svr.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVR GA FEATURE EXTRACTION AND MODELLING\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVR\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from ga_svr import GA_CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'data.xlsx'\n",
    "name_x = 'X'\n",
    "name_y = 'y'\n",
    "## Split 1:X\n",
    "x_df = pd.read_excel(file_name, sheet_name=name_x, index_col=0)\n",
    "y_df = pd.read_excel(file_name, sheet_name=name_y, index_col=0)\n",
    "\n",
    "selected_y = 'y'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_type = input(\"Split wlasny (W) czy algorytmem 1 do X? (int np. 2,3,4...n) \")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------\n",
      "Training set NaNs:  []\n",
      "Checking again...\n",
      "Training set NaNs:  []\n"
     ]
    }
   ],
   "source": [
    "if split_type.lower() == 'w':\n",
    "    x_sorted = x_df.join(y_df[selected_y])\n",
    "    nan_train = x_sorted.columns[x_sorted.isna().any()].tolist()\n",
    "    print('---------------------')\n",
    "    print('Training set NaNs: ', nan_train)\n",
    "\n",
    "    train_x = x_sorted.drop(nan_train, axis=1)\n",
    "    print('Checking again...')\n",
    "    nan_train = train_x.columns[train_x.isna().any()].tolist()\n",
    "    print('Training set NaNs: ', nan_train)\n",
    "    \n",
    "    y_sorted = x_sorted[selected_y]\n",
    "    train_y = y_sorted.copy()\n",
    "    Xt = train_x[train_x['split']=='T']\n",
    "    Yt = y_sorted[train_x['split']=='T']\n",
    "    Xv = train_x[train_x['split']=='V']\n",
    "    Yv = y_sorted[train_x['split']=='V']\n",
    "    Xt = Xt.drop(['split', selected_y], axis=1)\n",
    "    Xv = Xv.drop(['split', selected_y], axis=1)\n",
    "\n",
    "    from sklearn.feature_selection import VarianceThreshold\n",
    "    selector = VarianceThreshold(threshold=0.0)\n",
    "    selector.fit(Xt)\n",
    "    indexes = selector.get_support()\n",
    "    train_x = Xt.iloc[:,indexes]\n",
    "    val_x = Xv.iloc[:,indexes]\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    scaler = StandardScaler()\n",
    "    Xt = pd.DataFrame(scaler.fit_transform(train_x), \n",
    "                       columns=train_x.columns.values, \n",
    "                       index=train_x.index.values)\n",
    "    Xv = pd.DataFrame(scaler.transform(val_x), \n",
    "                       columns=val_x.columns.values, \n",
    "                       index=val_x.index.values)\n",
    "else:\n",
    "    x_sorted = x_df.join(y_df[selected_y]).sort_values(by=selected_y, ascending=True)\n",
    "    y_sorted = x_sorted[selected_y]\n",
    "    x_sorted.drop(selected_y, axis=1, inplace=True)\n",
    "    if len([x for x in x_sorted.columns.values if x == 'split']) > 0:\n",
    "        x_sorted.drop('split', axis=1, inplace=True)\n",
    "    xdon = int(split_type)\n",
    "    nan_train = x_sorted.columns[x_sorted.isna().any()].tolist()\n",
    "\n",
    "    print('---------------------')\n",
    "    print('Training set NaNs: ', nan_train)\n",
    "\n",
    "    train_x = x_sorted.drop(nan_train, axis=1)\n",
    "    print('Checking again...')\n",
    "    nan_train = train_x.columns[train_x.isna().any()].tolist()\n",
    "    print('Training set NaNs: ', nan_train)\n",
    "\n",
    "    #usuwa zmienne o constant wartosciach\n",
    "    from sklearn.feature_selection import VarianceThreshold\n",
    "    selector = VarianceThreshold()\n",
    "    cut = selector.fit_transform(train_x)\n",
    "    indexes = selector.get_support()\n",
    "    train_x = train_x.iloc[:,indexes]\n",
    "    train_y = y_sorted.copy()\n",
    "\n",
    "    def x_to_n(X,n=3):\n",
    "        row_val = X.shape[0]\n",
    "        train_split = []\n",
    "        val_split = []  \n",
    "        for row in range(0, row_val):\n",
    "            #print(row % n)\n",
    "            if ((row%n) < n-1):\n",
    "                train_split.append(row)\n",
    "            else:\n",
    "                val_split.append(row)\n",
    "        last_train = np.max(train_split)\n",
    "        last_val = np.max(val_split)\n",
    "        if (np.max(train_split) < np.max(val_split)):\n",
    "            train_split.pop()\n",
    "            val_split.pop()\n",
    "            train_split.append(last_val)\n",
    "            val_split.append(last_train)\n",
    "            \n",
    "        return train_split, val_split\n",
    "\n",
    "    ts, vs = x_to_n(train_x, xdon)\n",
    "\n",
    "    Xt = train_x.iloc[ts]\n",
    "    Xv = train_x.iloc[vs]\n",
    "    Yt = train_y.iloc[ts]\n",
    "    Yv = train_y.iloc[vs]\n",
    "    \n",
    "    col_names = Xt.columns.values\n",
    "    tr_sample_names = Xt.index.values\n",
    "    v_sample_names = Xv.index.values\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    scaler = StandardScaler()\n",
    "    scaler.fit(Xt)\n",
    "    Xt = pd.DataFrame(scaler.transform(Xt), index = tr_sample_names, columns=col_names)\n",
    "    Xv = pd.DataFrame(scaler.transform(Xv), index = v_sample_names, columns=col_names)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = Xt\n",
    "Y = Yt\n",
    "X_names = Xt.columns.values\n",
    "\n",
    "is_array=False\n",
    "try:\n",
    "    X = np.array(X)\n",
    "    is_array = True\n",
    "except:\n",
    "    X = X\n",
    "    \n",
    "isy_array=False\n",
    "try:\n",
    "    Y = np.array(Y)\n",
    "    isy_array = True\n",
    "except:\n",
    "    Y = Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_vars = 2          \n",
    "iters = 10         \n",
    "mut_rat = 0.05       \n",
    "pop_size = 1000      \n",
    "\n",
    "eps = 0.1           \n",
    "kernel = 'rbf'   \n",
    "C_val = 1           \n",
    "degree = 3          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 modelu 0.834\n",
      "Q2 modelu 0.816\n",
      "RMSEc modelu 0.15\n",
      "RMSEv modelu 0.123\n",
      "MAE_c 0.137\n",
      "MAE_v 0.089\n"
     ]
    }
   ],
   "source": [
    "model_vars = ['VE2sign_Dz(e)', 'ATS3i']\n",
    "\n",
    "temp_X = Xt[model_vars]\n",
    "temp_V = Xv[model_vars]\n",
    "\n",
    "selected_X = temp_X \n",
    "selected_V = temp_V \n",
    "\n",
    "svr = SVR(epsilon=eps, kernel=kernel, C=C_val, degree=degree, max_iter=-1)\n",
    "svr.fit(selected_X, Yt)\n",
    "train_pred = svr.predict(selected_X)\n",
    "test_pred = svr.predict(selected_V)\n",
    "\n",
    "train_obs = np.asarray(Yt).reshape(-1)\n",
    "train_pred = np.asarray(train_pred).reshape(-1)\n",
    "test_obs = np.asarray(Yv).reshape(-1)\n",
    "test_pred = np.asarray(test_pred).reshape(-1)\n",
    "\n",
    "\n",
    "rmse_of_cal = np.sqrt(metrics.mean_squared_error(Yt, train_pred))\n",
    "rmse_of_val = np.sqrt(metrics.mean_squared_error(Yv, test_pred))\n",
    "mae_c = metrics.mean_absolute_error(Yt, train_pred)\n",
    "mae_v = metrics.mean_absolute_error(Yv, test_pred)\n",
    "\n",
    "\n",
    "r2= svr.score(selected_X, Yt)\n",
    "q2= svr.score(selected_V, Yv)\n",
    "\n",
    "print(\"R2 modelu\", round(r2,3))\n",
    "print(\"Q2 modelu\", round(q2,3))\n",
    "print(\"RMSEc modelu\", round(rmse_of_cal,3))\n",
    "print(\"RMSEv modelu\", round(rmse_of_val,3))\n",
    "print(\"MAE_c\", round(mae_c, 3))\n",
    "print(\"MAE_v\", round(mae_v, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/michalina/Library/Python/3.9/lib/python/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but SVR was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
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         "text": "SVR for Target Property"
        },
        "width": 900
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      }
     },
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   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "Descriptor1_train = selected_X.iloc[:, 0].to_numpy()  \n",
    "Descriptor2_train = selected_X.iloc[:, 1].to_numpy()\n",
    "Descriptor1_test = selected_V.iloc[:, 0].to_numpy()\n",
    "Descriptor2_test = selected_V.iloc[:, 1].to_numpy()\n",
    "\n",
    "x_min, x_max = np.min(Descriptor1_train), np.max(Descriptor1_train)\n",
    "y_min, y_max = np.min(Descriptor2_train), np.max(Descriptor2_train)\n",
    "x_range = np.linspace(x_min, x_max, 50)\n",
    "y_range = np.linspace(y_min, y_max, 50)\n",
    "X, Y = np.meshgrid(x_range, y_range)\n",
    "Z_train = svr.predict(np.c_[X.ravel(), Y.ravel()]).reshape(X.shape)\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Scatter3d(x=Descriptor1_train, y=Descriptor2_train, z=Yt, \n",
    "                 mode='markers', marker=dict(size=5, color='blue'), name='Training Set'),\n",
    "    go.Scatter3d(x=Descriptor1_test, y=Descriptor2_test, z=Yv, \n",
    "                 mode='markers', marker=dict(size=5, color='red'), name='Validation Set'),\n",
    "    go.Surface(x=X, y=Y, z=Z_train, colorscale='Viridis', opacity=0.7, name='Prediction Surface')\n",
    "])\n",
    "\n",
    "fig.update_layout(scene=dict(\n",
    "                    xaxis_title='Descriptor 1',\n",
    "                    yaxis_title='Descriptor 2',\n",
    "                    zaxis_title='Predicted Y'),\n",
    "                  title='SVR for Target Property',\n",
    "                  width=900, height=700)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9fd37604a9ad4072b9ba6dc70ca02e59",
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      "text/plain": [
       "  0%|          | 0/19 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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           "bgcolor": "#E5ECF6",
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          },
          "title": {
           "x": 0.05
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           "automargin": true,
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        },
        "title": {
         "text": "SHAP Summary Plot - ATS3i"
        },
        "xaxis": {
         "showgrid": false,
         "title": {
          "text": "SHAP Value"
         }
        },
        "yaxis": {
         "range": [
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   ],
   "source": [
    "\n",
    "import shap\n",
    "\n",
    "Xt['ID'] = list(range(1, len(Xt) + 1))\n",
    "Xv['ID'] = list(range(len(Xt) + 1, len(Xt) + len(Xv) + 1))\n",
    "\n",
    "temp_Xt = Xt[model_vars].values\n",
    "temp_Xv = Xv[model_vars].values\n",
    "\n",
    "combined_X = np.vstack((temp_Xt, temp_Xv))\n",
    "\n",
    "explainer = shap.KernelExplainer(svr.predict, temp_Xt)\n",
    "\n",
    "shap_values = explainer.shap_values(combined_X)\n",
    "\n",
    "shap_values_df = pd.DataFrame(shap_values, columns=model_vars)\n",
    "combined_X_df = pd.DataFrame(combined_X, columns=model_vars)\n",
    "\n",
    "combined_X_df['ID'] = Xt['ID'].tolist() + Xv['ID'].tolist()\n",
    "\n",
    "summary_df = pd.DataFrame()\n",
    "\n",
    "for i, var in enumerate(model_vars):\n",
    "    temp_df = pd.DataFrame()\n",
    "    temp_df['Feature Value'] = combined_X_df[var]\n",
    "    temp_df['SHAP Value'] = shap_values_df[var]\n",
    "    temp_df['Feature'] = var\n",
    "    temp_df['Feature Position'] = i \n",
    "    temp_df['ID'] = combined_X_df['ID']  \n",
    "    summary_df = pd.concat([summary_df, temp_df], axis=0)\n",
    "\n",
    "min_feature_value = combined_X_df[model_vars].min().min()\n",
    "max_feature_value = combined_X_df[model_vars].max().max()\n",
    "\n",
    "fig1 = go.Figure()\n",
    "fig2 = go.Figure()\n",
    "\n",
    "for i, feature in enumerate(model_vars):\n",
    "    feature_data = summary_df[summary_df['Feature'] == feature]\n",
    "    y_values = np.linspace(i - 0.5, i + 0.5, len(feature_data)) \n",
    "\n",
    "    if feature == 'VE2sign_Dz(e)':\n",
    "        fig = fig1\n",
    "    elif feature == 'ATS3i':\n",
    "        fig = fig2\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=feature_data['SHAP Value'],\n",
    "        y=y_values,\n",
    "        mode='markers+text', \n",
    "        marker=dict(\n",
    "            size=5,  \n",
    "            symbol='circle', \n",
    "            color=feature_data['Feature Value'],\n",
    "            colorscale='RdBu',\n",
    "            showscale=True,\n",
    "            colorbar=dict(title='Feature Value', tickformat='.2f'),  \n",
    "            line=dict(width=1, color='black') \n",
    "        ),\n",
    "        text=feature_data['ID'].astype(str),\n",
    "        textposition='top center', \n",
    "        hoverinfo='text',\n",
    "        name=feature,\n",
    "        textfont=dict(size=8)\n",
    "    ))\n",
    "\n",
    "fig1.update_layout(\n",
    "    title='SHAP Summary Plot - VE2sign_Dz(e)',\n",
    "    xaxis_title='SHAP Value',\n",
    "    yaxis_title='Feature',\n",
    "    showlegend=False, \n",
    "    height=300,  \n",
    "    hovermode='closest', \n",
    "    margin=dict(l=100, r=100, b=50, t=80),  \n",
    "    font=dict(size=12),  \n",
    "    yaxis=dict(range=[-0.5, 0.5], tickvals=[0], ticktext=['VE2sign_Dz(e)']),  \n",
    "    xaxis=dict(showgrid=False), \n",
    ")\n",
    "\n",
    "fig2.update_layout(\n",
    "    title='SHAP Summary Plot - ATS3i',\n",
    "    xaxis_title='SHAP Value',\n",
    "    yaxis_title='Feature',\n",
    "    showlegend=False,  \n",
    "    height=300, \n",
    "    hovermode='closest', \n",
    "    margin=dict(l=100, r=100, b=50, t=80), \n",
    "    font=dict(size=12), \n",
    "    yaxis=dict(range=[0.5, 1.5], tickvals=[1], ticktext=['ATS3i']),  \n",
    "    xaxis=dict(showgrid=False),  \n",
    ")\n",
    "\n",
    "\n",
    "fig1.show()\n",
    "fig2.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x_to_pred = pd.read_excel('to_pred_reg.xlsx', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VE2sign_Dz(e)</th>\n",
       "      <th>ATS3i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003071</td>\n",
       "      <td>4.313430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000617</td>\n",
       "      <td>4.330059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.004255</td>\n",
       "      <td>4.279374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.006057</td>\n",
       "      <td>3.438032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.001280</td>\n",
       "      <td>4.982196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>0.000736</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12637</th>\n",
       "      <td>0.001388</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12638</th>\n",
       "      <td>0.001234</td>\n",
       "      <td>4.967865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12642</th>\n",
       "      <td>0.000406</td>\n",
       "      <td>4.757097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1607 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       VE2sign_Dz(e)     ATS3i\n",
       "ID                            \n",
       "1           0.003071  4.313430\n",
       "2           0.000617  4.330059\n",
       "11          0.004255  4.279374\n",
       "67          0.006057  3.438032\n",
       "82          0.001280  4.982196\n",
       "...              ...       ...\n",
       "12635       0.000250  5.010229\n",
       "12636       0.000736  4.417346\n",
       "12637       0.001388  4.936330\n",
       "12638       0.001234  4.967865\n",
       "12642       0.000406  4.757097\n",
       "\n",
       "[1607 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x_to_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_vars = ['VE2sign_Dz(e)',\t'ATS3i'] \n",
    "df_x_to_pred = df_x_to_pred[model_vars]\n",
    "df_x_to_pred = df_x_to_pred.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x_to_pred.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df_x_to_pred) \n",
    "\n",
    "df_x_to_pred_scaled = scaler.transform(df_x_to_pred)\n",
    "df_x_to_pred_scaled = pd.DataFrame(df_x_to_pred_scaled, columns=model_vars, index=df_x_to_pred.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = svr.predict(df_x_to_pred_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_przewidywanki = pd.DataFrame(predictions, \n",
    "                                columns=['New_predictions'], \n",
    "                                index=df_x_to_pred.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>New_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.049598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.047281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.089972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>-0.068794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.183989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12635</th>\n",
       "      <td>0.194727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12636</th>\n",
       "      <td>-0.007906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12637</th>\n",
       "      <td>0.178165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12638</th>\n",
       "      <td>0.183076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12642</th>\n",
       "      <td>0.133829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1607 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       New_predictions\n",
       "1            -0.049598\n",
       "2            -0.047281\n",
       "11           -0.089972\n",
       "67           -0.068794\n",
       "82            0.183989\n",
       "...                ...\n",
       "12635         0.194727\n",
       "12636        -0.007906\n",
       "12637         0.178165\n",
       "12638         0.183076\n",
       "12642         0.133829\n",
       "\n",
       "[1607 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_przewidywanki"
   ]
  }
 ],
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