{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e1bdbd46-1f35-4373-80ec-727f0e26f009",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "327dafe0-68d4-4200-a889-b03bc97a1057",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ced7579-bb9f-4a20-abe2-c0c258ef4073",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data[:, :1]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50359601-4b0d-4a94-a1c8-44a833b8f4e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17fc4619-c5c0-4beb-81df-81617b1c7a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[6.3],\n",
       "        [6.5],\n",
       "        [5.6],\n",
       "        [5.7],\n",
       "        [6.4]]),\n",
       " array([1, 2, 1, 1, 2]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[:5], y_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "510d7a07-7746-4305-96d6-a74bc5a7f144",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "733f81f4-fc25-41a2-8c7d-e6e4abd70143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "267ed05e-7285-4873-ae8c-2396f986bf31",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0dd6f9d6-89d4-461f-9110-601d44126512",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.22816565, 0.91925051, 2.46382623, 1.15093687, 1.76876716,\n",
       "       0.68756415, 0.84202172, 1.84599594, 1.30539444, 0.99647929,\n",
       "       1.5370808 , 0.22419143, 0.76479293, 0.30142022, 0.45587779])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a65cd71-b625-4f31-894d-a4d0403fd1b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 2., 1., 2., 1., 1., 2., 1., 1., 2., 0., 1., 0., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = np.round(y_pred)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef5d8327-7ee8-43a6-b4f9-0785e8467d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b28aa4ac-87ab-45a4-b5c8-e7b125895c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7333333333333333"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, np.round(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d5cf8629-f623-4a3e-9400-8d7f6215383e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "caa6d389-b358-4160-a342-215013c5b2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reg_arr_model_1_dim.joblib']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(model, \"reg_arr_model_1_dim.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0804a91-46d4-4cec-bd60-bb5f022443bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load(\"reg_arr_model_1_dim.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07584f03-f1da-4014-a539-ca0e033a6356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.22816565])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06cf5cbb-0a96-4501-8fa6-bfc680d8aa20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import skops.hub_utils as hub_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4349089-88c9-49d2-8b65-351cabb74fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.1],\n",
       "       [5.7],\n",
       "       [7.7],\n",
       "       [6. ],\n",
       "       [6.8],\n",
       "       [5.4],\n",
       "       [5.6],\n",
       "       [6.9],\n",
       "       [6.2],\n",
       "       [5.8],\n",
       "       [6.5],\n",
       "       [4.8],\n",
       "       [5.5],\n",
       "       [4.9],\n",
       "       [5.1]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a4294f1f-5eeb-460f-a872-ba487a229093",
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -rf /Users/macbookpro/MyProjects/dev/dst\n",
    "!mkdir /Users/macbookpro/MyProjects/dev/dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "41a49678-1a01-439d-8f92-29f1884e5f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "hub_utils.init(\n",
    "    model=\"/Users/macbookpro/MyProjects/dev/reg_arr_model_1_dim.joblib\",\n",
    "    requirements=[\"scikit-learn\", \"numpy\"],\n",
    "    dst=\"/Users/macbookpro/MyProjects/dev/dst\",\n",
    "    task=\"tabular-classification\",\n",
    "    data=x_train[:3]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fe1a1620-66e3-4543-a506-1195ac39831e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skops.card import metadata_from_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e0a6d81-e1c4-4a75-b510-772eb44e924d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "library_name: sklearn\n",
       "tags:\n",
       "- sklearn\n",
       "- skops\n",
       "- tabular-classification\n",
       "widget:\n",
       "  structuredData:\n",
       "    x0:\n",
       "    - 6.3\n",
       "    - 6.5\n",
       "    - 5.6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_from_config(\"/Users/macbookpro/MyProjects/dev/dst/config.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "412fc8e8-ab28-44bf-88be-6cac61c168f1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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   "file_extension": ".py",
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}