regression_cleaner_data_syntetic_adjust.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1875f256-ec28-44c7-abb4-62003ad580f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\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, MinMaxScaler, PolynomialFeatures\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, PredictionErrorDisplay\n",
    "from sklearn.linear_model import LinearRegression, ElasticNet, Ridge, Lasso, RANSACRegressor\n",
    "from sklearn.model_selection import cross_val_score, KFold, 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, residuals_plot\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')\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8c6fe0d-54ec-4e5c-8271-e63bd40823a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_model_evaluate(true, predicted, title_column=\"\"):  \n",
    "    \n",
    "    mae = mean_absolute_error(true, predicted)\n",
    "    r2_square = r2_score(true, predicted)\n",
    "    \n",
    "    return pd.DataFrame({\n",
    "        f\"{title_column}\": [\n",
    "            mae, \n",
    "            r2_square],\n",
    "    },\n",
    "        index=[\n",
    "            \"MAE\",\n",
    "            \"R-squared\"\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))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de86c219-8b67-4c20-b13a-b9612fad42d0",
   "metadata": {},
   "source": [
    "## Extraindo Features das imagens mascaras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b7fc56f-66ec-438d-bbf6-2958f9e95c02",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "494028cc-1a4a-47ef-a509-28660f91294d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = load_dataset(\"data/coletas/combined_v2\")\n",
    "test = load_dataset(\"data/coletas/Dia 29-11-2022 - OK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "665d96d0-ab8a-44bb-8a4e-9ed87510d5b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tambaqui    73\n",
       "tilápia     60\n",
       "curimatã    18\n",
       "panga        7\n",
       "traíra       5\n",
       "lambari      3\n",
       "Name: specie, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.specie.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06718504-dee0-47c7-b6f2-03a7708517b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tambaqui    75\n",
       "curimatã     2\n",
       "panga        1\n",
       "tilápia      1\n",
       "Name: specie, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.specie.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "479c6b3c-7e79-41c5-aa32-37ba684287e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = LabelEncoder().fit(train.specie.to_list() + test.specie.to_list())\n",
    "train.specie = enc.transform(train.specie.values)\n",
    "test.specie = enc.transform(test.specie.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ab101b14-679f-4814-837d-ba2aedf05f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "specie                -0.511426\n",
       "bbox-0                -0.369371\n",
       "bbox-1                -0.579059\n",
       "moments_central-1-1   -0.227727\n",
       "moments_central-1-2   -0.347628\n",
       "moments_central-1-3   -0.392254\n",
       "moments_central-2-1   -0.446015\n",
       "moments_central-2-3   -0.003000\n",
       "moments_central-3-1   -0.081582\n",
       "moments_central-3-3   -0.323219\n",
       "orientation           -0.131475\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrmat = train.corr()\n",
    "corrmat[corrmat.weight < 0].weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09d3c680-58e5-41f1-943a-3d3814cf2fd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weight                 1.000000\n",
       "width                  0.772038\n",
       "length                 0.862963\n",
       "area                   0.954241\n",
       "bbox-2                 0.355345\n",
       "bbox-3                 0.597043\n",
       "bbox_area              0.948547\n",
       "centroid-0             0.012084\n",
       "centroid-1             0.013135\n",
       "convex_area            0.951091\n",
       "eccentricity           0.055461\n",
       "equivalent_diameter    0.918355\n",
       "euler_number           0.056988\n",
       "extent                 0.025896\n",
       "filled_area            0.954241\n",
       "major_axis_length      0.885280\n",
       "minor_axis_length      0.818823\n",
       "moments-0-0            0.954241\n",
       "moments-0-1            0.962304\n",
       "moments-0-2            0.946921\n",
       "moments-0-3            0.925326\n",
       "moments-1-0            0.935787\n",
       "moments-1-1            0.941119\n",
       "moments-1-2            0.927716\n",
       "moments-1-3            0.908200\n",
       "moments-2-0            0.887445\n",
       "moments-2-1            0.885396\n",
       "moments-2-2            0.869973\n",
       "moments-2-3            0.850189\n",
       "moments-3-0            0.817725\n",
       "moments-3-1            0.807975\n",
       "moments-3-2            0.789469\n",
       "moments-3-3            0.768762\n",
       "moments_central-0-0    0.954241\n",
       "moments_central-0-1    0.058940\n",
       "moments_central-0-2    0.938442\n",
       "moments_central-0-3    0.439487\n",
       "moments_central-1-0    0.064789\n",
       "moments_central-2-0    0.885203\n",
       "moments_central-2-2    0.868394\n",
       "moments_central-3-0    0.175147\n",
       "moments_central-3-2    0.076532\n",
       "perimeter              0.908174\n",
       "feret_diameter_max     0.901363\n",
       "solidity               0.290750\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrmat[corrmat.weight > 0].weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3387c2f9-f71f-4f43-a949-431452cc6573",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_names = list(dict(corrmat[corrmat.weight > 0.9].weight).keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e0d9fca-7064-45d9-a063-f4c35ebc0204",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_names.remove(\"weight\")\n",
    "# features_names.append(\"specie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e936ab1-9427-4f6c-a238-b2c2d6e8567e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test, index_train, index_test = (train[features_names].values, \n",
    "                                                             test[features_names].values, \n",
    "                                                             train.weight.values,\n",
    "                                                             test.weight.values,\n",
    "                                                             train[\"index\"].tolist(),\n",
    "                                                             test[\"index\"].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b19723ad-c44e-4156-9ca4-daee80660f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "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=2), \n",
    "                                        LinearRegression(fit_intercept=False)),\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eed79be-c65c-4698-8803-6515c1564693",
   "metadata": {},
   "source": [
    "### Features Não Normalizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f9a9b42e-1963-4039-b52d-68d7b302504e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "GradientBoostingRegressor\n"
     ]
    },
    {
     "data": {
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       "<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>10.394652</td>\n",
       "      <td>18.365193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.997891</td>\n",
       "      <td>0.908248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              Treino      Teste\n",
       "MAE        10.394652  18.365193\n",
       "R-squared   0.997891   0.908248"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "AdaBoostRegressor\n"
     ]
    },
    {
     "data": {
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       "<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>35.228744</td>\n",
       "      <td>19.767191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.977029</td>\n",
       "      <td>0.959993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino      Teste\n",
       "MAE        35.228744  19.767191\n",
       "R-squared   0.977029   0.959993"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "[LinearRegression]\n"
     ]
    },
    {
     "data": {
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       "    <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>37.328635</td>\n",
       "      <td>22.649136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.946188</td>\n",
       "      <td>0.957080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino      Teste\n",
       "MAE        37.328635  22.649136\n",
       "R-squared   0.946188   0.957080"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "[LassoRegression]\n"
     ]
    },
    {
     "data": {
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       "  <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>38.363991</td>\n",
       "      <td>24.692499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.944363</td>\n",
       "      <td>0.948331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino      Teste\n",
       "MAE        38.363991  24.692499\n",
       "R-squared   0.944363   0.948331"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "[RidgeRegression]\n"
     ]
    },
    {
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       "    <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>37.313529</td>\n",
       "      <td>22.609988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.946188</td>\n",
       "      <td>0.957193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino      Teste\n",
       "MAE        37.313529  22.609988\n",
       "R-squared   0.946188   0.957193"
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     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "[RandomForestRegressor]\n"
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      "____________________\n",
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      "ElasticNet\n"
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      "____________________\n",
      "\n",
      "[RANSACRegressor]\n"
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       "      <td>41.333257</td>\n",
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       "              Treino      Teste\n",
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      "____________________\n",
      "\n",
      "[PLSRegression]\n"
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       "              Treino      Teste\n",
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      "____________________\n",
      "\n",
      "[PCARegression]\n"
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     "text": [
      "____________________\n",
      "\n",
      "[PolyRegression]\n"
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       "              Treino      Teste\n",
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      "____________________\n",
      "\n",
      "XGBRegressor\n"
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       "             Treino      Teste\n",
       "MAE        0.068693  22.100110\n",
       "R-squared  1.000000   0.835677"
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     "text": [
      "____________________\n"
     ]
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   ],
   "source": [
    "run_all_algs(X_train, y_train, X_test, y_test)"
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   "metadata": {},
   "source": [
    "### Features Normalizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f11f62e9-6f81-4b7f-b3a8-4ae4e85770de",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "\n",
      "GradientBoostingRegressor\n"
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       "      <th>MAE</th>\n",
       "      <td>10.394652</td>\n",
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       "      <th>R-squared</th>\n",
       "      <td>0.997891</td>\n",
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       "              Treino      Teste\n",
       "MAE        10.394652  18.228542\n",
       "R-squared   0.997891   0.907532"
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      "____________________\n",
      "\n",
      "AdaBoostRegressor\n"
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       "      <th>MAE</th>\n",
       "      <td>33.453935</td>\n",
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       "      <th>R-squared</th>\n",
       "      <td>0.978876</td>\n",
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      "text/plain": [
       "              Treino      Teste\n",
       "MAE        33.453935  19.057338\n",
       "R-squared   0.978876   0.943313"
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     "text": [
      "____________________\n",
      "\n",
      "[LinearRegression]\n"
     ]
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       "      <td>37.328636</td>\n",
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       "              Treino      Teste\n",
       "MAE        37.328636  22.649137\n",
       "R-squared   0.946188   0.957080"
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     "text": [
      "____________________\n",
      "\n",
      "[LassoRegression]\n"
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       "      <th>MAE</th>\n",
       "      <td>44.190137</td>\n",
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       "      <th>R-squared</th>\n",
       "      <td>0.930636</td>\n",
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       "              Treino      Teste\n",
       "MAE        44.190137  21.719078\n",
       "R-squared   0.930636   0.955893"
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     "text": [
      "____________________\n",
      "\n",
      "[RidgeRegression]\n"
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       "      <th>MAE</th>\n",
       "      <td>40.719091</td>\n",
       "      <td>20.406157</td>\n",
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       "      <th>R-squared</th>\n",
       "      <td>0.937090</td>\n",
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       "              Treino      Teste\n",
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      "____________________\n",
      "\n",
      "[RandomForestRegressor]\n"
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       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>16.006072</td>\n",
       "      <td>12.561772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.987933</td>\n",
       "      <td>0.970092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino      Teste\n",
       "MAE        16.006072  12.561772\n",
       "R-squared   0.987933   0.970092"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "ElasticNet\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>50.222509</td>\n",
       "      <td>22.330325</td>\n",
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       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.921337</td>\n",
       "      <td>0.952111</td>\n",
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       "              Treino      Teste\n",
       "MAE        50.222509  22.330325\n",
       "R-squared   0.921337   0.952111"
      ]
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     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "[RANSACRegressor]\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>37.009482</td>\n",
       "      <td>25.023275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.937674</td>\n",
       "      <td>0.928386</td>\n",
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       "              Treino      Teste\n",
       "MAE        37.009482  25.023275\n",
       "R-squared   0.937674   0.928386"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "[PLSRegression]\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>47.215687</td>\n",
       "      <td>18.939618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.924475</td>\n",
       "      <td>0.964322</td>\n",
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       "              Treino      Teste\n",
       "MAE        47.215687  18.939618\n",
       "R-squared   0.924475   0.964322"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "[PCARegression]\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>50.536958</td>\n",
       "      <td>19.571486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.921360</td>\n",
       "      <td>0.959815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino      Teste\n",
       "MAE        50.536958  19.571486\n",
       "R-squared   0.921360   0.959815"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "[PolyRegression]\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>9.700269</td>\n",
       "      <td>86.195306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.997290</td>\n",
       "      <td>-6.569257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             Treino      Teste\n",
       "MAE        9.700269  86.195306\n",
       "R-squared  0.997290  -6.569257"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n",
      "\n",
      "XGBRegressor\n"
     ]
    },
    {
     "data": {
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       "      <th>Teste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.068693</td>\n",
       "      <td>22.100110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.835677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Treino      Teste\n",
       "MAE        0.068693  22.100110\n",
       "R-squared  1.000000   0.835677"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________\n"
     ]
    }
   ],
   "source": [
    "run_all_algs(X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64a3be13-8264-44dd-8aa9-bf1fbcec95de",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor(n_estimators=1000, max_depth=9,random_state=0, n_jobs=-1).fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7daa49a2-cc3e-4287-a681-32c32adf3806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</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>16.571450</td>\n",
       "      <td>13.034678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R-squared</th>\n",
       "      <td>0.989114</td>\n",
       "      <td>0.961640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Treino      Teste\n",
       "MAE        16.571450  13.034678\n",
       "R-squared   0.989114   0.961640"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_model_performance(model, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e6183911-063a-474a-8ad5-ae3b2493a842",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "syntetic = pd.DataFrame({\n",
    "    \"ACTUAL\": y_test,\n",
    "    \"PREDICTED_VALUES\": np.rint(model.predict(X_test_scaled)).astype(int),\n",
    "})\n",
    "\n",
    "syntetic[\"ERROR_ABS\"] = abs(syntetic[\"ACTUAL\"] - syntetic[\"PREDICTED_VALUES\"])\n",
    "syntetic[\"MEAN_VALUES\"] = np.rint((syntetic[\"ACTUAL\"] + syntetic[\"PREDICTED_VALUES\"]) / 2).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d58186b6-3049-4732-ad33-521d7800c259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTUAL</th>\n",
       "      <th>PREDICTED_VALUES</th>\n",
       "      <th>ERROR_ABS</th>\n",
       "      <th>MEAN_VALUES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>265</td>\n",
       "      <td>275</td>\n",
       "      <td>10</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>339</td>\n",
       "      <td>2</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>258</td>\n",
       "      <td>261</td>\n",
       "      <td>3</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>228</td>\n",
       "      <td>231</td>\n",
       "      <td>3</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>448</td>\n",
       "      <td>457</td>\n",
       "      <td>9</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>532</td>\n",
       "      <td>545</td>\n",
       "      <td>13</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>334</td>\n",
       "      <td>358</td>\n",
       "      <td>24</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>336</td>\n",
       "      <td>363</td>\n",
       "      <td>27</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>244</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>300</td>\n",
       "      <td>301</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ACTUAL  PREDICTED_VALUES  ERROR_ABS  MEAN_VALUES\n",
       "0      265               275         10          270\n",
       "1      337               339          2          338\n",
       "2      258               261          3          260\n",
       "3      228               231          3          230\n",
       "4      448               457          9          452\n",
       "..     ...               ...        ...          ...\n",
       "74     532               545         13          538\n",
       "75     334               358         24          346\n",
       "76     336               363         27          350\n",
       "77     244               244          0          244\n",
       "78     300               301          1          300\n",
       "\n",
       "[79 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syntetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8385cbe4-8392-4712-b6ef-77ca35eb5e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# syntetic.to_csv(\"syntetic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7715ddc0-798c-4890-bc9c-bf79fbe938d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = residuals_plot(model, \n",
    "                     X_train_scaled, \n",
    "                     y_train, \n",
    "                     X_test_scaled, \n",
    "                     y_test,\n",
    "                     hist=False)\n",
    "\n",
    "viz.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "22f642a3-890f-42f4-8e7c-eb291279da7d",
   "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, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
    "\n",
    "PredictionErrorDisplay.from_estimator(\n",
    "    model, \n",
    "    X_test_scaled,\n",
    "    y_test,\n",
    "    kind=\"actual_vs_predicted\",\n",
    "    ax=axs[0]\n",
    ");\n",
    "\n",
    "PredictionErrorDisplay.from_estimator(\n",
    "    model,\n",
    "    X_test_scaled,\n",
    "    y_test,\n",
    "    kind=\"residual_vs_predicted\", \n",
    "    ax=axs[1]\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89ee000e-e2ec-465e-a35e-5889104f4ca4",
   "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
}