython-input-13-5a53d11bac50>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, X_train, y_train, X_test, y_test, checkpoint_path, epcohs, batch_size, class_weights, fit_verbose, print_summary)\u001b[0m\n\u001b[1;32m     45\u001b[0m    \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test Accuracy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\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[0mmean\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     46\u001b[0m    \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'F1 Score:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'weighted'\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---> 47\u001b[0;31m    \u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoder\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[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m    \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\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     49\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mm\u001b[0m \u001b[0;31m#returns best performing model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plot_confusion_matrix' is not defined"
          ]
        }
      ]
    }
  ]
}