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