regression-last-execution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1875f256-ec28-44c7-abb4-62003ad580f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from tqdm import tqdm\n",
    "from glob import glob\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, PolynomialFeatures\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression, ElasticNet, Ridge, Lasso, RANSACRegressor\n",
    "from sklearn.model_selection import cross_val_score, cross_validate, KFold, StratifiedKFold, train_test_split, GridSearchCV\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from yellowbrick.regressor import PredictionError, ResidualsPlot\n",
    "from feature_extraction import extract_features_from_images\n",
    "from sklearn.cross_decomposition import PLSRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "47ee1c44-3104-47c6-8ad5-90e632b7b483",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.style.use('default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8c6fe0d-54ec-4e5c-8271-e63bd40823a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def handle_model_evaluate(true, predicted, title_column=\"\"):\n",
    "\n",
    "    mae = mean_absolute_error(true, predicted)\n",
    "    mse = mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(mse)\n",
    "    r2_square = r2_score(true, predicted)\n",
    "    mape = np.mean(np.abs((true - predicted) / true)) * 100\n",
    "\n",
    "    return pd.DataFrame(\n",
    "        {\n",
    "            f\"{title_column}\": [mae, mse, rmse, mape, r2_square],\n",
    "        },\n",
    "        index=[\"MAE\", \"MSE\", \"RMSE\", \"MAPE\", \"R-squared\"],\n",
    "    )\n",
    "\n",
    "    \n",
    "def show_model_performance(model, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    train = handle_model_evaluate(y_train, model.predict(X_train), \"Treino\")\n",
    "    test  = handle_model_evaluate(y_test, model.predict(X_test), \"Teste\")\n",
    "    \n",
    "    display(pd.concat([train, test], axis=1))\n",
    "    \n",
    "    \n",
    "def run_all_algs(X_train, y_train, X_test, y_test):\n",
    "\n",
    "    models = {\n",
    "        \"GradientBoostingRegressor\": GradientBoostingRegressor(),\n",
    "        # \"AdaBoostRegressor\": AdaBoostRegressor(),\n",
    "        \"LinearRegression\": LinearRegression(),\n",
    "        \"LassoRegression\": Lasso(),\n",
    "        \"RidgeRegression\": Ridge(),\n",
    "        \"RandomForestRegressor\": RandomForestRegressor(),\n",
    "        # \"ElasticNet\": ElasticNet(),\n",
    "        # \"[RANSACRegressor]\": RANSACRegressor(),\n",
    "        # \"[PLSRegression]\": PLSRegression(n_components=2),\n",
    "        \"PCARegression\": make_pipeline(PCA(n_components=2), LinearRegression()),\n",
    "        \"PolyRegression\": make_pipeline(PolynomialFeatures(degree=3), Lasso()),\n",
    "        # \"XGBRegressor\": XGBRegressor()\n",
    "    }\n",
    "    \n",
    "    for name, model in models.items():\n",
    "\n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        print(\"\\n\",\"\\b\"+name)\n",
    "        \n",
    "        show_model_performance(model,\n",
    "                               X_train,\n",
    "                               y_train,\n",
    "                               X_test,\n",
    "                               y_test)\n",
    "\n",
    "        print(\"_\" * 20)\n",
    "    \n",
    "def print_evaluate(true, predicted, title_column=\"\"):  \n",
    "    \n",
    "    mae = mean_absolute_error(true, predicted)\n",
    "    mse = mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(mse)\n",
    "    r2_square = r2_score(true, predicted)\n",
    "    mape = np.mean(np.abs((true - predicted) / true)) * 100\n",
    "    \n",
    "    print(pd.DataFrame({\n",
    "        f\"{title_column}\": [\n",
    "            mae, \n",
    "            mse, \n",
    "            rmse,\n",
    "            mape,\n",
    "            r2_square],\n",
    "    },\n",
    "        index=[\n",
    "            \"MAE\",\n",
    "            \"MSE\",\n",
    "            \"RMSE\",\n",
    "            \"MAPE\",\n",
    "            \"R-squared\"\n",
    "        ]))\n",
    "\n",
    "    \n",
    "def ApplyesKFoldV2(x, y, k=5):\n",
    "    metrics = [\n",
    "        \"r2\",\n",
    "        \"neg_mean_absolute_error\",\n",
    "        \"neg_mean_squared_error\",\n",
    "        \"neg_root_mean_squared_error\",\n",
    "        \"neg_mean_absolute_percentage_error\"    \n",
    "    ]\n",
    "    \n",
    "    kfold  = KFold(n_splits=k, shuffle=True, random_state=0)\n",
    "\n",
    "    models = {\n",
    "        \"GradientBoostingRegressor\": GradientBoostingRegressor(),\n",
    "        # \"AdaBoostRegressor\": AdaBoostRegressor(),\n",
    "        \"LinearRegression\": LinearRegression(),\n",
    "        \"LassoRegression\": Lasso(),\n",
    "        \"RidgeRegression\": Ridge(),\n",
    "        \"RandomForestRegressor\": RandomForestRegressor(),\n",
    "        # \"ElasticNet\": ElasticNet(),\n",
    "        # \"RANSACRegressor\": RANSACRegressor(),\n",
    "        # \"PLSRegression\": PLSRegression(n_components=2),\n",
    "        \"PCARegression\": make_pipeline(PCA(n_components=2), LinearRegression()),\n",
    "        \"PolyRegression\": make_pipeline(PolynomialFeatures(degree=2), Lasso()),\n",
    "    }\n",
    "\n",
    "    results = {}\n",
    "    \n",
    "    for name, model in models.items():\n",
    "        results[name] = cross_validate(model, x, y, cv=kfold, scoring=metrics)\n",
    "        \n",
    "    for name, cv_results in results.items():\n",
    "        print(f\"[{name}]\")\n",
    "        display(pd.DataFrame({\n",
    "            f\"Média\": [\n",
    "                cv_results['test_r2'].mean(), \n",
    "                -cv_results['test_neg_mean_absolute_error'].mean(),\n",
    "                -cv_results['test_neg_mean_squared_error'].mean(),\n",
    "                -cv_results['test_neg_root_mean_squared_error'].mean(),\n",
    "                -cv_results['test_neg_mean_absolute_percentage_error'].mean()\n",
    "            ],\n",
    "            \"Desvio Padrão\": [\n",
    "                cv_results['test_r2'].std(),\n",
    "                cv_results['test_neg_mean_absolute_error'].std(),\n",
    "                cv_results['test_neg_mean_squared_error'].std(),\n",
    "                cv_results['test_neg_root_mean_squared_error'].std(),\n",
    "                cv_results['test_neg_mean_absolute_percentage_error'].std()\n",
    "            ]\n",
    "        },\n",
    "            index=[\"R-squared\", \"MAE\", \"MSE\", \"RMSE\", \"MAPE\"],\n",
    "        ))\n",
    "\n",
    "\n",
    "def apply_pca_reduce(data, n=None):\n",
    "    \n",
    "    scaler = StandardScaler()\n",
    "    X = scaler.fit_transform(data)\n",
    "    \n",
    "    pca = PCA(n_components=data.shape[-1]).fit(X)\n",
    "    \n",
    "    cumsum = np.cumsum(pca.explained_variance_ratio_)\n",
    "    dim = np.argmax(cumsum >= 0.90) + 1\n",
    "    \n",
    "    print('Para preservar 90% da variance é necessário ' + str(dim))\n",
    "        \n",
    "    pca_refit = PCA(n_components=n if n else dim).fit(X)\n",
    "    print('Ratio: ', pca_refit.explained_variance_ratio_)\n",
    "    \n",
    "    return pca_refit.transform(X)\n",
    "\n",
    "def corr_view(df, w=8, h=6):\n",
    "    corrmat = df.corr()\n",
    "\n",
    "    _, ax = plt.subplots(figsize=(w, h))\n",
    "\n",
    "    sns.heatmap(corrmat, linewidths=5, annot=True);\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    return corrmat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de86c219-8b67-4c20-b13a-b9612fad42d0",
   "metadata": {},
   "source": [
    "## Extraindo Features das imagens mascaras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d86390b-aa3f-4a6b-8e29-145fcd64be18",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataset(base_path):\n",
    "    \n",
    "    *_, base_path_last = base_path.split(\"/\")\n",
    "    features_file_name = f\"{base_path_last}_features.csv\"\n",
    "    \n",
    "    if not os.path.isfile(features_file_name):\n",
    "        extract_features_from_images(\n",
    "            images_arr=[],\n",
    "            images_path=f\"{base_path}/masks\",\n",
    "            output_file_name=features_file_name,\n",
    "            resize_shape=(224, 224))\n",
    "    \n",
    "    weights = pd.read_csv(f\"{base_path}/annotations.csv\")\n",
    "    features = pd.read_csv(features_file_name)\n",
    "\n",
    "    weights.index = weights.img\n",
    "    features.index = features.label\n",
    "\n",
    "    weights = weights.drop('img', axis=1)\n",
    "    features = features.drop('label', axis=1)\n",
    "    \n",
    "    data = pd.concat([weights, features],\n",
    "                     axis=1,\n",
    "                     ignore_index=False, \n",
    "                     verify_integrity=True)\n",
    "\n",
    "    data = data.reset_index()\n",
    "    \n",
    "    return data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0615dc38-dad6-417b-bf5e-ccd1985de297",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_dataset(\"data/coletas/combined\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbb2d989-5fee-496d-93b9-e9f7e19955e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'105.0 kg'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"{data.weight.sum() // 1000} \" + \"kg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd81eed5-ec2c-499d-b818-635cb7fb7a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.weight /= 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "479c6b3c-7e79-41c5-aa32-37ba684287e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5ad1ce4-6d15-48a6-a510-9b19a1500f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.specie = enc.fit_transform(data.specie.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef083dbd-bb9a-4b3b-b6f1-a31eda9948a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_unet_dataset(df):\n",
    "    \n",
    "    weights = df[[\"weight\", \"length\", \"specie\", \"width\"]]\n",
    "    weights.index = df[\"index\"]\n",
    "\n",
    "    features_file_name = \"features_from_unet_predictions.csv\"\n",
    "    features = pd.read_csv(features_file_name)\n",
    "    \n",
    "    features.index = features.label\n",
    "\n",
    "    features = features.drop('label', axis=1)\n",
    "    \n",
    "    data = pd.concat([weights, features],\n",
    "                     axis=1,\n",
    "                     ignore_index=False, \n",
    "                     verify_integrity=True)\n",
    "\n",
    "    data = data.reset_index()\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fba9d71-2b9d-40be-a4b2-cc41be12177f",
   "metadata": {},
   "source": [
    "### Ajustando Features Extraídas da U-net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "235cd318-df00-45ae-ba17-e9957461604b",
   "metadata": {},
   "outputs": [],
   "source": [
    "_, X_test = train_test_split(data, test_size=0.3, random_state=0)\n",
    "unet_testing_features = load_unet_dataset(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9558120a-5fc4-4ab6-b93c-255d4f5b887a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 100x100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = corr_view(data, w=1, h=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3387c2f9-f71f-4f43-a949-431452cc6573",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_names = list(dict(corrmat[corrmat.weight > 0.85].weight).keys())\n",
    "features_names.append(\"specie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7261dbd7-a9bc-47df-b56d-591751b925bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "      <th>length</th>\n",
       "      <th>area</th>\n",
       "      <th>bbox_area</th>\n",
       "      <th>convex_area</th>\n",
       "      <th>equivalent_diameter</th>\n",
       "      <th>filled_area</th>\n",
       "      <th>major_axis_length</th>\n",
       "      <th>moments-0-0</th>\n",
       "      <th>moments-0-1</th>\n",
       "      <th>...</th>\n",
       "      <th>moments-2-1</th>\n",
       "      <th>moments-2-2</th>\n",
       "      <th>moments-2-3</th>\n",
       "      <th>moments_central-0-0</th>\n",
       "      <th>moments_central-0-2</th>\n",
       "      <th>moments_central-2-0</th>\n",
       "      <th>moments_central-2-2</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>feret_diameter_max</th>\n",
       "      <th>specie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>220.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5867</td>\n",
       "      <td>8908</td>\n",
       "      <td>6372</td>\n",
       "      <td>86.429719</td>\n",
       "      <td>5867</td>\n",
       "      <td>122.598160</td>\n",
       "      <td>5867.0</td>\n",
       "      <td>344885.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.695134e+08</td>\n",
       "      <td>3.385144e+10</td>\n",
       "      <td>2.780223e+12</td>\n",
       "      <td>5867.0</td>\n",
       "      <td>5.510758e+06</td>\n",
       "      <td>1.470126e+06</td>\n",
       "      <td>7.711683e+08</td>\n",
       "      <td>340.048773</td>\n",
       "      <td>132.230859</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>545.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10098</td>\n",
       "      <td>15272</td>\n",
       "      <td>10689</td>\n",
       "      <td>113.389474</td>\n",
       "      <td>10098</td>\n",
       "      <td>154.971729</td>\n",
       "      <td>10098.0</td>\n",
       "      <td>818043.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.222528e+09</td>\n",
       "      <td>2.201669e+11</td>\n",
       "      <td>2.427110e+13</td>\n",
       "      <td>10098.0</td>\n",
       "      <td>1.513501e+07</td>\n",
       "      <td>4.568750e+06</td>\n",
       "      <td>3.687127e+09</td>\n",
       "      <td>438.374675</td>\n",
       "      <td>166.364059</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1025.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13583</td>\n",
       "      <td>20580</td>\n",
       "      <td>14173</td>\n",
       "      <td>131.508223</td>\n",
       "      <td>13583</td>\n",
       "      <td>180.579492</td>\n",
       "      <td>13583.0</td>\n",
       "      <td>1285297.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.716828e+09</td>\n",
       "      <td>4.027404e+11</td>\n",
       "      <td>4.892637e+13</td>\n",
       "      <td>13583.0</td>\n",
       "      <td>2.755749e+07</td>\n",
       "      <td>8.335128e+06</td>\n",
       "      <td>9.660429e+09</td>\n",
       "      <td>500.114790</td>\n",
       "      <td>196.918765</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>740.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>8521</td>\n",
       "      <td>12864</td>\n",
       "      <td>9204</td>\n",
       "      <td>104.159849</td>\n",
       "      <td>8521</td>\n",
       "      <td>183.209467</td>\n",
       "      <td>8521.0</td>\n",
       "      <td>791315.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.111224e+09</td>\n",
       "      <td>1.270943e+11</td>\n",
       "      <td>1.647843e+13</td>\n",
       "      <td>8521.0</td>\n",
       "      <td>1.787565e+07</td>\n",
       "      <td>1.987899e+06</td>\n",
       "      <td>2.195729e+09</td>\n",
       "      <td>459.948268</td>\n",
       "      <td>192.210822</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>475.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8184</td>\n",
       "      <td>11907</td>\n",
       "      <td>8614</td>\n",
       "      <td>102.079344</td>\n",
       "      <td>8184</td>\n",
       "      <td>138.020198</td>\n",
       "      <td>8184.0</td>\n",
       "      <td>587517.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.239091e+09</td>\n",
       "      <td>1.098955e+11</td>\n",
       "      <td>1.083366e+13</td>\n",
       "      <td>8184.0</td>\n",
       "      <td>9.715353e+06</td>\n",
       "      <td>3.038349e+06</td>\n",
       "      <td>2.008774e+09</td>\n",
       "      <td>380.776695</td>\n",
       "      <td>147.275253</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  length   area  bbox_area  convex_area  equivalent_diameter  \\\n",
       "0   220.0    22.0   5867       8908         6372            86.429719   \n",
       "1   545.0    26.0  10098      15272        10689           113.389474   \n",
       "2  1025.0    31.0  13583      20580        14173           131.508223   \n",
       "3   740.0    37.0   8521      12864         9204           104.159849   \n",
       "4   475.0    24.0   8184      11907         8614           102.079344   \n",
       "\n",
       "   filled_area  major_axis_length  moments-0-0  moments-0-1  ...  \\\n",
       "0         5867         122.598160       5867.0     344885.0  ...   \n",
       "1        10098         154.971729      10098.0     818043.0  ...   \n",
       "2        13583         180.579492      13583.0    1285297.0  ...   \n",
       "3         8521         183.209467       8521.0     791315.0  ...   \n",
       "4         8184         138.020198       8184.0     587517.0  ...   \n",
       "\n",
       "    moments-2-1   moments-2-2   moments-2-3  moments_central-0-0  \\\n",
       "0  4.695134e+08  3.385144e+10  2.780223e+12               5867.0   \n",
       "1  2.222528e+09  2.201669e+11  2.427110e+13              10098.0   \n",
       "2  3.716828e+09  4.027404e+11  4.892637e+13              13583.0   \n",
       "3  1.111224e+09  1.270943e+11  1.647843e+13               8521.0   \n",
       "4  1.239091e+09  1.098955e+11  1.083366e+13               8184.0   \n",
       "\n",
       "   moments_central-0-2  moments_central-2-0  moments_central-2-2   perimeter  \\\n",
       "0         5.510758e+06         1.470126e+06         7.711683e+08  340.048773   \n",
       "1         1.513501e+07         4.568750e+06         3.687127e+09  438.374675   \n",
       "2         2.755749e+07         8.335128e+06         9.660429e+09  500.114790   \n",
       "3         1.787565e+07         1.987899e+06         2.195729e+09  459.948268   \n",
       "4         9.715353e+06         3.038349e+06         2.008774e+09  380.776695   \n",
       "\n",
       "   feret_diameter_max  specie  \n",
       "0          132.230859       3  \n",
       "1          166.364059       4  \n",
       "2          196.918765       0  \n",
       "3          192.210822       2  \n",
       "4          147.275253       4  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[features_names].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "15fba32f-c50a-4276-94cd-7d6b7ed5d48d",
   "metadata": {},
   "outputs": [],
   "source": [
    "unet_testing_features = unet_testing_features[features_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23fcd4f0-002c-4252-872a-63b8ac877240",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.weight\n",
    "X = data[features_names].drop(labels=['weight'], axis=1)\n",
    "l = data['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3e936ab1-9427-4f6c-a238-b2c2d6e8567e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test, index_train, index_test = train_test_split(X.values, \n",
    "                                                                             y.values,\n",
    "                                                                             l.values.tolist(),\n",
    "                                                                             test_size=0.3, \n",
    "                                                                             random_state=0)\n",
    "\n",
    "Unet_y_test = unet_testing_features.weight\n",
    "unet_testing_features = unet_testing_features[features_names].drop(labels=['weight'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0fdedcdb-e9af-4bea-9d8c-2347451c35a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 74)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train), len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2f268823-7f31-4d57-ae5f-3288fe4fcf53",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled, X_test_scaled = scaler.fit_transform(X_train), scaler.transform(X_test)\n",
    "Unet_X_test_scaled = scaler.transform(unet_testing_features.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804b778a-2b42-4c54-8b1c-bd5762955405",
   "metadata": {},
   "source": [
    "# Prevendo para as máscaras geradas pela a unet - Sem normalização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "29431178-7ffa-4e31-b383-d301d11c4d20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "GradientBoostingRegressor\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>5.753149</td>\n",
       "      <td>34.618445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>53.400895</td>\n",
       "      <td>7108.305776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>7.307592</td>\n",
       "      <td>84.310769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>6.286146</td>\n",
       "      <td>8.043995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.999181</td>\n",
       "      <td>0.877678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino        Teste\n",
       "MAE         5.753149    34.618445\n",
       "MSE        53.400895  7108.305776\n",
       "RMSE        7.307592    84.310769\n",
       "MAPE        6.286146     8.043995\n",
       "R-squared   0.999181     0.877678"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "LinearRegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>23.974582</td>\n",
       "      <td>41.854334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>962.880129</td>\n",
       "      <td>8170.057059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>31.030310</td>\n",
       "      <td>90.388368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>37.276645</td>\n",
       "      <td>13.512083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.985241</td>\n",
       "      <td>0.859406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Treino        Teste\n",
       "MAE         23.974582    41.854334\n",
       "MSE        962.880129  8170.057059\n",
       "RMSE        31.030310    90.388368\n",
       "MAPE        37.276645    13.512083\n",
       "R-squared    0.985241     0.859406"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "LassoRegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>26.431473</td>\n",
       "      <td>39.656571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>1171.437241</td>\n",
       "      <td>8451.399781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>34.226265</td>\n",
       "      <td>91.931495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>27.699546</td>\n",
       "      <td>13.043333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.982044</td>\n",
       "      <td>0.854565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Treino        Teste\n",
       "MAE          26.431473    39.656571\n",
       "MSE        1171.437241  8451.399781\n",
       "RMSE         34.226265    91.931495\n",
       "MAPE         27.699546    13.043333\n",
       "R-squared     0.982044     0.854565"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "RidgeRegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>23.950637</td>\n",
       "      <td>41.683038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>962.984248</td>\n",
       "      <td>8140.760631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>31.031987</td>\n",
       "      <td>90.226164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>36.381970</td>\n",
       "      <td>13.413555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.985239</td>\n",
       "      <td>0.859911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Treino        Teste\n",
       "MAE         23.950637    41.683038\n",
       "MSE        962.984248  8140.760631\n",
       "RMSE        31.031987    90.226164\n",
       "MAPE        36.381970    13.413555\n",
       "R-squared    0.985239     0.859911"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "RandomForestRegressor\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>11.310737</td>\n",
       "      <td>35.603135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>381.744587</td>\n",
       "      <td>6778.256631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>19.538285</td>\n",
       "      <td>82.330168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>54.313172</td>\n",
       "      <td>7.797324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.994149</td>\n",
       "      <td>0.883357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Treino        Teste\n",
       "MAE         11.310737    35.603135\n",
       "MSE        381.744587  6778.256631\n",
       "RMSE        19.538285    82.330168\n",
       "MAPE        54.313172     7.797324\n",
       "R-squared    0.994149     0.883357"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "PCARegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>53.294235</td>\n",
       "      <td>69.551406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>5604.668222</td>\n",
       "      <td>13437.109407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>74.864332</td>\n",
       "      <td>115.918546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>249.648657</td>\n",
       "      <td>22.293332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.914090</td>\n",
       "      <td>0.768769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Treino         Teste\n",
       "MAE          53.294235     69.551406\n",
       "MSE        5604.668222  13437.109407\n",
       "RMSE         74.864332    115.918546\n",
       "MAPE        249.648657     22.293332\n",
       "R-squared     0.914090      0.768769"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "PolyRegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>14.071769</td>\n",
       "      <td>31.924466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>324.874632</td>\n",
       "      <td>5085.567821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>18.024279</td>\n",
       "      <td>71.313167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>20.140633</td>\n",
       "      <td>7.897775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.995020</td>\n",
       "      <td>0.912486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Treino        Teste\n",
       "MAE         14.071769    31.924466\n",
       "MSE        324.874632  5085.567821\n",
       "RMSE        18.024279    71.313167\n",
       "MAPE        20.140633     7.897775\n",
       "R-squared    0.995020     0.912486"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n"
     ]
    }
   ],
   "source": [
    "run_all_algs(X_train, \n",
    "             y_train,\n",
    "             X_test,\n",
    "             y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51c5dd7-2f3f-41f4-bc55-860f729a1d6f",
   "metadata": {},
   "source": [
    "# Prevendo os pesos para as márcas previstas pela unet | Normalizado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a311f27-182e-4b6e-8823-c3da02d87ea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "GradientBoostingRegressor\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>5.753149</td>\n",
       "      <td>41.138786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>53.400895</td>\n",
       "      <td>7331.734797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>7.307592</td>\n",
       "      <td>85.625550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>6.286146</td>\n",
       "      <td>10.032335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.999181</td>\n",
       "      <td>0.873833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino        Teste\n",
       "MAE         5.753149    41.138786\n",
       "MSE        53.400895  7331.734797\n",
       "RMSE        7.307592    85.625550\n",
       "MAPE        6.286146    10.032335\n",
       "R-squared   0.999181     0.873833"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "LinearRegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>23.975293</td>\n",
       "      <td>59.529593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>962.880039</td>\n",
       "      <td>9437.755647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>31.030308</td>\n",
       "      <td>97.148112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>37.311182</td>\n",
       "      <td>19.038821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.985241</td>\n",
       "      <td>0.837591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Treino        Teste\n",
       "MAE         23.975293    59.529593\n",
       "MSE        962.880039  9437.755647\n",
       "RMSE        31.030308    97.148112\n",
       "MAPE        37.311182    19.038821\n",
       "R-squared    0.985241     0.837591"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "LassoRegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>31.956953</td>\n",
       "      <td>55.031958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>1804.489268</td>\n",
       "      <td>8736.306219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>42.479280</td>\n",
       "      <td>93.468210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>21.587117</td>\n",
       "      <td>17.528081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.972340</td>\n",
       "      <td>0.849662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Treino        Teste\n",
       "MAE          31.956953    55.031958\n",
       "MSE        1804.489268  8736.306219\n",
       "RMSE         42.479280    93.468210\n",
       "MAPE         21.587117    17.528081\n",
       "R-squared     0.972340     0.849662"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "RidgeRegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>28.597508</td>\n",
       "      <td>55.451863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>1448.371179</td>\n",
       "      <td>8942.838852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>38.057472</td>\n",
       "      <td>94.566584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>15.823772</td>\n",
       "      <td>17.916019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.977799</td>\n",
       "      <td>0.846108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Treino        Teste\n",
       "MAE          28.597508    55.451863\n",
       "MSE        1448.371179  8942.838852\n",
       "RMSE         38.057472    94.566584\n",
       "MAPE         15.823772    17.916019\n",
       "R-squared     0.977799     0.846108"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "RandomForestRegressor\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>11.456047</td>\n",
       "      <td>43.001946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>356.194738</td>\n",
       "      <td>7651.059743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>18.873122</td>\n",
       "      <td>87.470336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>48.031462</td>\n",
       "      <td>10.051445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.994540</td>\n",
       "      <td>0.868338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Treino        Teste\n",
       "MAE         11.456047    43.001946\n",
       "MSE        356.194738  7651.059743\n",
       "RMSE        18.873122    87.470336\n",
       "MAPE        48.031462    10.051445\n",
       "R-squared    0.994540     0.868338"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "PCARegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>37.241035</td>\n",
       "      <td>54.234824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>2470.607808</td>\n",
       "      <td>9209.688890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>49.705209</td>\n",
       "      <td>95.967124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>92.887716</td>\n",
       "      <td>17.147199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.962130</td>\n",
       "      <td>0.841516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Treino        Teste\n",
       "MAE          37.241035    54.234824\n",
       "MSE        2470.607808  9209.688890\n",
       "RMSE         49.705209    95.967124\n",
       "MAPE         92.887716    17.147199\n",
       "R-squared     0.962130     0.841516"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "PolyRegression\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treino</th>\n",
       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>19.584782</td>\n",
       "      <td>49.269408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>622.533633</td>\n",
       "      <td>10944.823711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>24.950624</td>\n",
       "      <td>104.617511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPE</th>\n",
       "      <td>28.739563</td>\n",
       "      <td>19.989758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.990458</td>\n",
       "      <td>0.811657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Treino         Teste\n",
       "MAE         19.584782     49.269408\n",
       "MSE        622.533633  10944.823711\n",
       "RMSE        24.950624    104.617511\n",
       "MAPE        28.739563     19.989758\n",
       "R-squared    0.990458      0.811657"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n"
     ]
    }
   ],
   "source": [
    "run_all_algs(X_train_scaled, \n",
    "             y_train,\n",
    "             Unet_X_test_scaled,\n",
    "             y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3dbcfc4-d0a4-47d5-9084-57d8027428f3",
   "metadata": {},
   "source": [
    "## Polinomial Features -  Qualquer  regressor pode ser adicionado ao pipeline\n",
    "\n",
    "#### Obs: Não executar transformação de features\n",
    "\n",
    "\n",
    "Foi selecionado um dos modelos de regressão que performaram bem para visualizar dua predições"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "455e9bfc-a90d-4a72-8d25-87076e9a560c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Treino\n",
      "MAE         14.115978\n",
      "MSE        323.940446\n",
      "RMSE        17.998346\n",
      "MAPE        17.541936\n",
      "R-squared    0.995035\n",
      "                 Teste\n",
      "MAE          39.016092\n",
      "MSE        5582.740155\n",
      "RMSE         74.717737\n",
      "MAPE          9.595564\n",
      "R-squared     0.903930\n"
     ]
    }
   ],
   "source": [
    "model = make_pipeline(PolynomialFeatures(degree=3), Lasso(10)).fit(X_train, y_train)\n",
    "\n",
    "print_evaluate(y_train, model.predict(X_train), \"Treino\")\n",
    "print_evaluate(y_test, model.predict(unet_testing_features.values), \"Teste\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd8588a-d8c3-4982-9224-f19456f5758b",
   "metadata": {},
   "source": [
    "# Figuras para explicar o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fbcf08db-79f4-4359-8c1c-a06866fe8df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = make_pipeline(PolynomialFeatures(degree=3), Lasso(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "deb8bdc8-75e9-42bd-8acf-9691d575f3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, ax = plt.subplots(figsize=(6, 10))\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
    "\n",
    "\n",
    "def make_plot(ax, model, x, y, title):\n",
    "\n",
    "    model = PredictionError(model, ax=ax).fit(X_train, y_train)\n",
    "    score = model.score(x, y)\n",
    "    model.finalize()\n",
    "\n",
    "    ax.set_xlabel(\"(y) Peso Predito - gramas\")\n",
    "    ax.set_ylabel(\"(ŷ) Peso Real - gramas\")\n",
    "    ax.legend([f\"R² = {round(score, 3)}\", \"Melhor Ajuste\", \"Identidade\"])\n",
    "    ax.set_title(f\"Análise de Erro de Predição - {title}\")\n",
    "\n",
    "    ax.grid(color='gray', \n",
    "            linestyle='--',\n",
    "            linewidth=0.5,\n",
    "            alpha=0.5)\n",
    "    \n",
    "    return score\n",
    "\n",
    "test_score = make_plot(axs[1], \n",
    "                       model,\n",
    "                       unet_testing_features.values,\n",
    "                       y_test, \n",
    "                       title=\"Teste\")\n",
    "\n",
    "train_score = make_plot(axs[0],\n",
    "                        model,\n",
    "                        X_train,\n",
    "                        y_train, \n",
    "                        title=\"Treino\")\n",
    "\n",
    "plt.savefig(\"analise_erro_predicao.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6b5d1a57-042d-4600-9503-326242b16abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizer = ResidualsPlot(model, hist=False).fit(X_train, y_train)\n",
    "\n",
    "test_score = visualizer.score(unet_testing_features.values, y_test)\n",
    "\n",
    "visualizer.finalize()\n",
    "\n",
    "visualizer.ax.legend([f\"Treino R² = {round(train_score, 3)}\", f\"Teste R² = {round(test_score, 3)}\"], loc=\"upper left\")\n",
    "\n",
    "visualizer.ax.set_xlabel(\"(y) Peso Predito - gramas\")\n",
    "visualizer.ax.set_ylabel(\"Resíduos\")\n",
    "visualizer.ax.set_title(\"Análise Residual do Modelo de Regressão\")\n",
    "visualizer.ax.grid(color='gray', \n",
    "                   linestyle='--',\n",
    "                   linewidth=0.5,\n",
    "                   alpha=0.5)\n",
    "\n",
    "plt.savefig(\"analise_residuos_predicao.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d764180c-04ac-4b5e-829c-46cf9f60f176",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}