Random_Forest_Experiments.ipynb

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        "<a href=\"https://colab.research.google.com/github/kushasupaya/cof_featurization_ml/blob/main/Random_Forest_Experiments.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "54MO9-TUtQV-"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn.linear_model as sl\n",
        "import sklearn.preprocessing as sp\n",
        "import sklearn.decomposition as sd\n",
        "import sklearn.model_selection as sm\n",
        "import sklearn.ensemble as se\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler, PolynomialFeatures, PowerTransformer, MinMaxScaler, RobustScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "features = pd.read_csv('/content/features_5000 (1).csv')\n",
        "properties = pd.read_csv('/content/properties_5000_cofs.csv', nrows=5000)"
      ]
    },
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              "   MagpieData minimum Number  MagpieData maximum Number  \\\n",
              "0                        1.0                        8.0   \n",
              "1                        1.0                        8.0   \n",
              "2                        1.0                        8.0   \n",
              "3                        1.0                        8.0   \n",
              "4                        1.0                        8.0   \n",
              "\n",
              "   MagpieData range Number  MagpieData mean Number  MagpieData avg_dev Number  \\\n",
              "0                      7.0                4.556962                   2.341291   \n",
              "1                      7.0                4.527778                   2.547840   \n",
              "2                      7.0                5.093750                   2.302734   \n",
              "3                      7.0                4.075000                   2.613750   \n",
              "4                      7.0                4.075000                   2.613750   \n",
              "\n",
              "   MagpieData mode Number  MagpieData minimum MendeleevNumber  \\\n",
              "0                     6.0                                77.0   \n",
              "1                     6.0                                77.0   \n",
              "2                     6.0                                77.0   \n",
              "3                     6.0                                77.0   \n",
              "4                     6.0                                77.0   \n",
              "\n",
              "   MagpieData maximum MendeleevNumber  MagpieData range MendeleevNumber  \\\n",
              "0                                92.0                              15.0   \n",
              "1                                92.0                              15.0   \n",
              "2                                92.0                              15.0   \n",
              "3                                92.0                              15.0   \n",
              "4                                92.0                              15.0   \n",
              "\n",
              "   MagpieData mean MendeleevNumber  ...  rdf [19.00000 - 19.10000]A  \\\n",
              "0                        82.949367  ...                 1226.427439   \n",
              "1                        84.083333  ...                 2658.540982   \n",
              "2                        83.718750  ...                 2071.560406   \n",
              "3                        84.375000  ...                 1001.054614   \n",
              "4                        84.375000  ...                 2886.491788   \n",
              "\n",
              "   rdf [19.10000 - 19.20000]A  rdf [19.20000 - 19.30000]A  \\\n",
              "0                 1328.947710                 1349.889525   \n",
              "1                 2532.814052                 2569.867883   \n",
              "2                 2292.252430                 2151.810790   \n",
              "3                  971.570272                  929.908908   \n",
              "4                 2589.175200                 2590.019416   \n",
              "\n",
              "   rdf [19.30000 - 19.40000]A  rdf [19.40000 - 19.50000]A  \\\n",
              "0                 1384.966914                 1355.091328   \n",
              "1                 2578.948510                 2487.065301   \n",
              "2                 2582.791857                 2056.367362   \n",
              "3                  909.952924                  959.198360   \n",
              "4                 2891.145332                 2907.605363   \n",
              "\n",
              "   rdf [19.50000 - 19.60000]A  rdf [19.60000 - 19.70000]A  \\\n",
              "0                 1181.471244                 1392.827359   \n",
              "1                 2716.429690                 2301.459462   \n",
              "2                 2553.600794                 2520.868499   \n",
              "3                  929.094099                  912.545783   \n",
              "4                 2881.532216                 2881.302190   \n",
              "\n",
              "   rdf [19.70000 - 19.80000]A  rdf [19.80000 - 19.90000]A  \\\n",
              "0                 1361.552711                 1333.674810   \n",
              "1                 3190.525080                 2842.553965   \n",
              "2                 2401.061149                 2132.432505   \n",
              "3                  962.743612                  875.300451   \n",
              "4                 3027.292945                 2777.918877   \n",
              "\n",
              "   rdf [19.90000 - 20.00000]A  \n",
              "0                 1231.528709  \n",
              "1                 2613.627179  \n",
              "2                 2256.736200  \n",
              "3                  990.511539  \n",
              "4                 2756.249572  \n",
              "\n",
              "[5 rows x 335 columns]"
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3ea51b5f-064b-4a6b-92aa-33f289aed8a6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "X = features.drop(features.columns[[0]], axis =1 )\n",
        "Y = properties[[' deliverable capacity [v STP/v]',' heat desorption low P [kJ/mol]',' absolute methane uptake low P [mol/kg]']]\n",
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GhlHG5qA2wHD"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = sm.train_test_split(X, Y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eY1qvCNW6uEe"
      },
      "outputs": [],
      "source": [
        "pipeline = Pipeline([\n",
        "    ('ss', sp.StandardScaler()),\n",
        "    ('rf', se.RandomForestRegressor())])\n",
        "\n",
        "rf = se.RandomForestRegressor()\n",
        "\n",
        "# Number of trees in random forest\n",
        "n_estimators = [int(x) for x in np.linspace(start = 1, stop = 100, num = 10)]\n",
        "# Number of features to consider at every split\n",
        "max_features = ['sqrt','log2']\n",
        "# Maximum number of levels in tree\n",
        "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
        "max_depth.append(None)\n",
        "# Minimum number of samples required to split a node\n",
        "min_samples_split = [2, 5, 10]\n",
        "# Minimum number of samples required at each leaf node\n",
        "min_samples_leaf = [1, 2, 4]\n",
        "# Method of selecting samples for training each tree\n",
        "bootstrap = [True, False]\n",
        "# Create the random grid\n",
        "params = {\n",
        "            \"n_estimators\"      : [50],\n",
        "            \"max_features\"      : [\"sqrt\", \"log2\",None],\n",
        "            \"max_depth\"         : [None,10,20,30,50],\n",
        "            \"min_samples_split\" : [2,5,10],\n",
        "            \"min_samples_leaf\"  : [1,2,4,8],\n",
        "            \"bootstrap\": [True]\n",
        "            }\n",
        "\n",
        "\n",
        "grid_pipeline = sm.GridSearchCV(rf,params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rQ8ROp8o_80s"
      },
      "outputs": [],
      "source": [
        "grid_pipeline.fit(x_train,y_train)\n",
        "best_p = grid_pipeline.best_params_\n",
        "rf = se.RandomForestRegressor(bootstrap=True, max_depth=15, max_features='sqrt', min_samples_leaf=1, min_samples_split=8, n_estimators=500, criterion='sqaured_error')\n",
        "rf1 = se.RandomForestRegressor(n_estimators=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVtaJwLqZ_Zz",
        "outputId": "86cccdbb-8696-4551-b2db-3f73af123962"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'bootstrap': True,\n",
              " 'max_depth': 30,\n",
              " 'max_features': None,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'n_estimators': 50}"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "best_p"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8qD033-rL5W2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "3c9f0995-223f-4333-c6c5-6a910a974f60"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-93a8ee244c4b>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#best_m = grid_pipeline.best_estimator_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mbest_m\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbootstrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_samples_leaf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_samples_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'squared_error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mbest_m\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbest_m\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tr:{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_m\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    471\u001b[0m             \u001b[0;31m# parallel_backend contexts set at a higher level,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m             \u001b[0;31m# since correctness does not rely on using threads.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m             trees = Parallel(\n\u001b[0m\u001b[1;32m    474\u001b[0m                 \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m                 \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         )\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_sequential_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m             \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1865\u001b[0m         \u001b[0;31m# Let's create an ID that uniquely identifies the current call. If the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_batches\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1791\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_tasks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1792\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1793\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_completed_tasks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1794\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_progress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[0mcurr_sample_weight\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mcompute_sample_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"balanced\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m   1245\u001b[0m         \"\"\"\n\u001b[1;32m   1246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1247\u001b[0;31m         super().fit(\n\u001b[0m\u001b[1;32m   1248\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1249\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    377\u001b[0m             )\n\u001b[1;32m    378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "import sklearn.metrics as sr\n",
        "from sklearn.metrics import mean_squared_error\n",
        "#best_m = grid_pipeline.best_estimator_\n",
        "best_m = se.RandomForestRegressor(bootstrap=True, max_depth=30, max_features=None, min_samples_leaf=1, min_samples_split=2, n_estimators=1000, criterion='squared_error')\n",
        "best_m.fit(x_train,y_train)\n",
        "y_pred = best_m.predict(x_test)\n",
        "print(\"Tr:{}\".format(np.mean(best_m.predict(x_train)-y_train)**2))\n",
        "print(\"Te:{}\".format(np.mean(y_pred-y_test)**2))\n",
        "print(mean_squared_error(y_test, y_pred))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8sSAz2OYGQc",
        "outputId": "2b49e929-b467-4de4-bfbe-07ca777f7ed0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8954254233580113\n",
            "0.8954254233580113\n"
          ]
        }
      ],
      "source": [
        "print(sr.r2_score(y_test, y_pred))\n",
        "print(best_m.score(x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zyk9aA6pic78"
      },
      "outputs": [],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler, PolynomialFeatures, PowerTransformer, MinMaxScaler, RobustScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "#1 try\n",
        "\n",
        "numeric_transformer1 =  Pipeline(steps=[\n",
        "    ('imputer',SimpleImputer(strategy='median')),\n",
        "    ('scaler',StandardScaler()),\n",
        "    ('poly',PolynomialFeatures(degree=1)),\n",
        "])\n",
        "numeric_features =X.columns\n",
        "preprocessor1 = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer1, numeric_features)\n",
        "])\n",
        "\n",
        "rg = Pipeline(\n",
        "    steps=[(\"preprocessor\", preprocessor1), (\"regressor\", RandomForestRegressor())]\n",
        ")\n",
        "\n",
        "params = {\n",
        "\n",
        "            \"regressor__max_features\"      : [\"sqrt\", \"log2\",None],\n",
        "            \"regressor__max_depth\"         : [None,10,20,30,50],\n",
        "            \"regressor__min_samples_split\" : [2,5,10],\n",
        "            \"regressor__min_samples_leaf\"  : [1,2,4,8],\n",
        "            \"regressor__bootstrap\": [True]\n",
        "            }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "1mWdVxpNmAGV",
        "outputId": "3f344d60-9266-4bd4-9c65-9290097e1b67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tr: deliverable capacity [v STP/v]            0.000286\n",
            " heat desorption low P [kJ/mol]            0.000059\n",
            " absolute methane uptake low P [mol/kg]    0.000002\n",
            "dtype: float64\n",
            "Te: deliverable capacity [v STP/v]            0.001703\n",
            " heat desorption low P [kJ/mol]            0.000001\n",
            " absolute methane uptake low P [mol/kg]    0.000017\n",
            "dtype: float64\n",
            "8.183391040427804\n",
            "0.891097569802142\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3430: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
            "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3430: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
            "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-0d0e53adef05>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'r2_score'"
          ]
        }
      ],
      "source": [
        "gp = sm.GridSearchCV(rg,param_grid=params)\n",
        "gp.fit(x_train,y_train)\n",
        "best_p = gp.best_params_\n",
        "y_pred = gp.predict(x_test)\n",
        "print(\"Tr:{}\".format(np.mean(gp.predict(x_train)-y_train)**2))\n",
        "print(\"Te:{}\".format(np.mean(y_pred-y_test)**2))\n",
        "print(mean_squared_error(y_test, y_pred))\n",
        "\n",
        "print(gp.score(x_test,y_test))\n",
        "print(gp.r2_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmiTLARcWGym",
        "outputId": "92d74aea-c37e-4b26-e363-b5281eb3752e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'regressor__bootstrap': True,\n",
              " 'regressor__max_depth': 50,\n",
              " 'regressor__max_features': None,\n",
              " 'regressor__min_samples_leaf': 1,\n",
              " 'regressor__min_samples_split': 2,\n",
              " 'regressor__n_estimators': 50}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rea0s1H9jYQh",
        "outputId": "108f0a5c-7d52-44a4-c497-31c7dee94143"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3430: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
            "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3430: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
            "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tr: deliverable capacity [v STP/v]            0.000045\n",
            " heat desorption low P [kJ/mol]            0.000031\n",
            " absolute methane uptake low P [mol/kg]    0.000002\n",
            "dtype: float64\n",
            "Te: deliverable capacity [v STP/v]            1.580394e-03\n",
            " heat desorption low P [kJ/mol]            7.770649e-07\n",
            " absolute methane uptake low P [mol/kg]    2.802077e-05\n",
            "dtype: float64\n",
            "7.561553166734336\n",
            "0.8951215370419073\n"
          ]
        }
      ],
      "source": [
        "rg1 = Pipeline(\n",
        "    steps=[(\"preprocessor\", preprocessor1), (\"regressor\", RandomForestRegressor(bootstrap=True, max_depth=50, max_features=None, min_samples_leaf=1, min_samples_split=2,n_estimators=500))]\n",
        ")\n",
        "rg1.fit(x_train,y_train)\n",
        "y_pred = rg1.predict(x_test)\n",
        "print(\"Tr:{}\".format(np.mean(rg1.predict(x_train)-y_train)**2))\n",
        "print(\"Te:{}\".format(np.mean(y_pred-y_test)**2))\n",
        "print(mean_squared_error(y_test, y_pred))\n",
        "\n",
        "print(rg1.score(x_test,y_test))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "id": "JNH5cthfmUa_",
        "outputId": "8ff9f313-fd88-4bf7-8dca-e1d661480c23"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-b22cb3bb8163>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
          ]
        }
      ],
      "source": [
        "len(X.columns)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}