ADD: Latent Class Report

choice_learn/models/latent_class_base_model.py

@@ -3,6 +3,7 @@
 import time
 
 import numpy as np
+import pandas as pd
 import tensorflow as tf
 import tqdm
 
@@ -930,3 +931,37 @@ def get_latent_classes_weights(self):
             Latent classes weights/probabilities
         """
         return tf.nn.softmax(tf.concat([[tf.constant(0.0)], self.latent_logits], axis=0))
+
+    def compute_report(self, choice_dataset):
+        """Compute a report of the estimated weights.
+
+        Parameters
+        ----------
+        choice_dataset : ChoiceDataset
+            ChoiceDataset used for the estimation of the weights that will be
+            used to compute the Std Err of this estimation.
+
+        Returns
+        -------
+        pandas.DataFrame
+            A DF with estimation, Std Err, z_value and p_value for each coefficient.
+        """
+        reports = []
+        for i, model in enumerate(self.models):
+            compute = getattr(model, "compute_report", None)
+            if callable(compute):
+                report = model.compute_report(choice_dataset)
+                report["Latent Class"] = i
+                reports.append(report)
+            else:
+                raise ValueError(f"{i}-th model {model} does not have a compute_report method.")
+        return pd.concat(reports, axis=0, ignore_index=True)[
+            [
+                "Latent Class",
+                "Coefficient Name",
+                "Coefficient Estimation",
+                "Std. Err",
+                "z_value",
+                "P(.>z)",
+            ]
+        ]

notebooks/models/latent_class_model.ipynb

@@ -39,6 +39,7 @@
     "\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
     "\n",
+    "import matplotlib as mpl\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
@@ -106,9 +107,254 @@
     "print(f\"Negative Log-Likelihood: {nll}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "keep_output": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using L-BFGS optimizer, setting up .fit() function\n",
+      "Using L-BFGS optimizer, setting up .fit() function\n",
+      "Using L-BFGS optimizer, setting up .fit() function\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/zz/r1py7zhj35q75v09h8_42nzh0000gp/T/ipykernel_67121/1263996749.py:4: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  cmap = mpl.cm.get_cmap(\"Set1\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_279d5_row0_col0, #T_279d5_row0_col1, #T_279d5_row0_col2, #T_279d5_row0_col3, #T_279d5_row0_col4, #T_279d5_row0_col5, #T_279d5_row1_col0, #T_279d5_row1_col1, #T_279d5_row1_col2, #T_279d5_row1_col3, #T_279d5_row1_col4, #T_279d5_row1_col5, #T_279d5_row2_col0, #T_279d5_row2_col1, #T_279d5_row2_col2, #T_279d5_row2_col3, #T_279d5_row2_col4, #T_279d5_row2_col5, #T_279d5_row3_col0, #T_279d5_row3_col1, #T_279d5_row3_col2, #T_279d5_row3_col3, #T_279d5_row3_col4, #T_279d5_row3_col5, #T_279d5_row4_col0, #T_279d5_row4_col1, #T_279d5_row4_col2, #T_279d5_row4_col3, #T_279d5_row4_col4, #T_279d5_row4_col5, #T_279d5_row5_col0, #T_279d5_row5_col1, #T_279d5_row5_col2, #T_279d5_row5_col3, #T_279d5_row5_col4, #T_279d5_row5_col5 {\n",
+       "  background-color: #e41a1c;\n",
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+       "  background-color: #377eb8;\n",
+       "}\n",
+       "#T_279d5_row12_col0, #T_279d5_row12_col1, #T_279d5_row12_col2, #T_279d5_row12_col3, #T_279d5_row12_col4, #T_279d5_row12_col5, #T_279d5_row13_col0, #T_279d5_row13_col1, #T_279d5_row13_col2, #T_279d5_row13_col3, #T_279d5_row13_col4, #T_279d5_row13_col5, #T_279d5_row14_col0, #T_279d5_row14_col1, #T_279d5_row14_col2, #T_279d5_row14_col3, #T_279d5_row14_col4, #T_279d5_row14_col5, #T_279d5_row15_col0, #T_279d5_row15_col1, #T_279d5_row15_col2, #T_279d5_row15_col3, #T_279d5_row15_col4, #T_279d5_row15_col5, #T_279d5_row16_col0, #T_279d5_row16_col1, #T_279d5_row16_col2, #T_279d5_row16_col3, #T_279d5_row16_col4, #T_279d5_row16_col5, #T_279d5_row17_col0, #T_279d5_row17_col1, #T_279d5_row17_col2, #T_279d5_row17_col3, #T_279d5_row17_col4, #T_279d5_row17_col5 {\n",
+       "  background-color: #4daf4a;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_279d5\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_279d5_level0_col0\" class=\"col_heading level0 col0\" >Latent Class</th>\n",
+       "      <th id=\"T_279d5_level0_col1\" class=\"col_heading level0 col1\" >Coefficient Name</th>\n",
+       "      <th id=\"T_279d5_level0_col2\" class=\"col_heading level0 col2\" >Coefficient Estimation</th>\n",
+       "      <th id=\"T_279d5_level0_col3\" class=\"col_heading level0 col3\" >Std. Err</th>\n",
+       "      <th id=\"T_279d5_level0_col4\" class=\"col_heading level0 col4\" >z_value</th>\n",
+       "      <th id=\"T_279d5_level0_col5\" class=\"col_heading level0 col5\" >P(.>z)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_279d5_row0_col0\" class=\"data row0 col0\" >0</td>\n",
+       "      <td id=\"T_279d5_row0_col1\" class=\"data row0 col1\" >Weights_items_features_0</td>\n",
+       "      <td id=\"T_279d5_row0_col2\" class=\"data row0 col2\" >-0.675645</td>\n",
+       "      <td id=\"T_279d5_row0_col3\" class=\"data row0 col3\" >0.023987</td>\n",
+       "      <td id=\"T_279d5_row0_col4\" class=\"data row0 col4\" >-28.167109</td>\n",
+       "      <td id=\"T_279d5_row0_col5\" class=\"data row0 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_279d5_row1_col0\" class=\"data row1 col0\" >0</td>\n",
+       "      <td id=\"T_279d5_row1_col1\" class=\"data row1 col1\" >Weights_items_features_1</td>\n",
+       "      <td id=\"T_279d5_row1_col2\" class=\"data row1 col2\" >-0.060604</td>\n",
+       "      <td id=\"T_279d5_row1_col3\" class=\"data row1 col3\" >0.008162</td>\n",
+       "      <td id=\"T_279d5_row1_col4\" class=\"data row1 col4\" >-7.424849</td>\n",
+       "      <td id=\"T_279d5_row1_col5\" class=\"data row1 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_279d5_row2_col0\" class=\"data row2 col0\" >0</td>\n",
+       "      <td id=\"T_279d5_row2_col1\" class=\"data row2 col1\" >Weights_items_features_2</td>\n",
+       "      <td id=\"T_279d5_row2_col2\" class=\"data row2 col2\" >1.851951</td>\n",
+       "      <td id=\"T_279d5_row2_col3\" class=\"data row2 col3\" >0.054914</td>\n",
+       "      <td id=\"T_279d5_row2_col4\" class=\"data row2 col4\" >33.724579</td>\n",
+       "      <td id=\"T_279d5_row2_col5\" class=\"data row2 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_279d5_row3_col0\" class=\"data row3 col0\" >0</td>\n",
+       "      <td id=\"T_279d5_row3_col1\" class=\"data row3 col1\" >Weights_items_features_3</td>\n",
+       "      <td id=\"T_279d5_row3_col2\" class=\"data row3 col2\" >1.322549</td>\n",
+       "      <td id=\"T_279d5_row3_col3\" class=\"data row3 col3\" >0.048159</td>\n",
+       "      <td id=\"T_279d5_row3_col4\" class=\"data row3 col4\" >27.462420</td>\n",
+       "      <td id=\"T_279d5_row3_col5\" class=\"data row3 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_279d5_row4_col0\" class=\"data row4 col0\" >0</td>\n",
+       "      <td id=\"T_279d5_row4_col1\" class=\"data row4 col1\" >Weights_items_features_4</td>\n",
+       "      <td id=\"T_279d5_row4_col2\" class=\"data row4 col2\" >-5.857089</td>\n",
+       "      <td id=\"T_279d5_row4_col3\" class=\"data row4 col3\" >0.191162</td>\n",
+       "      <td id=\"T_279d5_row4_col4\" class=\"data row4 col4\" >-30.639460</td>\n",
+       "      <td id=\"T_279d5_row4_col5\" class=\"data row4 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_279d5_row5_col0\" class=\"data row5 col0\" >0</td>\n",
+       "      <td id=\"T_279d5_row5_col1\" class=\"data row5 col1\" >Weights_items_features_5</td>\n",
+       "      <td id=\"T_279d5_row5_col2\" class=\"data row5 col2\" >-6.513206</td>\n",
+       "      <td id=\"T_279d5_row5_col3\" class=\"data row5 col3\" >0.195680</td>\n",
+       "      <td id=\"T_279d5_row5_col4\" class=\"data row5 col4\" >-33.285046</td>\n",
+       "      <td id=\"T_279d5_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_279d5_row6_col0\" class=\"data row6 col0\" >1</td>\n",
+       "      <td id=\"T_279d5_row6_col1\" class=\"data row6 col1\" >Weights_items_features_0</td>\n",
+       "      <td id=\"T_279d5_row6_col2\" class=\"data row6 col2\" >-1.817566</td>\n",
+       "      <td id=\"T_279d5_row6_col3\" class=\"data row6 col3\" >0.077771</td>\n",
+       "      <td id=\"T_279d5_row6_col4\" class=\"data row6 col4\" >-23.370796</td>\n",
+       "      <td id=\"T_279d5_row6_col5\" class=\"data row6 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_279d5_row7_col0\" class=\"data row7 col0\" >1</td>\n",
+       "      <td id=\"T_279d5_row7_col1\" class=\"data row7 col1\" >Weights_items_features_1</td>\n",
+       "      <td id=\"T_279d5_row7_col2\" class=\"data row7 col2\" >-1.726365</td>\n",
+       "      <td id=\"T_279d5_row7_col3\" class=\"data row7 col3\" >0.058838</td>\n",
+       "      <td id=\"T_279d5_row7_col4\" class=\"data row7 col4\" >-29.340986</td>\n",
+       "      <td id=\"T_279d5_row7_col5\" class=\"data row7 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_279d5_row8_col0\" class=\"data row8 col0\" >1</td>\n",
+       "      <td id=\"T_279d5_row8_col1\" class=\"data row8 col1\" >Weights_items_features_2</td>\n",
+       "      <td id=\"T_279d5_row8_col2\" class=\"data row8 col2\" >3.696567</td>\n",
+       "      <td id=\"T_279d5_row8_col3\" class=\"data row8 col3\" >0.160258</td>\n",
+       "      <td id=\"T_279d5_row8_col4\" class=\"data row8 col4\" >23.066404</td>\n",
+       "      <td id=\"T_279d5_row8_col5\" class=\"data row8 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_279d5_row9_col0\" class=\"data row9 col0\" >1</td>\n",
+       "      <td id=\"T_279d5_row9_col1\" class=\"data row9 col1\" >Weights_items_features_3</td>\n",
+       "      <td id=\"T_279d5_row9_col2\" class=\"data row9 col2\" >4.111840</td>\n",
+       "      <td id=\"T_279d5_row9_col3\" class=\"data row9 col3\" >0.157179</td>\n",
+       "      <td id=\"T_279d5_row9_col4\" class=\"data row9 col4\" >26.160225</td>\n",
+       "      <td id=\"T_279d5_row9_col5\" class=\"data row9 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_279d5_row10_col0\" class=\"data row10 col0\" >1</td>\n",
+       "      <td id=\"T_279d5_row10_col1\" class=\"data row10 col1\" >Weights_items_features_4</td>\n",
+       "      <td id=\"T_279d5_row10_col2\" class=\"data row10 col2\" >-26.693516</td>\n",
+       "      <td id=\"T_279d5_row10_col3\" class=\"data row10 col3\" >3.274723</td>\n",
+       "      <td id=\"T_279d5_row10_col4\" class=\"data row10 col4\" >-8.151381</td>\n",
+       "      <td id=\"T_279d5_row10_col5\" class=\"data row10 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_279d5_row11_col0\" class=\"data row11 col0\" >1</td>\n",
+       "      <td id=\"T_279d5_row11_col1\" class=\"data row11 col1\" >Weights_items_features_5</td>\n",
+       "      <td id=\"T_279d5_row11_col2\" class=\"data row11 col2\" >-14.925840</td>\n",
+       "      <td id=\"T_279d5_row11_col3\" class=\"data row11 col3\" >0.634699</td>\n",
+       "      <td id=\"T_279d5_row11_col4\" class=\"data row11 col4\" >-23.516403</td>\n",
+       "      <td id=\"T_279d5_row11_col5\" class=\"data row11 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_279d5_row12_col0\" class=\"data row12 col0\" >2</td>\n",
+       "      <td id=\"T_279d5_row12_col1\" class=\"data row12 col1\" >Weights_items_features_0</td>\n",
+       "      <td id=\"T_279d5_row12_col2\" class=\"data row12 col2\" >-2.104791</td>\n",
+       "      <td id=\"T_279d5_row12_col3\" class=\"data row12 col3\" >0.104296</td>\n",
+       "      <td id=\"T_279d5_row12_col4\" class=\"data row12 col4\" >-20.181009</td>\n",
+       "      <td id=\"T_279d5_row12_col5\" class=\"data row12 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_279d5_row13_col0\" class=\"data row13 col0\" >2</td>\n",
+       "      <td id=\"T_279d5_row13_col1\" class=\"data row13 col1\" >Weights_items_features_1</td>\n",
+       "      <td id=\"T_279d5_row13_col2\" class=\"data row13 col2\" >-1.652622</td>\n",
+       "      <td id=\"T_279d5_row13_col3\" class=\"data row13 col3\" >0.073820</td>\n",
+       "      <td id=\"T_279d5_row13_col4\" class=\"data row13 col4\" >-22.387188</td>\n",
+       "      <td id=\"T_279d5_row13_col5\" class=\"data row13 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_279d5_row14_col0\" class=\"data row14 col0\" >2</td>\n",
+       "      <td id=\"T_279d5_row14_col1\" class=\"data row14 col1\" >Weights_items_features_2</td>\n",
+       "      <td id=\"T_279d5_row14_col2\" class=\"data row14 col2\" >-5.554287</td>\n",
+       "      <td id=\"T_279d5_row14_col3\" class=\"data row14 col3\" >0.245318</td>\n",
+       "      <td id=\"T_279d5_row14_col4\" class=\"data row14 col4\" >-22.641151</td>\n",
+       "      <td id=\"T_279d5_row14_col5\" class=\"data row14 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_279d5_row15_col0\" class=\"data row15 col0\" >2</td>\n",
+       "      <td id=\"T_279d5_row15_col1\" class=\"data row15 col1\" >Weights_items_features_3</td>\n",
+       "      <td id=\"T_279d5_row15_col2\" class=\"data row15 col2\" >-13.565555</td>\n",
+       "      <td id=\"T_279d5_row15_col3\" class=\"data row15 col3\" >0.544168</td>\n",
+       "      <td id=\"T_279d5_row15_col4\" class=\"data row15 col4\" >-24.928965</td>\n",
+       "      <td id=\"T_279d5_row15_col5\" class=\"data row15 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "      <td id=\"T_279d5_row16_col0\" class=\"data row16 col0\" >2</td>\n",
+       "      <td id=\"T_279d5_row16_col1\" class=\"data row16 col1\" >Weights_items_features_4</td>\n",
+       "      <td id=\"T_279d5_row16_col2\" class=\"data row16 col2\" >-9.794930</td>\n",
+       "      <td id=\"T_279d5_row16_col3\" class=\"data row16 col3\" >0.631004</td>\n",
+       "      <td id=\"T_279d5_row16_col4\" class=\"data row16 col4\" >-15.522781</td>\n",
+       "      <td id=\"T_279d5_row16_col5\" class=\"data row16 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_279d5_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "      <td id=\"T_279d5_row17_col0\" class=\"data row17 col0\" >2</td>\n",
+       "      <td id=\"T_279d5_row17_col1\" class=\"data row17 col1\" >Weights_items_features_5</td>\n",
+       "      <td id=\"T_279d5_row17_col2\" class=\"data row17 col2\" >-12.126673</td>\n",
+       "      <td id=\"T_279d5_row17_col3\" class=\"data row17 col3\" >0.681118</td>\n",
+       "      <td id=\"T_279d5_row17_col4\" class=\"data row17 col4\" >-17.804060</td>\n",
+       "      <td id=\"T_279d5_row17_col5\" class=\"data row17 col5\" >0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f7d56306970>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "report = lc_model.compute_report(elec_dataset)\n",
+    "\n",
+    "def format_color_groups(df):\n",
+    "    cmap = mpl.cm.get_cmap(\"Set1\")\n",
+    "    colors = [mpl.colors.rgb2hex(cmap(i)) for i in range(cmap.N)]\n",
+    "    x = df.copy()\n",
+    "    factors = list(x['Latent Class'].unique())\n",
+    "    i = 0\n",
+    "    for factor in factors:\n",
+    "        style = f'background-color: {colors[i]}'\n",
+    "        x.loc[x['Latent Class'] == factor, :] = style\n",
+    "        i += 1\n",
+    "    return x\n",
+    "\n",
+    "report.style.apply(format_color_groups, axis=None)"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "keep_output": true
+   },
    "source": [
     "## Latent Conditional Logit\n",
     "We used a very simple MNL. Here we simulate the same MNL, by using the Conditional-Logit formulation.\\\n",
@@ -281,7 +527,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "tf_env",
+   "display_name": "choice_learn",
    "language": "python",
    "name": "python3"
   },
@@ -295,7 +541,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,