{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R2AxpObRncMd"
   },
   "source": [
    "***Copyright 2020 The TensorFlow Authors.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:34.629488Z",
     "iopub.status.busy": "2024-12-15T12:24:34.628857Z",
     "iopub.status.idle": "2024-12-15T12:24:34.633506Z",
     "shell.execute_reply": "2024-12-15T12:24:34.632847Z"
    },
    "id": "gQ5Kfh1YnkFS"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uc0VwsT5nvQi"
   },
   "source": [
    "# Shape Constraints for Ethics with Tensorflow Lattice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gqJQZdvfn32j"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/lattice/tutorials/shape_constraints_for_ethics\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/lattice/blob/master/docs/tutorials/shape_constraints_for_ethics.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/lattice/blob/master/docs/tutorials/shape_constraints_for_ethics.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/lattice/docs/tutorials/shape_constraints_for_ethics.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFZbuZMAoBny"
   },
   "source": [
    "## Overview\n",
    "\n",
    "This tutorial demonstrates how the TensorFlow Lattice (TFL) library can be used\n",
    "to train models that behave *responsibly*, and do not violate certain\n",
    "assumptions that are *ethical* or *fair*. In particular, we will focus on using monotonicity constraints to avoid *unfair penalization* of certain attributes. This tutorial includes demonstrations\n",
    "of the experiments from the paper\n",
    "[*Deontological Ethics By Monotonicity Shape Constraints*](https://arxiv.org/abs/2001.11990)\n",
    "by Serena Wang and Maya Gupta, published at\n",
    "[AISTATS 2020](https://www.aistats.org/).\n",
    "\n",
    "We will use TFL premade models on public datasets, but note that\n",
    "everything in this tutorial can also be done with models constructed from TFL\n",
    "Keras layers.\n",
    "\n",
    "Before proceeding, make sure your runtime has all required packages installed\n",
    "(as imported in the code cells below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o4L76T-NpgCS"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6FvmHcqbpkL7"
   },
   "source": [
    "Installing TF Lattice package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:34.636909Z",
     "iopub.status.busy": "2024-12-15T12:24:34.636375Z",
     "iopub.status.idle": "2024-12-15T12:24:38.982879Z",
     "shell.execute_reply": "2024-12-15T12:24:38.981754Z"
    },
    "id": "f91yvUt_peYs"
   },
   "outputs": [
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     ]
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      "Requirement already satisfied: markdown-it-py>=2.2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from rich->keras>=3.5.0->tensorflow==2.18.0->tensorflow_decision_forests) (3.0.0)\r\n",
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      "Requirement already satisfied: zipp>=3.20 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.19,>=2.18->tensorflow==2.18.0->tensorflow_decision_forests) (3.21.0)\r\n",
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      "Using cached tensorflow_decision_forests-1.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.9 MB)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cached wurlitzer-3.1.1-py3-none-any.whl (8.6 kB)\r\n",
      "Using cached ydf-0.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.5 MB)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: ydf, wurlitzer, tensorflow_decision_forests\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed tensorflow_decision_forests-1.11.0 wurlitzer-3.1.1 ydf-0.9.0\r\n"
     ]
    }
   ],
   "source": [
    "#@test {\"skip\": true}\n",
    "!pip install -U tensorflow tf-keras tensorflow-lattice seaborn pydot graphviz\n",
    "!pip install -U tensorflow_decision_forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6TDoQsvSpmfx"
   },
   "source": [
    "Importing required packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:38.986757Z",
     "iopub.status.busy": "2024-12-15T12:24:38.986125Z",
     "iopub.status.idle": "2024-12-15T12:24:42.703530Z",
     "shell.execute_reply": "2024-12-15T12:24:42.702766Z"
    },
    "id": "KGt0pm0b1O5X"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-15 12:24:39.272389: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1734265479.296062   73126 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1734265479.303333   73126 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<p style=\"margin:0px;\">🌲 Try <a href=\"https://ydf.readthedocs.io/en/latest/\" target=\"_blank\">YDF</a>, the successor of\n",
       "    <a href=\"https://www.tensorflow.org/decision_forests\" target=\"_blank\">TensorFlow\n",
       "        Decision Forests</a> using the same algorithms but with more features and faster\n",
       "    training!\n",
       "</p>\n",
       "<div style=\"display: flex; flex-wrap: wrap; margin:5px;max-width: 880px;\">\n",
       "    <div style=\"flex: 1; border-radius: 10px; background-color: F0F0F0; padding: 5px;\">\n",
       "        <p\n",
       "            style=\"font-weight: bold; margin:0px;text-align: center;border-bottom: 1px solid #C0C0C0;margin-bottom: 4px;\">\n",
       "            Old code</p>\n",
       "        <pre style=\"overflow-wrap: anywhere; overflow: auto; margin:0px;font-size: 9pt;\">\n",
       "import tensorflow_decision_forests as tfdf\n",
       "\n",
       "tf_ds = tfdf.keras.pd_dataframe_to_tf_dataset(ds, label=\"l\")\n",
       "model = tfdf.keras.RandomForestModel(label=\"l\")\n",
       "model.fit(tf_ds)\n",
       "</pre>\n",
       "    </div>\n",
       "    <div style=\"width: 5px;\"></div>\n",
       "    <div style=\"flex: 1; border-radius: 10px; background-color: F0F0F0; padding: 5px;\">\n",
       "        <p\n",
       "            style=\"font-weight: bold; margin:0px;text-align: center;border-bottom: 1px solid #C0C0C0;margin-bottom: 4px;\">\n",
       "            New code</p>\n",
       "        <pre style=\"overflow-wrap: anywhere; overflow: auto; margin:0px;font-size: 9pt;\">\n",
       "import ydf\n",
       "\n",
       "model = ydf.RandomForestLearner(label=\"l\").train(ds)\n",
       "</pre>\n",
       "    </div>\n",
       "</div>\n",
       "<p style=\"margin:0px;font-size: 9pt;\">(Learn more in the <a\n",
       "        href=\"https://ydf.readthedocs.io/en/latest/tutorial/migrating_to_ydf/\" target=\"_blank\">migration\n",
       "        guide</a>)</p>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_lattice as tfl\n",
    "import tensorflow_decision_forests as tfdf\n",
    "\n",
    "import logging\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sys\n",
    "import tempfile\n",
    "logging.disable(sys.maxsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:42.706944Z",
     "iopub.status.busy": "2024-12-15T12:24:42.706622Z",
     "iopub.status.idle": "2024-12-15T12:24:42.713584Z",
     "shell.execute_reply": "2024-12-15T12:24:42.712959Z"
    },
    "id": "csVitiM20zAY"
   },
   "outputs": [],
   "source": [
    "# Use Keras 2.\n",
    "version_fn = getattr(tf.keras, \"version\", None)\n",
    "if version_fn and version_fn().startswith(\"3.\"):\n",
    "  import tf_keras as keras\n",
    "else:\n",
    "  keras = tf.keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DFN6GOcBAqzv"
   },
   "source": [
    "Default values used in this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:42.716456Z",
     "iopub.status.busy": "2024-12-15T12:24:42.715971Z",
     "iopub.status.idle": "2024-12-15T12:24:42.719326Z",
     "shell.execute_reply": "2024-12-15T12:24:42.718720Z"
    },
    "id": "9uqMM2joAnoW"
   },
   "outputs": [],
   "source": [
    "# Default number of training epochs, batch sizes and learning rate.\n",
    "NUM_EPOCHS = 256\n",
    "BATCH_SIZE = 256\n",
    "LEARNING_RATES = 0.01\n",
    "# Directory containing dataset files.\n",
    "DATA_DIR = 'https://raw.githubusercontent.com/serenalwang/shape_constraints_for_ethics/master'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OZJQfJvY3ibC"
   },
   "source": [
    "# Case study #1: Law school admissions\n",
    "\n",
    "In the first part of this tutorial, we will consider a case study using the Law\n",
    "School Admissions dataset from the Law School Admissions Council (LSAC). We will\n",
    "train a classifier to predict whether or not a student will pass the bar using\n",
    "two features: the student's LSAT score and undergraduate GPA.\n",
    "\n",
    "Suppose that the classifier’s score was used to guide law school admissions or\n",
    "scholarships. According to merit-based social norms, we would expect that\n",
    "students with higher GPA and higher LSAT score should receive a higher score\n",
    "from the classifier. However, we will observe that it is easy for models to\n",
    "violate these intuitive norms, and sometimes penalize people for having a higher\n",
    "GPA or LSAT score.\n",
    "\n",
    "To address this *unfair penalization* problem, we can impose monotonicity\n",
    "constraints so that a model never penalizes higher GPA or higher LSAT score, all\n",
    "else equal. In this tutorial, we will show how to impose those monotonicity\n",
    "constraints using TFL."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vJES8lYT1fHN"
   },
   "source": [
    "## Load Law School Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:42.722182Z",
     "iopub.status.busy": "2024-12-15T12:24:42.721687Z",
     "iopub.status.idle": "2024-12-15T12:24:43.315843Z",
     "shell.execute_reply": "2024-12-15T12:24:43.314937Z"
    },
    "id": "Cl89ZOsQ14An"
   },
   "outputs": [],
   "source": [
    "# Load data file.\n",
    "law_file_name = 'lsac.csv'\n",
    "law_file_path = os.path.join(DATA_DIR, law_file_name)\n",
    "raw_law_df = pd.read_csv(law_file_path, delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RCpTYCNjqOsC"
   },
   "source": [
    "Preprocess dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:43.319536Z",
     "iopub.status.busy": "2024-12-15T12:24:43.318962Z",
     "iopub.status.idle": "2024-12-15T12:24:43.322293Z",
     "shell.execute_reply": "2024-12-15T12:24:43.321640Z"
    },
    "id": "jdY5rtLs4xQK"
   },
   "outputs": [],
   "source": [
    "# Define label column name.\n",
    "LAW_LABEL = 'pass_bar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:43.324939Z",
     "iopub.status.busy": "2024-12-15T12:24:43.324480Z",
     "iopub.status.idle": "2024-12-15T12:24:43.332829Z",
     "shell.execute_reply": "2024-12-15T12:24:43.332203Z"
    },
    "id": "1t1Hd8gu6Uat"
   },
   "outputs": [],
   "source": [
    "def preprocess_law_data(input_df):\n",
    "  # Drop rows with where the label or features of interest are missing.\n",
    "  output_df = input_df[~input_df[LAW_LABEL].isna() & ~input_df['ugpa'].isna() &\n",
    "                       (input_df['ugpa'] > 0) & ~input_df['lsat'].isna()]\n",
    "  return output_df\n",
    "\n",
    "\n",
    "law_df = preprocess_law_data(raw_law_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YhvSKr9SCrHP"
   },
   "source": [
    "### Split data into train/validation/test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:43.335635Z",
     "iopub.status.busy": "2024-12-15T12:24:43.335168Z",
     "iopub.status.idle": "2024-12-15T12:24:43.466211Z",
     "shell.execute_reply": "2024-12-15T12:24:43.465431Z"
    },
    "id": "gQKkIGD-CvGD"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-15 12:24:43.408373: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
     ]
    }
   ],
   "source": [
    "def split_dataset(input_df, random_state=888):\n",
    "  \"\"\"Splits an input dataset into train, val, and test sets.\"\"\"\n",
    "  train_df, test_val_df = train_test_split(\n",
    "      input_df, test_size=0.3, random_state=random_state\n",
    "  )\n",
    "  val_df, test_df = train_test_split(\n",
    "      test_val_df, test_size=0.66, random_state=random_state\n",
    "  )\n",
    "  return train_df, val_df, test_df\n",
    "\n",
    "\n",
    "dataframes = {}\n",
    "datasets = {}\n",
    "\n",
    "(dataframes['law_train'], dataframes['law_val'], dataframes['law_test']) = (\n",
    "    split_dataset(law_df)\n",
    ")\n",
    "\n",
    "for df_name, df in dataframes.items():\n",
    "  datasets[df_name] = tf.data.Dataset.from_tensor_slices(\n",
    "      ((df[['ugpa']], df[['lsat']]), df[['pass_bar']])\n",
    "  ).batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zObwzY7f3aLy"
   },
   "source": [
    "### Visualize data distribution\n",
    "\n",
    "First we will visualize the distribution of the data. We will plot the GPA and\n",
    "LSAT scores for all students that passed the bar and also for all students that\n",
    "did not pass the bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:43.469623Z",
     "iopub.status.busy": "2024-12-15T12:24:43.469038Z",
     "iopub.status.idle": "2024-12-15T12:24:43.474839Z",
     "shell.execute_reply": "2024-12-15T12:24:43.474215Z"
    },
    "id": "dRAZB5cLORUG"
   },
   "outputs": [],
   "source": [
    "def plot_dataset_contour(input_df, title):\n",
    "  plt.rcParams['font.family'] = ['serif']\n",
    "  g = sns.jointplot(\n",
    "      x='ugpa',\n",
    "      y='lsat',\n",
    "      data=input_df,\n",
    "      kind='kde',\n",
    "      xlim=[1.4, 4],\n",
    "      ylim=[0, 50])\n",
    "  g.plot_joint(plt.scatter, c='b', s=10, linewidth=1, marker='+')\n",
    "  g.ax_joint.collections[0].set_alpha(0)\n",
    "  g.set_axis_labels('Undergraduate GPA', 'LSAT score', fontsize=14)\n",
    "  g.fig.suptitle(title, fontsize=14)\n",
    "  # Adust plot so that the title fits.\n",
    "  plt.subplots_adjust(top=0.9)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:43.477444Z",
     "iopub.status.busy": "2024-12-15T12:24:43.476984Z",
     "iopub.status.idle": "2024-12-15T12:24:57.979982Z",
     "shell.execute_reply": "2024-12-15T12:24:57.979322Z"
    },
    "id": "feovlsWPQhVG"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "law_df_pos = law_df[law_df[LAW_LABEL] == 1]\n",
    "plot_dataset_contour(\n",
    "    law_df_pos, title='Distribution of students that passed the bar')\n",
    "law_df_neg = law_df[law_df[LAW_LABEL] == 0]\n",
    "plot_dataset_contour(\n",
    "    law_df_neg, title='Distribution of students that failed the bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6grrFEMPfPjk"
   },
   "source": [
    "## Train calibrated lattice model to predict bar exam passage\n",
    "\n",
    "Next, we will train a *calibrated lattice model* from TFL to predict whether or\n",
    "not a student will pass the bar. The two input features will be LSAT score and\n",
    "undergraduate GPA, and the training label will be whether the student passed the\n",
    "bar.\n",
    "\n",
    "We will first train a calibrated lattice model without any constraints. Then, we\n",
    "will train a calibrated lattice model with monotonicity constraints and observe\n",
    "the difference in the model output and accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HSfAwgiO_6YA"
   },
   "source": [
    "### Helper functions for visualization of trained model outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:57.983359Z",
     "iopub.status.busy": "2024-12-15T12:24:57.982813Z",
     "iopub.status.idle": "2024-12-15T12:24:57.991166Z",
     "shell.execute_reply": "2024-12-15T12:24:57.990538Z"
    },
    "id": "aw28Xc7IS6vR"
   },
   "outputs": [],
   "source": [
    "def plot_model_contour(model, from_logits=False, num_keypoints=20):\n",
    "  x = np.linspace(min(law_df['ugpa']), max(law_df['ugpa']), num_keypoints)\n",
    "  y = np.linspace(min(law_df['lsat']), max(law_df['lsat']), num_keypoints)\n",
    "\n",
    "  x_grid, y_grid = np.meshgrid(x, y)\n",
    "\n",
    "  positions = np.vstack([x_grid.ravel(), y_grid.ravel()])\n",
    "  plot_df = pd.DataFrame(positions.T, columns=['ugpa', 'lsat'])\n",
    "  plot_df[LAW_LABEL] = np.ones(len(plot_df))\n",
    "  predictions = model.predict((plot_df[['ugpa']], plot_df[['lsat']]))\n",
    "  if from_logits:\n",
    "    predictions = tf.math.sigmoid(predictions)\n",
    "  grid_predictions = np.reshape(predictions, x_grid.shape)\n",
    "\n",
    "  plt.rcParams['font.family'] = ['serif']\n",
    "  plt.contour(\n",
    "      x_grid,\n",
    "      y_grid,\n",
    "      grid_predictions,\n",
    "      colors=('k',),\n",
    "      levels=np.linspace(0, 1, 11),\n",
    "  )\n",
    "  plt.contourf(\n",
    "      x_grid,\n",
    "      y_grid,\n",
    "      grid_predictions,\n",
    "      cmap=plt.cm.bone,\n",
    "      levels=np.linspace(0, 1, 11),\n",
    "  )\n",
    "  plt.xticks(fontsize=20)\n",
    "  plt.yticks(fontsize=20)\n",
    "\n",
    "  cbar = plt.colorbar()\n",
    "  cbar.ax.set_ylabel('Model score', fontsize=20)\n",
    "  cbar.ax.tick_params(labelsize=20)\n",
    "\n",
    "  plt.xlabel('Undergraduate GPA', fontsize=20)\n",
    "  plt.ylabel('LSAT score', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fAMSCaRHIn1w"
   },
   "source": [
    "## Train unconstrained (non-monotonic) calibrated lattice model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mK7RWDJ5ugdd"
   },
   "source": [
    "We create a TFL premade model using a '`CalibratedLatticeConfig`. This model is a calibrated lattice model with an output calibration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:57.994168Z",
     "iopub.status.busy": "2024-12-15T12:24:57.993616Z",
     "iopub.status.idle": "2024-12-15T12:24:57.998459Z",
     "shell.execute_reply": "2024-12-15T12:24:57.997806Z"
    },
    "id": "J16TOicHQ1sM"
   },
   "outputs": [],
   "source": [
    "model_config = tfl.configs.CalibratedLatticeConfig(\n",
    "    feature_configs=[\n",
    "        tfl.configs.FeatureConfig(\n",
    "            name='ugpa',\n",
    "            lattice_size=3,\n",
    "            pwl_calibration_num_keypoints=16,\n",
    "            monotonicity=0,\n",
    "            pwl_calibration_always_monotonic=False,\n",
    "        ),\n",
    "        tfl.configs.FeatureConfig(\n",
    "            name='lsat',\n",
    "            lattice_size=3,\n",
    "            pwl_calibration_num_keypoints=16,\n",
    "            monotonicity=0,\n",
    "            pwl_calibration_always_monotonic=False,\n",
    "        ),\n",
    "    ],\n",
    "    output_calibration=True,\n",
    "    output_initialization=np.linspace(-2, 2, num=8),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jt1Rm6qCuuat"
   },
   "source": [
    "We calculate and populate feature quantiles in the feature configs using the `premade_lib` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:58.001225Z",
     "iopub.status.busy": "2024-12-15T12:24:58.000685Z",
     "iopub.status.idle": "2024-12-15T12:24:58.008032Z",
     "shell.execute_reply": "2024-12-15T12:24:58.007418Z"
    },
    "id": "eSELqBdURE0F"
   },
   "outputs": [],
   "source": [
    "feature_keypoints = tfl.premade_lib.compute_feature_keypoints(\n",
    "    feature_configs=model_config.feature_configs,\n",
    "    features=dataframes['law_train'][['ugpa', 'lsat', 'pass_bar']],\n",
    ")\n",
    "tfl.premade_lib.set_feature_keypoints(\n",
    "    feature_configs=model_config.feature_configs,\n",
    "    feature_keypoints=feature_keypoints,\n",
    "    add_missing_feature_configs=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:24:58.010658Z",
     "iopub.status.busy": "2024-12-15T12:24:58.010140Z",
     "iopub.status.idle": "2024-12-15T12:25:01.109143Z",
     "shell.execute_reply": "2024-12-15T12:25:01.108376Z"
    },
    "id": "ahV2Sn0Xz1aO"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S1dV1cHAQi8VHjx4dVMGqpaVVWVk5ZcqU7du3l5aWfvfdd3IdqqqqDh06FBAQMG3atJ6eHjp7nyaZTEa93qqYxn2iGQPN9F62bBlC6OzZs2RLR0fHxYsX+Xz+EOp+zYFvv7Gysvrss8/u3btXVFQUFham2v+daMKp9dtvvx06dEhu0fPnz8PDww0MDKhnZBGN45sS9I9RQ6Y8b/FTNqhzzzU1NVVVVZEvVftLBAAA9KmhYBUKhZ999llhYeGzZ89KS0uDgoI4HE5SUhLZYcmSJWKxeP/+/TKZ7P79+2FhYYonJ2bPnl1dXf348ePCwsIHDx44OTnR2TSLxVq0aFF9ff3u3bubmpra2tpyc3PxcFo5ISEhfD4/Ojp68eLFctVbXFyclZXVBx98cP78+dbW1paWlkOHDsXExCQkJIyhyT5xqOPGjfvHP/5RUFBQV1eXlJQ0Cg8Pw3VqT08PfrIACRdY2tradGanUhQdHR0ZGbl+/frIyMjq6urOzs7a2tojR464uLjY2NgcOXIEDWbv06GjoxMaGlpUVNRfGveJZgw00zsuLs7S0jI8PDwjI0MqlVZXV7/33nt4V6qlvBsyfOUa16nm5uZbt26tqKjA/zu9+uqr6o0tOjp669atGzZsiIiIKC8v7+jokEgkGRkZjo6O9fX1WVlZchEOeHxTTrVZqkh53u7atWvcuHHh4eE5OTkymezOnTtBQUHUs60jHR4AAPSLOgKL5iwBcrfl7d69u7CwkNoSFRUld3nIy8sLvxcPwb5z5467u7tAIODz+c7Ozvn5+dT1SySSNWvWTJgwgc/nOzo6lpSU2NnZ4fVs2bIF96msrHRyctLR0TEzM0tOTu4zMEUVFRWNjY3r1q0zMzNjs9nGxsbBwcHkxT7qMH+CIPBjk6ijbkl48stXX32VzWYbGhouWbKEnL9J7qug831iozlLgI6OzqpVq7Kysrq7u4cZj9x3LjcqWZFEItHS0pIbZUz8NXQ6ODhY+fr73Kdk5+Li4pUrV+KdKxAIFixYkJSU1NHRQXZQvvd3794tl8Z4VBNpxYoVZB9TU9Pi4mIXFxddXV25NFaeA3QyUDG9FSPBPZuamsLDwy0tLdlstlAodHd3v3jxYp9hKPmt7M+ozRKAEDI0NPz444+VT/UwqHi+//57ub0glUrJpXJ5ZWxsrHxtJSUlq1evtrCw4HA4AoFgzpw5sbGxEolErtuAx7fhH6MUd6vc0xbi4uLy8vKoLdu3b6eTt1hVVdXSpUtfeeUVPP1ZRkaGq6srfu+HH35I0EtgJRDMEgAAGBIGQfkbdvLkycDAQGIkb0WaNWtWU1NTTU3NyG1CVb7//vvk5OTS0tLR2RyeUObUqVMjuhX6j2YdnXiAJhudHKD/aFbNz8kxdHxTFwaDkZqaOtJTTwAAXjxj5hL26Dt48GBERIS6o1Axa2trPF8jABrizTffVHcIAAAANJ0a7mHVZN9+++2yZctkMtnBgwefPn0KpwEAAAAAANRu9ApW/KztGzdu1NbWMhiM6OjoUdv0oKSlpenr6x84cODEiRNjaBAVAECNxsrxDQAAxqjRK8g2bdq0adOmUdvc0KxZs2bNmjXqjgIAMMaMieMbAACMXXBLAAAAAAAA0GhQsAIAAAAAAI0G92gCAAB4SY30k4fHkIULF06cOFHdUfwX7BeAFHISClYAAAAvqcDAQHWHoCk0an5c2C8AKeQkFKwAAABeXhpVqKkLg8FQdwjyYL+85BRzEu5hBQAAAAAAGg3OsAIAABg9cXFxVVVVCxcunDdvnkAgUHc4AICxAc6wAgAAGD26urrffffd4sWL9fX1X3/99dDQ0OPHj//+++/qjgsAoNHgDCsAAIDR89FHHwUEBNTX15eUlBQUFOTn5x85cqS9vV0kEs2ZM8fOzs7R0dHR0ZHH46k7UgCABoEzrAAAAEabSCTy8fGJj4/Pz8//888/S0tLIyMj+Xz+gQMH3NzcBALBnDlzwsLCfvjhh0ePHg1tE+vWrauurlZt2AAAdYGCFQAAgDqx2Ww7O7uwsLCTJ082NDTU1tb++9//dnBwKCgoeP/99y0sLExMTAICApKSkvLz87u6uuiss7m5+fDhwzNmzIiJieno6BjpjwAAGGlwSwAAAAANYmJisnz58uXLlyOEZDLZ9evX8Z0DMTExLS0turq6tra2jo6ODg4ODg4O48aN63MlhYWFBEF0dXXFxMT88MMP33333RtvvDG6nwMAoEpwhhUAAICG0tXVdXR03LJlS3p6+pMnT27fvp2cnGxtbZ2enu7r62tgYGBlZbVq1aqUlJTy8nKCIMg3XrlyhcPhIIR6enoePnzo7OwcFBTU1NSkvo8CABiWPs6w4v9rwSi7evXqggUL1B3F31y9ehWS4WUGOQk0CovFsra2tra2XrVqFUKooaGhuLi4rKysoKAgPDy8ra1NKBTOnTvXwcHB0dExNze3s7MTv7GnpwchdPLkyfT09N27d69du1YD58kHACj3t4LVzMzM399fXaG85BYsWGBvb6/uKP6PRgUD1AJyEqicv7+/mZmZSlZlbGzs4+Pj4+ODEOru7r5x40Z+fn5ZWdnRo0d37NjBYrHk+nd1dXV3d69fv/7HH388fPjwtGnTVBIGAGB0/K1gtbe3P3XqlLpCARolIiJC3SEA8DeQk6A/WlpadnZ2dnZ2+OX//u//Ll26VLEbvmegqKjIxsbms88+27p166hGCQAYBriHFQAAwAvl4cOHbDa7v6VdXV1dXV3/+te/4CQrAGMIFKwAAABeKFeuXOnt7VXehyCIhw8fIoQOHz789OnT0QgLADAMULACAAB4ofz66694oBWTyWSz2Uzm//2lEwqF06ZN8/T0/Oc//xkfH48QcnJy6u7uVrI2XV1dBkVCQsJIxw/okNsvDAaDyWQaGhouXbq0pKQE9xGJRNQOlZWV5Nv9/f0ZDEZtbS3ZEh0dTfaMj48f1H4vKSkJDg62tLTk8/njxo2bMWPG22+/feDAgfv379P5LAkJCXgrEydOHE6fFxwBAAAAvCgeP36sq6trZWXl5ua2Zs2aHTt2HD169MKFC5WVlc+fP5frjBBKTU0dcJ3Xrl1DCPn6+o5MyH2QSqWTJ0/28vIanc3R/B5GzdD2i0Qi+fnnn42MjNhsdk5ODm5MSUlBCIWHh1Pf2N3draenhxA6cuQItb25uVlPT6+zs7PP9fepp6dn06ZNWlpan376aUVFRXt7e319fXZ29uLFi3GV1dXVRfNT29rampqaki/7zAG5PqNjlLMRU8wBeHAAAACAF8fEiROlUqm6o+iDrq7urFmz8vPz6bQTBNHb2zvgjQ2ASigULlu27Pnz50FBQeHh4bdv30YIeXh4IIQyMzMTExPJnkVFRXjWs8zMzA8++IBsz8nJcXZ2VnIDtKJt27YlJCSkpKSsXbsWtxgbG7u5ub355ps+Pj7nz58f8sdRSw5ocjbCLQEAAACAZhEIBPfv3z937py6A1ElsVhMUB7uMEJcXFwQQuXl5RKJBCFkZmY2ffr0ysrKR48ekX2ysrLWr1+vq6t74cIFfPcI2e7u7k5/W5WVlfHx8XZ2dmS1SmKxWNu2bRvOB9GcHNCQSKBgBQAAAMCI27x5s4WFRXR0dHl5+chthayJycdDkCdZyT6ZmZk+Pj5vvvnm06dPi4uLyfbs7GzcmaaUlJTe3t7+nmZib29PEISWFlzKVg0oWAEAAIBh6e7uTk1NdXNzE4lEfD5/5syZSUlJ5CVUPFzm2bNnBQUFeNwMLmL6a09LSyPH+rS3t5NbaW5ujoiIsLKy4nK5EydOXLx48dGjR9va2vDSxsbGjRs3Tpo0icPhGBoa+vn5Xb9+fdS/iQHU1NR89dVXM2bMmD59enx8/B9//KHyTVy+fBkhZG1tLRQKcQs+aZqVlYVftrS0VFZWLly4UK799u3b2tralpaW9Lf166+/IoRsbGwG7Kk8QxT1lwNYZWWll5eXUCjU1tZ2cXEpKChQfFdVVVVAQICBgQF+2dTUNLQsHTAbORyOvr6+p6dnbm6uYhgPHz4MDAzU09MzMDDw9vamOQqtb6N5Cy0AAACgOZCKBl2lp6cjhHbt2tXS0tLY2Lhv3z4mk7lp0yZqHx0dHQcHB8X39tfu6+uLEGpra8Mv6+rqLC0tRSJRenr6n3/+WV9fv3PnToRQYmIiQRBisdjCwsLY2Pjs2bNSqfT27dvOzs48Hu/KlSsDfjpitAZdrVixgjpjA75V1MbGZu/evXV1dUOIR26/tLa2Kg66Igiira2Nz+cLhUI8/unEiRM+Pj4EQdy7dw8hNH/+fNwtISEhNDRUyfoVTZgwASFUVFQ0YKh0MkRxQJVcDuA+QqHQxcUlPz9fKpWWlJTY2NhwOJzLly/LvcvZ2Tk3N/fZs2dXr15lsViNjY3DydI+s9HY2Dg9Pb21tbWqqsrPz4/BYBw+fFjuLb6+vleuXJHJZDk5OXw+f+7cuQN+V5hiDkDBCgAA4CWlwoJ10aJF1JagoCA2m93a2kq2DLNgDQ4OVozWw8MDF6yrV69GCB0/fpxcVFdXx+Vy7ezsBvx0hJoKVozBYLBYLCaTuWDBgkOHDuFvbFD7hboqAwODt956q7i4WK4nPpmal5dHEMT777+/f/9+3G5lZcVkMpubmwmCcHNzy8jIUFz/gAWr4uYU0ckQmgUrQqiwsJBsuXnzJkLI1tZW7l3nzp0bQgyDysaffvqJ7NDe3m5iYsLn8+vr66lvSU9PJ/v4+/sjhBobGxXXr0gxB+DWCgAAAGBYvL29vb29qS22trbHjh0rLy+3t7dXySbOnDmDEPL09KQ2koPQ09LSmEwmNQaRSGRtbV1WVlZTU0Nn5s6rV6+SN32OELFYrNhIEAQe9lRcXFxcXBwaGurl5YUQ6urqorlaX1/ftLQ05X3c3d2zsrIyMzMdHR2zs7OjoqJwu4eHR3Jyck5ODi5zFy1aRP/jIIRMTEzq6uqampoG7KnCDOHxePPnzydfzpw508TE5MaNG3V1dbiAxubNmzdyMeBsxHsK43K5rq6uP/74Y1ZW1qpVq8j2uXPnkj+bmZkhhMRi8fjx4we1OQwKVgAAAGBYWltb9+zZc+bMmZqaGjw4HXv+/LlK1t/R0dHa2srj8QQCQX9LEULkXZtUd+/epVOwJiYmUid+Ggm6urpKluKbKQmCwNXnsWPHfH19lb+FPg8Pj4iIiKysrMDAQB6PZ2Vlhdvd3d2Tk5OzsrKEQuGcOXN0dHQGtVpnZ+eysrKbN2/K/SOhSIUZgm9LpbYYGRmJxeInT55QC1bFz6KqGPrLRmNjY4RQfX09tZGakxwOB/21o4cABl0BAAAAw+Lj47Nz5861a9dWV1f39vYSBIGLP4Iyi1N/5y/pnNfkcrlCobC9vb3PKWa5XK6enp6Wllafc9TjaZ4GNAq3BOBrxH1isVgsFktLS8vDw+N//ud/EELvv/++qqpVhND06dPNzMzKysqOHTtGnbjKxcWFw+Hgk6+DmtAKW7dunZaW1unTp/tcunnzZiaTiR+vRSdDaML/nFA9efIEIWRkZKT8jcPJUqr+srGhoQEhJBKJ6H2OQYOCFQAAABgiLS2t8vLygoICkUi0ceNGQ0ND/CefHLxP0tbWxpPVI4SmTp2Kn8CkpF3OsmXLEEJyc2G+/vrrH3/8MULIz8+vu7ubHC2Offnll+bm5sofPKteDAaDzWYzGAw7O7s9e/aIxeJz585RLyirkLu7O0EQ+/bto05cpaur6+DgIBaLjx49OqiCVUtLq7KycsqUKdu3by8tLf3uu+/kOlRVVR06dCggIGDatGk9PT10MoQmmUx248YN8uWtW7fEYrGtrS319KoimjEMKhvPnj1LtnR0dFy8eJHP5w+h7qcJClYAAABg6Fgs1qJFi+rr63fv3t3U1NTW1pabm3vw4EG5brNnz66urn78+HFhYeGDBw+cnJyUt8uJi4uztLT8+OOP8TwANTU1ISEhdRslmT4AABhFSURBVHV1uGCNi4uzsrL64IMPzp8/39ra2tLScujQoZiYmISEBM2cBxRH9dprr+3cubO2traoqCgsLMzQ0HDktojr1J6eHrlTzrjA0tbWpjM7laLo6OjIyMj169dHRkZWV1d3dnbW1tYeOXLExcXFxsbmyJEjiHaG0KSjoxMaGlpUVPTs2bPS0tKgoCAOh5OUlKT8XcPMUjk4G8PDwzMyMqRSaXV19XvvvVdXV5eUlIRvDBgRI30JAAAAANBMiMal8AHva6yoqGhsbFy3bp2ZmRmbzTY2Ng4ODo6MjMRLyXH6lZWVTk5OOjo6ZmZmycnJ5PoV2/GIFtKKFStwz6ampvDwcEtLSzabPWHChHfeeae6uppcD54X89VXX2Wz2YaGhkuWLKFO7TT872H4VqxYgT/R5MmTd+7cee/eveHEI7dfpk6dqry/RCLR0tKSGyZP/DUVQHBwsPL197nfyc7FxcUrV67ECSAQCBYsWJCUlNTR0UF2UJ4hu3fvpq45KipKMQfIPqampsXFxS4uLrq6unw+39nZOT8/H2+lsLBQLkjqJxpaltLJRqFQ6O7ufvHixT7DiIqKIv5+54OXl5fynUX0lQMMYuSfkwYAAABoIAaDkZqaGhAQoO5A1Gx0vofIyEiCIN577z08N5Pa4wGaTDEHNPFKAQAAAABeMPHx8eoOAYxhULACAAB4cdTV1a1cuXLSpEkWFhYWFhb4B1NTU828lRMAQBP8AgMAAHhxiESi0tLSS5cuaWlp9fT04EkfWSzW+PHjzc3Np06damlpaWFhYW5ubmFhoe5gAQB0QcEKAADgxcFgMOzt7bOysqiPSurp6WloaGhoaPjtt99wIUtO9hQVFfX666+/9tpraooXAEALTGsFAADghbJw4UI2m93nop6eno6Oju7ubjxTPULo888/h2oVAM33tzOsNTU1V65cUVcowMzMTFVPnR6+wsLCx48fqzsKoGaQk0DlFi5cSOdJocOxYMECcvLzPjGZTDs7u2+//dbGxobL5Y5oMAAAlfhbwXrlypXAwEB1hQL8/f1PnTql7ij+6+uvv+7vcXPg5QE5CVRuhKYrkslk169fLysrKygoyM3NZTD6nrSRzWbzeLydO3d+9NFHTCZcYwRgzOjjHlaYmVUtli9fru4Q5GlUsQJGH+QkUDk6Tyqnqaenp6Ki4urVq4WFhUVFRRUVFb29vebm5vb29lFRUXv37v3jjz+o/VksVk9PT0BAQGJi4og+UQkAMBJg0BUAAICxobW1taSkJD8/v6ys7MqVKy0tLTo6OrNmzXJ1dd28efMbb7wxadIk3PP27ds//PADOe5KS0vLzMwsJSVl8eLFaoseADAMULACAADQUN3d3VVVVQUFBbhIxQ/DnDBhgqOj4+eff25nZzdv3jwOh6P4xvnz5x89ehQhpKWlxWKxvvjii08++aS/kVgAAM0HBSsAAAANIhaLy8rK8N2oBQUFbW1tAoHAxsbGx8cnPj7e3t5+/PjxA65kwYIFPT09CKHFixcfOHCAPPMKABijoGAFAACgTl1dXTdv3sTnUPPz83///XcWizV16lQ7O7u4uDhHR8fXX399sAOk/t//+3/W1taxsbFLly4dobABAKMJClYAAACjTSwWkxf6S0tLOzo6RCLRnDlzVq1aZWdn5+joqK+vP5z1s1is69evw+NYAXhhwC8zAACA0fPll1+GhIQ0NzdzOJzZs2fPnz9/w4YN9vb2Kr9qD9UqAC8S+H0GAAAweng8XnR09Pz582fPng2T9gMAaIKCFQAAwOgJCwsbiQcHAABebPCcDwAAAAAAoNHgDCsAAICXV2FhobpDAH2A/QLkQMEKAADg5bV37969e/eqOwogD/YLkAMFKwAAgJcUQRDqDgH0AfYLUAT3sAIAAAAAAI02lIJVV1eXQZGQkKDysIZGYwN7gcl95wwGg8lkGhoaLl26tKSkBPcRiUTUDpWVleTb/f39GQxGbW0t2RIdHU32jI+PH9Q+LSkpCQ4OtrS05PP548aNmzFjxttvv33gwIH79+/T+SwJCQl4KxMnThxOH6BGs2bNYgwkNjZWMW/lfPvtt9TVlpaWBgcHT5o0icfj6enpzZ07NyYmRiKRDDY8dR2jhpa3J06cwO/i8XgjFxsAANAxlIJVJpNdu3YNIeTr60sQxKZNm1Qd1RBpbGAvMLnvnCCIp0+fpqSkFBYWOjg4XLhwASFUX1+fkpKCEAoPDycIYtq0afi9PT09Fy9eRAhlZWWRK4yNjW1ubtbT0+vs7IyMjKS5T3t7ez/99NOFCxcaGRmdP39eIpFUVFQkJib++eefISEhkydP7u7uHvCzbNq0iSAIW1tbuQ/42muveXt7K+kzOuQiAf05deoU8Zd169YhhM6fP0+2BAYGor7ylsrZ2Zm6wq1bty5YsEBfXz8jI0Mikfz+++/bt28/c+bMlClTCgoKBhWbuo5RQ8vbd955hyAIV1fXEYoKAADoG6u3BOjq6jo6Oqo7CtAHoVC4bNmyr7/+uqurKzw8HDd6eHgghDIzM6k9i4qKOjs7FdtzcnKcnZ3ZbDb9jW7bti0hIeGbb7756quvpk2bxuVyjY2N3dzcMjMzPT09h/NxCILo7e3t7e0dzkoGq8/0VkskIDY2Nj4+Pjk5OTExccaMGTweT19f39vbu6CgwNzc3NPTk3rFAAAAwAiBQVcvl6dPn/J4PD6fP9IbcnFxQQiVl5dLJBI9PT0zM7Pp06dXVFQ8evTI3Nwc98nKylq/fn1KSsqFCxd6enpYLBbZ7u7uTn9blZWV8fHxdnZ2a9eulVvEYrG2bdt2/vz5IX8QgUBA846CkaY5kahWQ0PD+PHjyb0/TNevX1fe4cSJEwOu5PLly/iHe/fu7dixY/bs2fhMLZW2tnZiYuIbb7yxcePG7OzsIQULAACArrF6hhUMTX5+/vjx44OCgs6dO9fV1TVyGyLHeDIYDPyD4knWzMxMHx+fN9988+nTp8XFxWR7dnY27kxTSkpKb2/v8uXL+1xqb29PEAQ8VVxjHTp0yNjYeOPGjYWFhWofGhwaGkpeFkAIHTx4sLu7u7/UcnJyMjExycnJefDgwWgFCAAALynVFKxpaWnkMIKHDx8GBgbq6ekZGBh4e3uT54Sod/2XlJS4uroKBAJtbW0XFxfyPrDY2Fjch7wempmZiVvGjx9PXc+zZ88KCgrwokHVIt3d3ampqW5ubiKRiM/nz5w5MykpCV9mlUgkciMzcH+yxd/fH6+ksbFx48aNkyZN4nA4hoaGfn5+5Hkd6ldRVVUVEBBgYGCAXzY1NQ33i1aF58+fp6amenl5GRoahoSE5OXljcRVZnyOytraWigU4hZ80pS8XbWlpaWysnLhwoVy7bdv39bW1ra0tKS/rV9//RUhZGNjM2BPJXu/T9S92d7eLre0srLSy8tLKBTKpbHyHFAeQ3/p3V8kzc3NERERVlZWHA5HX1/f09MzNzdXMYz+fis1hEQiOXjw4MKFC83MzKKiom7fvq3uiP7rl19+QQgpufUTL8rLy6NzfKNJeYZQd+sff/wRGBgoEAgMDAxWrlz59OnThw8f+vj4CASCCRMmrF27ViqVKq6/v7wlly5dulQoFOro6Dg5OeXn5w8qPAAAGCnUoQapqalyLf3pc7yCr68vbrxy5YpMJsvJyeHz+XPnzqX2sbW11dHRsbe3x31KSkpsbGw4HM7ly5fJPjo6Og4ODtR32dnZGRgYUFsU+ygJjCo9PR0htGvXrpaWlsbGxn379jGZTDwiAXN3d2cymffu3aO+y97e/vjx4/hnsVhsYWFhbGx89uxZqVR6+/ZtZ2dnHo935coVua/C2dk5Nzf32bNnV69eZbFYjY2N/UVFEIS/v7+/v7+SDirxn//8h7r3ORwOQsjQ0HDjxo15eXlDi0fuO29tbf3555+NjIzYbHZOTg7Zra2tjc/nC4XCrq4ugiBOnDjh4+NDEMS9e/cQQvPnz8fdEhISQkNDlaxf0YQJExBCRUVFA4Y64N4nCMLW1tbU1JTagvdmW1sbtY9QKHRxccnPz5dKpX2mcX85QCeG/tJbLpK6ujpLS0tjY+P09PTW1taqqio/Pz8Gg3H48GG5tyj/rezP6OTkjh07uFyuXE5Onjx5+/btd+/eHWY8ioOuSDivFIWFhZF9BkytoKAgvDfxSzrHt+Efo4i/dqufn19paalMJvvhhx8QQp6enr6+vteuXZNKpQcPHkQIffzxx9R3DZi3d+/e1dPTMzU1zc7OlkqlN2/eXLJkyaRJk7hc7qDCUwIhlJqaSrMzAACQVF+wpqenky34lCS1UMMnJK5du0a23Lx5EyFka2tLtox0wbpo0SJqS1BQEJvNbm1txS/xqb6QkBCyQ35+vqmpaWdnJ365evVqhBBZvxIEUVdXx+Vy7ezs5L6Kc+fO9ReGIrUUrHJVgqmp6ZYtWyorKwcVj9wffgaDYWBg8NZbbxUXF8v1xCdTcWX8/vvv79+/H7dbWVkxmczm5maCINzc3DIyMhTXP2DBqrg5RQPufYJ2wYoQwpewMcU07i8H6MRAs2ANDg5GCP30009kh/b2dhMTEz6fX19fT32L8t/K/qilYCXhUXc2NjZ79+6tq6sbWjwDFqxyebVhwwbFglVJauGCNS4uDr+kc3wb/jGK+Gu3nj17lmyxtrZGCP3yyy9ki6Wl5dSpU6nrGTBv8c0Pp0+fJjvU1tZyuVy5gnXA8JSAghUAMDSqv7Fv7ty55M9mZmYIIbFYTF7QRwjp6OjMmjWLfDlz5kwTE5MbN27U1dXhPw8jytvbW25iIFtb22PHjpWXl9vb2yOElixZMnPmzKNHj8bExBgYGCCEdu/e/dFHH5GD1tPS0phMJnUlIpHI2tq6rKyspqaGOs3hvHnzBhVbfX39qVOnhvzR6CgrK+uzHY/Wr62t3bNnz5dffjlr1iyCIAY1ZaOvr29aWpryPu7u7llZWZmZmY6OjtnZ2VFRUbjdw8MjOTk5JycHl7mLFi2iv12EkImJSV1dHZ07Lgbc+/TxeLz58+eTL/tLY8UcUGEMZ86cQQh5eXmRLVwu19XV9ccff8zKylq1ahXZPuBvZX9kMtlI52R5eXmf7fge61u3bn3yyScRERGLFi36888/TU1NRzQYOTi1mpub++uAF5mYmJAtwz++0c+QOXPmUEMtLy+ntpiamt64cUNu5crzFt9iTh3yaGJiMmXKlOrq6iGEBwAAKqT6gpW8ZxH9dd5O7vYmPT09ubcYGRmJxeInT56MQsHa2tq6Z8+eM2fO1NTUUOf9fv78OflzeHj4hx9++M0332zbtq26uvrSpUvff/89XtTR0dHa2or+/jFJd+/epRZ5Ojo6g4rt2rVrAQEBg3qLyuH5Sq9fv85gMMRi8a1bt2bOnKmqlXt4eERERGRlZQUGBvJ4PCsrK9zu7u6enJyclZUlFArnzJkz2O/N2dm5rKzs5s2bA85gRWfv04RvS6W29JnGip9FVTHgVOTxeAKBgNpubGyMEKqvr6c2Dvhb2Z+GhoZRyEkcUp8Igujp6WEwGJcuXWIymQ0NDfX19SKRaIQi2b9/P/UlTq3r16/3NwoQV4TUf7GGf3yjnyGvvPIK+TOTyWSxWNra2mQLi8VS3MtK8nbcuHFSqZTH4+nq6sp1oBasKvwlAgAA+tQwSwC+8kttefLkCULIyMjovzExmfiEH0nxiTJyx1z6fHx8du7cuXbt2urq6t7eXoIgEhMT0d+fXLxixQpjY+P9+/d3dHTs2bNn9erV+vr6eBGXy9XT09PS0sI3YsrBczkNmaen50ifUe/vlgAMn0XGtw96eHg4OzursFpFCE2fPt3MzKysrOzYsWPUszguLi4cDgeffB3UhFbYunXrtLS0Tp8+3efSzZs3M5lMPFkmnb1PE/6/hUoujftDJwY66c3lcoVCYXt7u9zAmoaGBoSQqko6Kyurkc7JHTt29Pd5GQwGm81mMBhz587du3evl5fX/PnzR65aVYRTq79zzPn5+WKx2MfHh5ypDdE4vg1IhVmqSEnecrlcgUDQ3t4uk8moHVpaWkYtPAAA6I8aCtb29nbyoZ0IoVu3bonFYltbW/L0w4QJE6jP6qyvr3/06JHcSrS1tcmidurUqfhBSsppaWmVl5cXFBSIRKKNGzcaGhriP5NtbW1yPblcbkhIyJMnT/bs2XP8+PGwsDDqUj8/v+7ubrmhtV9++aW5uTmdxylpIFynmpiYREREVFRU3L1794svvhjsaU6a3N3dCYLYt28f9ZSVrq6ug4ODWCw+evTooApWLS2tysrKKVOmbN++vbS09LvvvpPrUFVVdejQoYCAgGnTpvX09NDZ+zTJZDLq9VbFNO4TzRhopveyZcsQQmfPniVbOjo6Ll68yOfzh1D3axQ8N8Jrr722c+fOmpqaoqKisLCwPm91HVE4tX777bdDhw7JLXr+/Hl4eLiBgcHevXup7QMe35Sgf4waMuV5i69RUOeea2pqqqqqIl+q9pcIAADoU0PBKhQKP/vss8LCwmfPnpWWlgYFBXE4nKSkJLLDkiVLxGLx/v37ZTLZ/fv3w8LCFE9OzJ49u7q6+vHjx4WFhQ8ePHBycqKzaRaLtWjRovr6+t27dzc1NbW1teXm5uLhtHJCQkL4fH50dPTixYsnT55MXRQXF2dlZfXBBx+cP3++tbW1paXl0KFDMTExCQkJY2uyT3wddvz48f/85z/z8vJqamri4+PJ56aOEFyn9vT0yJ2NxgWWtrY2ndmpFEVHR0dGRq5fvz4yMrK6urqzs7O2tvbIkSMuLi42NjZHjhxBg9n7dOjo6ISGhhYVFfWXxn2iGQPN9I6Li7O0tAwPD8/IyJBKpdXV1e+9915dXV1SUhK+MWDMwTlpZWUVFRVVVVVVVVW1ZcsW6h2ioy86Onrr1q0bNmyIiIgoLy/v6OiQSCQZGRmOjo719fVZWVmvvvoqtf+AxzflVJulipTn7a5du8aNGxceHp6TkyOTye7cuRMUFES9Q2CkwwMAgH5Rr83RnCVA7tzb7t27CwsLqS1RUVFyl4e8vLzwe/EQ7Dt37ri7uwsEAj6f7+zsnJ+fT12/RCJZs2bNhAkT+Hy+o6NjSUmJnZ0dXs+WLVtwn8rKSicnJx0dHTMzs+Tk5D4DU1RRUdHY2Lhu3TozMzM2m21sbBwcHBwZGYmXUof5EwSBH5tEHXVLwpNfvvrqq2w229DQcMmSJeT8TXJfBZ3vExvlWQIEAsGHH3546dKlnp6e4cQj953LjUpWJJFItLS05EYZE38NnQ4ODla+/j73Kdm5uLh45cqVeOcKBIIFCxYkJSV1dHSQHZTv/d27d8ulMR7VRFqxYgXZx9TUtLi42MXFRVdXVy6NlecAnQxUTG/FSHDPpqam8PBwS0tLNpstFArd3d0vXrzYZxhKfiv7M2qzBOB4RCLRp59+Sh1iP5x4yPvOSVKplFwql1fGxsbK11ZSUrJ69WoLCwsOhyMQCObMmRMbGyuRSOS6DXh8G/4xSnG3Uk/oIoTi4uLy8vKoLdu3b6eTt1hVVdXSpUtfeeUVPP1ZRkaGq6srfu+HH35I0EtgJRDMEgAAGBIGQfkbdvLkycDAQGIkb0WaNWtWU1NTTU3NyG1CVb7//vvk5OTS0tLR2RyeUGakR2RfuHDhyJEj7777roeHh5KRLqMWD9Bko5MDycnJ5eXl7777roODA5Op7JqP5ufkGDq+qQuDwUhNTVX76FIAwJgzli5hj7KDBw9GRESoOwoVW7x48eLFi9UdBQD/Z8OGDeoOAQAAgKZTwz2smuzbb79dtmyZTCY7ePDg06dP4TQAAAAAAIDajV7Bip+1fePGjdraWgaDER0dPWqbHpS0tDR9ff0DBw6cOHFibA2iAgCoy1g5vgEAwBg1egXZpk2bNm3aNGqbG5o1a9asWbNG3VEAAMaYMXF8AwCAsQtuCQAAAAAAABoNClYAAAAAAKDRoGAFAAAAAAAaDQpWAAAAAACg0aBgBQAAAAAAGg0KVgAAAAAAoNGgYAUAAAAAABoNClYAAAAAAKDRoGAFAAAAAAAaDQpWAAAAAACg0aBgBQAAAAAAGg0KVgAAAAAAoNGgYAUAAAAAABpNS7Hp5MmTox8HqKmpmThxorqj+JuamhpIhpcZ5CQAAAAN0UfBGhgYOPpxAISQv7+/ukP4m6tXr0IyvOQgJwEAAGgCBkEQ6o4BAAAAAACAfsE9rAAAAAAAQKNBwQoAAAAAADQaFKwAAAAAAECjQcEKAAAAAAA02v8HB52baeNsviMAAAAASUVORK5CYII=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomon_lattice_model = tfl.premade.CalibratedLattice(model_config=model_config)\n",
    "keras.utils.plot_model(\n",
    "    nomon_lattice_model, expand_nested=True, show_layer_names=False, rankdir=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:25:01.112465Z",
     "iopub.status.busy": "2024-12-15T12:25:01.111932Z",
     "iopub.status.idle": "2024-12-15T12:25:25.308949Z",
     "shell.execute_reply": "2024-12-15T12:25:25.308265Z"
    },
    "id": "Oc5f-6zNtyxr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/63 [..............................] - ETA: 11s - loss: 0.1992 - accuracy: 0.9336"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "41/63 [==================>...........] - ETA: 0s - loss: 0.1762 - accuracy: 0.9448 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "63/63 [==============================] - 0s 1ms/step - loss: 0.1727 - accuracy: 0.9460\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/10 [==>...........................] - ETA: 0s - loss: 0.1818 - accuracy: 0.9453"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "10/10 [==============================] - 0s 1ms/step - loss: 0.1877 - accuracy: 0.9390\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/18 [>.............................] - ETA: 0s - loss: 0.2045 - accuracy: 0.9258"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "18/18 [==============================] - 0s 1ms/step - loss: 0.1672 - accuracy: 0.9480\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracies for train: 0.945995, val: 0.939003, test: 0.948020\n"
     ]
    }
   ],
   "source": [
    "nomon_lattice_model.compile(\n",
    "    loss=keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    metrics=[\n",
    "        keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "    ],\n",
    "    optimizer=keras.optimizers.Adam(LEARNING_RATES),\n",
    ")\n",
    "nomon_lattice_model.fit(datasets['law_train'], epochs=NUM_EPOCHS, verbose=0)\n",
    "\n",
    "train_acc = nomon_lattice_model.evaluate(datasets['law_train'])[1]\n",
    "val_acc = nomon_lattice_model.evaluate(datasets['law_val'])[1]\n",
    "test_acc = nomon_lattice_model.evaluate(datasets['law_test'])[1]\n",
    "print(\n",
    "    'accuracies for train: %f, val: %f, test: %f'\n",
    "    % (train_acc, val_acc, test_acc)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:25:25.312084Z",
     "iopub.status.busy": "2024-12-15T12:25:25.311493Z",
     "iopub.status.idle": "2024-12-15T12:25:25.750845Z",
     "shell.execute_reply": "2024-12-15T12:25:25.750192Z"
    },
    "id": "LuFxP9lDTZup"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/13 [=>............................] - ETA: 1s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13/13 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_contour(nomon_lattice_model, from_logits=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eKVkjHg_LaWb"
   },
   "source": [
    "## Train monotonic calibrated lattice model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W42OXWLVwx3w"
   },
   "source": [
    "We can get a monotonic model by setting the monotonicity constraints in feature configs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:25:25.753844Z",
     "iopub.status.busy": "2024-12-15T12:25:25.753650Z",
     "iopub.status.idle": "2024-12-15T12:25:25.757396Z",
     "shell.execute_reply": "2024-12-15T12:25:25.756745Z"
    },
    "id": "XeOKlPRc0BQe"
   },
   "outputs": [],
   "source": [
    "model_config.feature_configs[0].monotonicity = 1\n",
    "model_config.feature_configs[1].monotonicity = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:25:25.760167Z",
     "iopub.status.busy": "2024-12-15T12:25:25.759631Z",
     "iopub.status.idle": "2024-12-15T12:25:56.778701Z",
     "shell.execute_reply": "2024-12-15T12:25:56.778005Z"
    },
    "id": "C_MUEvGNp6g2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/63 [..............................] - ETA: 9s - loss: 0.1958 - accuracy: 0.9336"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/63 [==================>...........] - ETA: 0s - loss: 0.1763 - accuracy: 0.9446"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "63/63 [==============================] - 0s 1ms/step - loss: 0.1712 - accuracy: 0.9463\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/10 [==>...........................] - ETA: 0s - loss: 0.1789 - accuracy: 0.9453"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "10/10 [==============================] - 0s 1ms/step - loss: 0.1869 - accuracy: 0.9403\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/18 [>.............................] - ETA: 0s - loss: 0.2047 - accuracy: 0.9258"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "18/18 [==============================] - 0s 1ms/step - loss: 0.1654 - accuracy: 0.9487\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracies for train: 0.946308, val: 0.940292, test: 0.948684\n"
     ]
    }
   ],
   "source": [
    "mon_lattice_model = tfl.premade.CalibratedLattice(model_config=model_config)\n",
    "\n",
    "mon_lattice_model.compile(\n",
    "    loss=keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    metrics=[\n",
    "        keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "    ],\n",
    "    optimizer=keras.optimizers.Adam(LEARNING_RATES),\n",
    ")\n",
    "mon_lattice_model.fit(datasets['law_train'], epochs=NUM_EPOCHS, verbose=0)\n",
    "\n",
    "train_acc = mon_lattice_model.evaluate(datasets['law_train'])[1]\n",
    "val_acc = mon_lattice_model.evaluate(datasets['law_val'])[1]\n",
    "test_acc = mon_lattice_model.evaluate(datasets['law_test'])[1]\n",
    "print(\n",
    "    'accuracies for train: %f, val: %f, test: %f'\n",
    "    % (train_acc, val_acc, test_acc)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:25:56.781643Z",
     "iopub.status.busy": "2024-12-15T12:25:56.781180Z",
     "iopub.status.idle": "2024-12-15T12:25:57.174465Z",
     "shell.execute_reply": "2024-12-15T12:25:57.173772Z"
    },
    "id": "ABdhYOUVCXzD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/13 [=>............................] - ETA: 1s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13/13 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_contour(mon_lattice_model, from_logits=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GWzBEV_p0WE-"
   },
   "source": [
    "We demonstrated that TFL calibrated lattice models could be trained to be\n",
    "monotonic in both LSAT score and GPA without too big of a sacrifice in accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fsI14lrFxRha"
   },
   "source": [
    "## Train other unconstrained models\n",
    "\n",
    "How does the calibrated lattice model compare to other types of models, like\n",
    "deep neural networks (DNNs) or gradient boosted trees (GBTs)? Do DNNs and GBTs\n",
    "appear to have reasonably fair outputs? To address this question, we will next\n",
    "train an unconstrained DNN and GBT. In fact, we will observe that the DNN and\n",
    "GBT both easily violate monotonicity in LSAT score and undergraduate GPA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uo1ruWXcvUqb"
   },
   "source": [
    "### Train an unconstrained Deep Neural Network (DNN) model\n",
    "\n",
    "The architecture was previously optimized to achieve high validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:25:57.178186Z",
     "iopub.status.busy": "2024-12-15T12:25:57.177584Z",
     "iopub.status.idle": "2024-12-15T12:26:18.794133Z",
     "shell.execute_reply": "2024-12-15T12:26:18.793433Z"
    },
    "id": "3pplraob0Od-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/63 [..............................] - ETA: 6s - loss: 0.1979 - accuracy: 0.9336"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44/63 [===================>..........] - ETA: 0s - loss: 0.1754 - accuracy: 0.9472"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "63/63 [==============================] - 0s 1ms/step - loss: 0.1729 - accuracy: 0.9482\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/10 [==>...........................] - ETA: 0s - loss: 0.1753 - accuracy: 0.9492"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "10/10 [==============================] - 0s 1ms/step - loss: 0.1846 - accuracy: 0.9424\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/18 [>.............................] - ETA: 0s - loss: 0.2040 - accuracy: 0.9336"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "18/18 [==============================] - 0s 1ms/step - loss: 0.1658 - accuracy: 0.9505\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracies for train: 0.948248, val: 0.942440, test: 0.950453\n"
     ]
    }
   ],
   "source": [
    "keras.utils.set_random_seed(42)\n",
    "inputs = [\n",
    "    keras.Input(shape=(1,), dtype=tf.float32),\n",
    "    keras.Input(shape=(1), dtype=tf.float32),\n",
    "]\n",
    "inputs_flat = keras.layers.Concatenate()(inputs)\n",
    "dense_layers = keras.Sequential(\n",
    "    [\n",
    "        keras.layers.Dense(64, activation='relu'),\n",
    "        keras.layers.Dense(32, activation='relu'),\n",
    "        keras.layers.Dense(1, activation=None),\n",
    "    ],\n",
    "    name='dense_layers',\n",
    ")\n",
    "dnn_model = keras.Model(inputs=inputs, outputs=dense_layers(inputs_flat))\n",
    "dnn_model.compile(\n",
    "    loss=keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    metrics=[keras.metrics.BinaryAccuracy(name='accuracy')],\n",
    "    optimizer=keras.optimizers.Adam(LEARNING_RATES),\n",
    ")\n",
    "dnn_model.fit(datasets['law_train'], epochs=NUM_EPOCHS, verbose=0)\n",
    "\n",
    "train_acc = dnn_model.evaluate(datasets['law_train'])[1]\n",
    "val_acc = dnn_model.evaluate(datasets['law_val'])[1]\n",
    "test_acc = dnn_model.evaluate(datasets['law_test'])[1]\n",
    "print(\n",
    "    'accuracies for train: %f, val: %f, test: %f'\n",
    "    % (train_acc, val_acc, test_acc)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:18.796987Z",
     "iopub.status.busy": "2024-12-15T12:26:18.796541Z",
     "iopub.status.idle": "2024-12-15T12:26:19.160272Z",
     "shell.execute_reply": "2024-12-15T12:26:19.159613Z"
    },
    "id": "LwPQqLt-E7R4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/13 [=>............................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13/13 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_contour(dnn_model, from_logits=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OOAKK0_3vWir"
   },
   "source": [
    "### Train an unconstrained Gradient Boosted Trees (GBT) model\n",
    "\n",
    "The tree structure was previously optimized to achieve high validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:19.163628Z",
     "iopub.status.busy": "2024-12-15T12:26:19.163185Z",
     "iopub.status.idle": "2024-12-15T12:26:24.813342Z",
     "shell.execute_reply": "2024-12-15T12:26:24.812666Z"
    },
    "id": "6UrCJHqhgd3o"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-15 12:26:19.178391: W external/ydf/yggdrasil_decision_forests/learner/gradient_boosted_trees/gradient_boosted_trees.cc:1840] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n",
      "2024-12-15 12:26:19.178430: W external/ydf/yggdrasil_decision_forests/learner/gradient_boosted_trees/gradient_boosted_trees.cc:1850] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n",
      "2024-12-15 12:26:19.178438: W external/ydf/yggdrasil_decision_forests/learner/gradient_boosted_trees/gradient_boosted_trees.cc:1864] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num validation examples: tf.Tensor(2328, shape=(), dtype=int32)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1734265583.475952   73126 kernel.cc:782] Start Yggdrasil model training\n",
      "I0000 00:00:1734265583.475995   73126 kernel.cc:783] Collect training examples\n",
      "I0000 00:00:1734265583.476008   73126 kernel.cc:795] Dataspec guide:\n",
      "column_guides {\n",
      "  column_name_pattern: \"^__LABEL$\"\n",
      "  type: CATEGORICAL\n",
      "  categorial {\n",
      "    min_vocab_frequency: 0\n",
      "    max_vocab_count: -1\n",
      "  }\n",
      "}\n",
      "default_column_guide {\n",
      "  categorial {\n",
      "    max_vocab_count: 2000\n",
      "  }\n",
      "  discretized_numerical {\n",
      "    maximum_num_bins: 255\n",
      "  }\n",
      "}\n",
      "ignore_columns_without_guides: false\n",
      "detect_numerical_as_discretized_numerical: false\n",
      "\n",
      "I0000 00:00:1734265583.476352   73126 kernel.cc:401] Number of batches: 63\n",
      "I0000 00:00:1734265583.476369   73126 kernel.cc:402] Number of examples: 15980\n",
      "I0000 00:00:1734265583.476750   73126 kernel.cc:802] Training dataset:\n",
      "Number of records: 15980\n",
      "Number of columns: 3\n",
      "\n",
      "Number of columns by type:\n",
      "\tNUMERICAL: 2 (66.6667%)\n",
      "\tCATEGORICAL: 1 (33.3333%)\n",
      "\n",
      "Columns:\n",
      "\n",
      "NUMERICAL: 2 (66.6667%)\n",
      "\t0: \"0\" NUMERICAL mean:3.22705 min:1.6 max:4 sd:0.415473\n",
      "\t1: \"1\" NUMERICAL mean:36.8057 min:11 max:48 sd:5.46358\n",
      "\n",
      "CATEGORICAL: 1 (33.3333%)\n",
      "\t2: \"__LABEL\" CATEGORICAL integerized vocab-size:3 no-ood-item\n",
      "\n",
      "Terminology:\n",
      "\tnas: Number of non-available (i.e. missing) values.\n",
      "\tood: Out of dictionary.\n",
      "\tmanually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.\n",
      "\ttokenized: The attribute value is obtained through tokenization.\n",
      "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n",
      "\tvocab-size: Number of unique values.\n",
      "\n",
      "I0000 00:00:1734265583.476770   73126 kernel.cc:807] Collect validation dataset\n",
      "I0000 00:00:1734265583.476785   73126 kernel.cc:401] Number of batches: 10\n",
      "I0000 00:00:1734265583.476788   73126 kernel.cc:402] Number of examples: 2328\n",
      "I0000 00:00:1734265583.476856   73126 kernel.cc:813] Validation dataset:\n",
      "Number of records: 2328\n",
      "Number of columns: 3\n",
      "\n",
      "Number of columns by type:\n",
      "\tNUMERICAL: 2 (66.6667%)\n",
      "\tCATEGORICAL: 1 (33.3333%)\n",
      "\n",
      "Columns:\n",
      "\n",
      "NUMERICAL: 2 (66.6667%)\n",
      "\t0: \"0\" NUMERICAL mean:3.23239 min:1.8 max:4 sd:0.403059\n",
      "\t1: \"1\" NUMERICAL mean:36.8697 min:17 max:48 sd:5.51876\n",
      "\n",
      "CATEGORICAL: 1 (33.3333%)\n",
      "\t2: \"__LABEL\" CATEGORICAL integerized vocab-size:3 no-ood-item\n",
      "\n",
      "Terminology:\n",
      "\tnas: Number of non-available (i.e. missing) values.\n",
      "\tood: Out of dictionary.\n",
      "\tmanually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.\n",
      "\ttokenized: The attribute value is obtained through tokenization.\n",
      "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n",
      "\tvocab-size: Number of unique values.\n",
      "\n",
      "I0000 00:00:1734265583.476869   73126 kernel.cc:818] Configure learner\n",
      "2024-12-15 12:26:23.477070: W external/ydf/yggdrasil_decision_forests/learner/gradient_boosted_trees/gradient_boosted_trees.cc:1840] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n",
      "2024-12-15 12:26:23.477099: W external/ydf/yggdrasil_decision_forests/learner/gradient_boosted_trees/gradient_boosted_trees.cc:1850] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n",
      "2024-12-15 12:26:23.477106: W external/ydf/yggdrasil_decision_forests/learner/gradient_boosted_trees/gradient_boosted_trees.cc:1864] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n",
      "I0000 00:00:1734265583.477151   73126 kernel.cc:831] Training config:\n",
      "learner: \"GRADIENT_BOOSTED_TREES\"\n",
      "features: \"^0$\"\n",
      "features: \"^1$\"\n",
      "label: \"^__LABEL$\"\n",
      "task: CLASSIFICATION\n",
      "random_seed: 42\n",
      "metadata {\n",
      "  framework: \"TF Keras\"\n",
      "}\n",
      "pure_serving_model: false\n",
      "[yggdrasil_decision_forests.model.gradient_boosted_trees.proto.gradient_boosted_trees_config] {\n",
      "  num_trees: 20\n",
      "  decision_tree {\n",
      "    max_depth: 4\n",
      "    min_examples: 5\n",
      "    in_split_min_examples_check: true\n",
      "    keep_non_leaf_label_distribution: true\n",
      "    num_candidate_attributes: -1\n",
      "    missing_value_policy: GLOBAL_IMPUTATION\n",
      "    allow_na_conditions: false\n",
      "    categorical_set_greedy_forward {\n",
      "      sampling: 0.1\n",
      "      max_num_items: -1\n",
      "      min_item_frequency: 1\n",
      "    }\n",
      "    growing_strategy_best_first_global {\n",
      "    }\n",
      "    categorical {\n",
      "      cart {\n",
      "      }\n",
      "    }\n",
      "    axis_aligned_split {\n",
      "    }\n",
      "    internal {\n",
      "      sorting_strategy: PRESORTED\n",
      "    }\n",
      "    uplift {\n",
      "      min_examples_in_treatment: 5\n",
      "      split_score: KULLBACK_LEIBLER\n",
      "    }\n",
      "  }\n",
      "  shrinkage: 0.1\n",
      "  loss: DEFAULT\n",
      "  validation_set_ratio: 0.1\n",
      "  validation_interval_in_trees: 1\n",
      "  early_stopping: VALIDATION_LOSS_INCREASE\n",
      "  early_stopping_num_trees_look_ahead: 30\n",
      "  l2_regularization: 0\n",
      "  lambda_loss: 1\n",
      "  mart {\n",
      "  }\n",
      "  adapt_subsample_for_maximum_training_duration: false\n",
      "  l1_regularization: 0\n",
      "  use_hessian_gain: false\n",
      "  l2_regularization_categorical: 1\n",
      "  xe_ndcg {\n",
      "    ndcg_truncation: 5\n",
      "  }\n",
      "  stochastic_gradient_boosting {\n",
      "    ratio: 1\n",
      "  }\n",
      "  apply_link_function: true\n",
      "  compute_permutation_variable_importance: false\n",
      "  early_stopping_initial_iteration: 10\n",
      "}\n",
      "\n",
      "I0000 00:00:1734265583.477477   73126 kernel.cc:834] Deployment config:\n",
      "cache_path: \"/tmpfs/tmp/tmpvvq5aqsg/working_cache\"\n",
      "num_threads: 1\n",
      "try_resume_training: true\n",
      "\n",
      "I0000 00:00:1734265583.477592  100425 kernel.cc:895] Train model\n",
      "I0000 00:00:1734265583.549481  100425 kernel.cc:926] Export model in log directory: /tmpfs/tmp/tmpvvq5aqsg with prefix 17de4d5178394a23\n",
      "I0000 00:00:1734265583.550034  100425 kernel.cc:944] Save model in resources\n",
      "I0000 00:00:1734265583.551071   73126 abstract_model.cc:914] Model self evaluation:\n",
      "Task: CLASSIFICATION\n",
      "Label: __LABEL\n",
      "Loss (BINOMIAL_LOG_LIKELIHOOD): 0.372453\n",
      "\n",
      "Accuracy: 0.948024  CI95[W][0 1]\n",
      "ErrorRate: : 0.051976\n",
      "\n",
      "\n",
      "Confusion Table:\n",
      "truth\\prediction\n",
      "   1     2\n",
      "1  4   121\n",
      "2  0  2203\n",
      "Total: 2328\n",
      "\n",
      "\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1734265583.561323   73126 quick_scorer_extended.cc:922] The binary was compiled without AVX2 support, but your CPU supports it. Enable it for faster model inference.\n",
      "I0000 00:00:1734265583.561425   73126 abstract_model.cc:1404] Engine \"GradientBoostedTreesQuickScorerExtended\" built\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracies for GBT: train: 0.949625, val: 0.948024, test: 0.951559\n"
     ]
    }
   ],
   "source": [
    "tree_model = tfdf.keras.GradientBoostedTreesModel(\n",
    "    exclude_non_specified_features=False,\n",
    "    num_threads=1,\n",
    "    num_trees=20,\n",
    "    max_depth=4,\n",
    "    growing_strategy='BEST_FIRST_GLOBAL',\n",
    "    random_seed=42,\n",
    "    temp_directory=tempfile.mkdtemp(),\n",
    ")\n",
    "tree_model.compile(metrics=[keras.metrics.BinaryAccuracy(name='accuracy')])\n",
    "tree_model.fit(\n",
    "    datasets['law_train'], validation_data=datasets['law_val'], verbose=0\n",
    ")\n",
    "\n",
    "tree_train_acc = tree_model.evaluate(datasets['law_train'], verbose=0)[1]\n",
    "tree_val_acc = tree_model.evaluate(datasets['law_val'], verbose=0)[1]\n",
    "tree_test_acc = tree_model.evaluate(datasets['law_test'], verbose=0)[1]\n",
    "print(\n",
    "    'accuracies for GBT: train: %f, val: %f, test: %f'\n",
    "    % (tree_train_acc, tree_val_acc, tree_test_acc)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:24.816507Z",
     "iopub.status.busy": "2024-12-15T12:26:24.815957Z",
     "iopub.status.idle": "2024-12-15T12:26:25.162447Z",
     "shell.execute_reply": "2024-12-15T12:26:25.161760Z"
    },
    "id": "AZFyfQT1E_nR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/13 [=>............................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13/13 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_contour(tree_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uX2qiMlrY8aO"
   },
   "source": [
    "# Case study #2: Credit Default\n",
    "\n",
    "The second case study that we will consider in this tutorial is predicting an\n",
    "individual's credit default probability. We will use the Default of Credit Card\n",
    "Clients dataset from the UCI repository. This data was collected from 30,000\n",
    "Taiwanese credit card users and contains a binary label of whether or not a user\n",
    "defaulted on a payment in a time window. Features include marital status,\n",
    "gender, education, and how long a user is behind on payment of their existing\n",
    "bills, for each of the months of April-September 2005.\n",
    "\n",
    "As we did with the first case study, we again illustrate using monotonicity\n",
    "constraints to avoid *unfair penalization*: if the model were to be used to\n",
    "determine a user’s credit score, it could feel unfair to many if they were\n",
    "penalized for paying their bills sooner, all else equal. Thus, we apply a\n",
    "monotonicity constraint that keeps the model from penalizing early payments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tz5yduNuFinA"
   },
   "source": [
    "## Load Credit Default data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.165971Z",
     "iopub.status.busy": "2024-12-15T12:26:25.165485Z",
     "iopub.status.idle": "2024-12-15T12:26:25.710294Z",
     "shell.execute_reply": "2024-12-15T12:26:25.709513Z"
    },
    "id": "KuylMNBCILwy"
   },
   "outputs": [],
   "source": [
    "# Load data file.\n",
    "credit_file_name = 'credit_default.csv'\n",
    "credit_file_path = os.path.join(DATA_DIR, credit_file_name)\n",
    "credit_df = pd.read_csv(credit_file_path, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.713808Z",
     "iopub.status.busy": "2024-12-15T12:26:25.713345Z",
     "iopub.status.idle": "2024-12-15T12:26:25.716635Z",
     "shell.execute_reply": "2024-12-15T12:26:25.716025Z"
    },
    "id": "Hv_GQcEHIf9v"
   },
   "outputs": [],
   "source": [
    "# Define label column name.\n",
    "CREDIT_LABEL = 'default'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "13oZWY0YIoy3"
   },
   "source": [
    "### Split data into train/validation/test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.719557Z",
     "iopub.status.busy": "2024-12-15T12:26:25.719036Z",
     "iopub.status.idle": "2024-12-15T12:26:25.754873Z",
     "shell.execute_reply": "2024-12-15T12:26:25.754253Z"
    },
    "id": "dty5tXJqIscz"
   },
   "outputs": [],
   "source": [
    "dfs = {}\n",
    "datasets = {}\n",
    "\n",
    "dfs[\"credit_train\"], dfs[\"credit_val\"], dfs[\"credit_test\"] = split_dataset(\n",
    "    credit_df\n",
    ")\n",
    "\n",
    "for df_name, df in dfs.items():\n",
    "  datasets[df_name] = tf.data.Dataset.from_tensor_slices(\n",
    "      ((df[['MARRIAGE']], df[['PAY_0']]), df[['default']])\n",
    "  ).batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_kAciWXHKGV7"
   },
   "source": [
    "### Visualize data distribution\n",
    "\n",
    "First we will visualize the distribution of the data. We will plot the mean and\n",
    "standard error of the observed default rate for people with different marital\n",
    "statuses and repayment statuses. The repayment status represents the number of\n",
    "months a person is behind on paying back their loan (as of April 2005)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.757868Z",
     "iopub.status.busy": "2024-12-15T12:26:25.757333Z",
     "iopub.status.idle": "2024-12-15T12:26:25.767471Z",
     "shell.execute_reply": "2024-12-15T12:26:25.766871Z"
    },
    "id": "8CxacQxnkHWE"
   },
   "outputs": [],
   "source": [
    "def get_agg_data(df, x_col, y_col, bins=11):\n",
    "  xbins = pd.cut(df[x_col], bins=bins)\n",
    "  data = df[[x_col, y_col]].groupby(xbins).agg(['mean', 'sem'])\n",
    "  return data\n",
    "\n",
    "\n",
    "def plot_2d_means_credit(input_df, x_col, y_col, x_label, y_label):\n",
    "  plt.rcParams['font.family'] = ['serif']\n",
    "  _, ax = plt.subplots(nrows=1, ncols=1)\n",
    "  plt.setp(ax.spines.values(), color='black', linewidth=1)\n",
    "  ax.tick_params(\n",
    "      direction='in', length=6, width=1, top=False, right=False, labelsize=18)\n",
    "  df_single = get_agg_data(input_df[input_df['MARRIAGE'] == 1], x_col, y_col)\n",
    "  df_married = get_agg_data(input_df[input_df['MARRIAGE'] == 2], x_col, y_col)\n",
    "  ax.errorbar(\n",
    "      df_single[(x_col, 'mean')],\n",
    "      df_single[(y_col, 'mean')],\n",
    "      xerr=df_single[(x_col, 'sem')],\n",
    "      yerr=df_single[(y_col, 'sem')],\n",
    "      color='orange',\n",
    "      marker='s',\n",
    "      capsize=3,\n",
    "      capthick=1,\n",
    "      label='Single',\n",
    "      markersize=10,\n",
    "      linestyle='')\n",
    "  ax.errorbar(\n",
    "      df_married[(x_col, 'mean')],\n",
    "      df_married[(y_col, 'mean')],\n",
    "      xerr=df_married[(x_col, 'sem')],\n",
    "      yerr=df_married[(y_col, 'sem')],\n",
    "      color='b',\n",
    "      marker='^',\n",
    "      capsize=3,\n",
    "      capthick=1,\n",
    "      label='Married',\n",
    "      markersize=10,\n",
    "      linestyle='')\n",
    "  leg = ax.legend(loc='upper left', fontsize=18, frameon=True, numpoints=1)\n",
    "  ax.set_xlabel(x_label, fontsize=18)\n",
    "  ax.set_ylabel(y_label, fontsize=18)\n",
    "  ax.set_ylim(0, 1.1)\n",
    "  ax.set_xlim(-2, 8.5)\n",
    "  ax.patch.set_facecolor('white')\n",
    "  leg.get_frame().set_edgecolor('black')\n",
    "  leg.get_frame().set_facecolor('white')\n",
    "  leg.get_frame().set_linewidth(1)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.770139Z",
     "iopub.status.busy": "2024-12-15T12:26:25.769685Z",
     "iopub.status.idle": "2024-12-15T12:26:25.931371Z",
     "shell.execute_reply": "2024-12-15T12:26:25.930719Z"
    },
    "id": "VHXyYbyekKLT"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmpfs/tmp/ipykernel_73126/4037607942.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data = df[[x_col, y_col]].groupby(xbins).agg(['mean', 'sem'])\n",
      "/tmpfs/tmp/ipykernel_73126/4037607942.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data = df[[x_col, y_col]].groupby(xbins).agg(['mean', 'sem'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_2d_means_credit(\n",
    "    dfs['credit_train'],\n",
    "    'PAY_0',\n",
    "    'default',\n",
    "    'Repayment Status (April)',\n",
    "    'Observed default rate',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4hnZBigB7kzY"
   },
   "source": [
    "## Train calibrated lattice model to predict credit default rate\n",
    "\n",
    "Next, we will train a *calibrated lattice model* from TFL to predict whether or\n",
    "not a person will default on a loan. The two input features will be the person's\n",
    "marital status and how many months the person is behind on paying back their\n",
    "loans in April (repayment status). The training label will be whether or not the\n",
    "person defaulted on a loan.\n",
    "\n",
    "We will first train a calibrated lattice model without any constraints. Then, we\n",
    "will train a calibrated lattice model with monotonicity constraints and observe\n",
    "the difference in the model output and accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iwxnlRrQPdTg"
   },
   "source": [
    "### Helper functions for visualization of trained model outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.934475Z",
     "iopub.status.busy": "2024-12-15T12:26:25.934010Z",
     "iopub.status.idle": "2024-12-15T12:26:25.938648Z",
     "shell.execute_reply": "2024-12-15T12:26:25.937996Z"
    },
    "id": "zVGxEfbhPZ5H"
   },
   "outputs": [],
   "source": [
    "def plot_predictions_credit(\n",
    "    input_df,\n",
    "    model,\n",
    "    x_col,\n",
    "    x_label='Repayment Status (April)',\n",
    "    y_label='Predicted default probability',\n",
    "):\n",
    "  predictions = model.predict((input_df[['MARRIAGE']], input_df[['PAY_0']]))\n",
    "  predictions = tf.math.sigmoid(predictions)\n",
    "  new_df = input_df.copy()\n",
    "  new_df.loc[:, 'predictions'] = predictions\n",
    "  plot_2d_means_credit(new_df, x_col, 'predictions', x_label, y_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UMIpywE1P07H"
   },
   "source": [
    "## Train unconstrained (non-monotonic) calibrated lattice model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.941604Z",
     "iopub.status.busy": "2024-12-15T12:26:25.941092Z",
     "iopub.status.idle": "2024-12-15T12:26:25.945920Z",
     "shell.execute_reply": "2024-12-15T12:26:25.945306Z"
    },
    "id": "cxGu3gBOApOm"
   },
   "outputs": [],
   "source": [
    "model_config = tfl.configs.CalibratedLatticeConfig(\n",
    "    feature_configs=[\n",
    "        tfl.configs.FeatureConfig(\n",
    "            name='MARRIAGE',\n",
    "            lattice_size=3,\n",
    "            pwl_calibration_num_keypoints=2,\n",
    "            monotonicity=0,\n",
    "            pwl_calibration_always_monotonic=False,\n",
    "        ),\n",
    "        tfl.configs.FeatureConfig(\n",
    "            name='PAY_0',\n",
    "            lattice_size=3,\n",
    "            pwl_calibration_num_keypoints=16,\n",
    "            monotonicity=0,\n",
    "            pwl_calibration_always_monotonic=False,\n",
    "        ),\n",
    "    ],\n",
    "    output_calibration=True,\n",
    "    output_initialization=np.linspace(-2, 2, num=8),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.948571Z",
     "iopub.status.busy": "2024-12-15T12:26:25.948086Z",
     "iopub.status.idle": "2024-12-15T12:26:25.954809Z",
     "shell.execute_reply": "2024-12-15T12:26:25.954193Z"
    },
    "id": "cVZKH36LA8BQ"
   },
   "outputs": [],
   "source": [
    "feature_keypoints = tfl.premade_lib.compute_feature_keypoints(\n",
    "    feature_configs=model_config.feature_configs,\n",
    "    features=dfs[\"credit_train\"][['MARRIAGE', 'PAY_0', 'default']],\n",
    ")\n",
    "tfl.premade_lib.set_feature_keypoints(\n",
    "    feature_configs=model_config.feature_configs,\n",
    "    feature_keypoints=feature_keypoints,\n",
    "    add_missing_feature_configs=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:25.957388Z",
     "iopub.status.busy": "2024-12-15T12:26:25.957009Z",
     "iopub.status.idle": "2024-12-15T12:26:56.800208Z",
     "shell.execute_reply": "2024-12-15T12:26:56.799525Z"
    },
    "id": "2It6hvNRA8Bi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/83 [..............................] - ETA: 12s - loss: 0.5323 - accuracy: 0.7734"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42/83 [==============>...............] - ETA: 0s - loss: 0.4548 - accuracy: 0.8185 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83/83 [==============================] - ETA: 0s - loss: 0.4537 - accuracy: 0.8186"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83/83 [==============================] - 0s 1ms/step - loss: 0.4537 - accuracy: 0.8186\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/12 [=>............................] - ETA: 0s - loss: 0.4454 - accuracy: 0.8281"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "12/12 [==============================] - 0s 1ms/step - loss: 0.4423 - accuracy: 0.8291\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4272 - accuracy: 0.8359"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4547 - accuracy: 0.8168\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracies for train: 0.818619, val: 0.829085, test: 0.816835\n"
     ]
    }
   ],
   "source": [
    "nomon_lattice_model = tfl.premade.CalibratedLattice(model_config=model_config)\n",
    "\n",
    "nomon_lattice_model.compile(\n",
    "    loss=keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    metrics=[\n",
    "        keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "    ],\n",
    "    optimizer=keras.optimizers.Adam(LEARNING_RATES),\n",
    ")\n",
    "nomon_lattice_model.fit(datasets['credit_train'], epochs=NUM_EPOCHS, verbose=0)\n",
    "\n",
    "train_acc = nomon_lattice_model.evaluate(datasets['credit_train'])[1]\n",
    "val_acc = nomon_lattice_model.evaluate(datasets['credit_val'])[1]\n",
    "test_acc = nomon_lattice_model.evaluate(datasets['credit_test'])[1]\n",
    "print(\n",
    "    'accuracies for train: %f, val: %f, test: %f'\n",
    "    % (train_acc, val_acc, test_acc)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:56.803171Z",
     "iopub.status.busy": "2024-12-15T12:26:56.802590Z",
     "iopub.status.idle": "2024-12-15T12:26:58.027925Z",
     "shell.execute_reply": "2024-12-15T12:26:58.026904Z"
    },
    "id": "5zQ_jm75kRX6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/657 [..............................] - ETA: 1:07"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/657 [=>............................] - ETA: 0s  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 94/657 [===>..........................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "347/657 [==============>...............] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "602/657 [==========================>...] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "653/657 [============================>.] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "657/657 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmpfs/tmp/ipykernel_73126/4037607942.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data = df[[x_col, y_col]].groupby(xbins).agg(['mean', 'sem'])\n",
      "/tmpfs/tmp/ipykernel_73126/4037607942.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data = df[[x_col, y_col]].groupby(xbins).agg(['mean', 'sem'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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dunUBIMs6Yn///Tf69euH69evSxqHiOhjp9XpOU1X/Ggruy8yffr8889ha2uLly9f4ty5c2qvPnry5Ily8WzGS9qNjoUjYGIp7aa9Jpbp+xuwt2/fYsyYMZg7d67G9gkTJgAAWrZsiXLlyuVbbIGBgfjyyy+xY8cOXLx4MVPZAUXJAalFYGfMmAEfHx+cOHECW7ZsyVSo9cGDB5g8eTLc3NyyvFiBiIg00yppOnz4sM4D5XYl79xiYWGBb7/9FvPnz8eWLVswcuTITH2Cg4MBAMWLF0ebNm3yO8TcY1MaaHsNeB+nuj317X/lA5odV39lnIVj+v4GSrGIet68ebh48SJ69OiBSpUqwdraGvHx8QgPD8fixYtx48YN1K5dG6tXr1bue/36dSQlJSmT+ydPniAqKgpFixZFyZIlcfv2bSQmJmZqd3JygpOTE+7fv48XL14oK3rHx8er7A8Afn5+GDlyJGbMmIFmzZohMDAQn332GeLj4xEUFIS4uDjUq1dPbY2n7OID0pP/NWvWoGfPnvjmm28wfPhwtG3bFqampggPD8eUKVOQnJyMXbt2ZXmVKBERaSYTWvxpa2JigkGDBml9tdyHHj9+jCVLligvedbVypUrlZdV3759O8vLuIH0K4pGjRoFLy8v7N69G3Z2dirtT58+haenJ+Li4rB9+3a0a9dO2ZaQkIAqVargzp07WLlyJfz9/dWOcf78eXh7eyM8PFzrheBXr15F9+7dsWbNGnz66ada7ZMnUhKBjen3bsOXr1XLFBiJ1NRUnDx5EgcPHsTZs2dx48YNPHr0CG/evIGlpSWcnZ1RrVo1dOnSBV999RVMTP47M+3u7q729LG/vz9WrlyJRo0a4ciRI5naFSUkevbsqfZmzor9MwoODsacOXNw/vx5pKWloWzZsujatStGjRqFJk2a4NixY5g0aRLGjh2rdXwZ3bp1C3/++SdCQkKUNcXKlCmDVq1aYeTIkXB2ds7yfcxvBvMzQEQfrZx8f2t99dzgwYO1qsmkTlRUFJYsWSJpX4UnT57gyZMnAFTXpVy/fh2vX78GkP7loO7qt3nz5iEhIQEnT57EoUOH0KlTJ5X2YsWKYefOnWjVqhW6du2KCRMmwMfHB7GxsZgwYQLu3LmDMWPGaEyYSP/kcjkaNGigvM1PTmS1lg3IfqZ15cqVmZIXTTp16pTp86egmKn68I+T7OLLqGzZspg/f77W/YmISHtaLQT39/dH0aJFJQ9ib2+PHj16SN4fABYuXIjKlSujcuXKGDdunHJ7ixYtlNvDwsLU7jtkyBAUKVIEdevW1bg+67PPPkNUVBT69OmDRYsWoUGDBhgwYABKlSqFffv2YfLkyTrFTx+3+Ph4jBs3Ds+ePVPb/urVK1y7dg0AUL++YVVaJyKidFrNNAUFBek0SIkSJXQ+RmBgoORq2gMGDMCAAQOy7VeiRAnMnTtX40LiAuftw/RH6tv/tr24+N+aJisXgykpYOzi4+MxadIk2NjYYMyYMZna//zzTyQlJaFVq1Y8TUVEZKAkF7fMicuXL6NKlSq5tqaJcsmNJUDUBNVtGe8n5xWQXouJcs348ePx+PFjtGnTBo6Ojnj8+DE2bNiAoKAgVK5cWec/LoiIKO/kS9IEQPKl1JSHKgwASrXT3M5Zplzj6uqKPXv2YMeOHTh8+DA2bNiAZ8+ewdraGpUqVcKff/6JgQMHZnmbFSIi0i+tkqaNGzdi7dq1GDlypHKhbU4qfCcnJxtsyYGPGk+/5Ru5XI6WLVuiZcuW+g6FiIgk0ipp6t+/P169eoUnT57g1KlTAHJ2RQ9guHWaiIiIiLShVdLk7++PoKCgTJWEJ02ahBIlSmS7f2xsLH799VdpERIREREZAK2Spjlz5mDOnDmZtrdv316r2k2XL1/G+PHjcx4dERERkYGQfMPegIAArSuEOzk5ISAgQOpQRERERHon+eq5nCRBxYoVY9JERERERi3XSg7cvn0bV65cQUJCAooUKYJKlSple084IiIiImOhc9IUHByMwMBAXL58OVObl5cXAgMD0bFjR12HoTzw8GH6QxMXl/QHERER6bCmCQB+/vln+Pn5ISoqCkKITI/IyEh06dIFo0ePzq14KRctWQJ4e2t+6HiPZSIiogJF8kzTtm3bMH36dJiYmKBdu3Zo0qQJ3N3dYW1tjTdv3iAmJgYHDhzArl27MH36dNStWxft27fPzdhJRwMGAO3aAW/fAop7xB4/DiiKUnOWiYiI6D+Sk6Y5c+bA0dERe/fuhbe3t9o+Q4YMwblz59CyZUvMmTOHSZOBUZx+S0z8b1u1aoCNjd5CIiIiMliST89duHABEyZM0JgwKdSsWROBgYEIDw+XOhQRERGR3klOmlJTU1GnTh2t+tatWxepqalShyLKUqNGjSCTyVQeNWrUyNEx3r17hxIlSmQ6zsqVK/Mm6Fw2fPhw2NraYv369fk2pru7u8p7FRgYmG9jExHpg+SkqVy5coiPj9eqb3x8PEqXLi11KKIsBQUFITIyEitWrFBuu3DhAnbs2KH1Mf766y88/P9LCUuUKIHIyEhERkaiQ4cOuR1unlixYgUSEhKwdu3afBszJCQEkZGRqFmzZr6NSUSkT5KTpm+++Ubrv8JXrlyJtm3bSh2K8lFoqL4jyLkyZcrAy8sLZcqUAQCYmqYv1Zs4caJW+79//x5//PEHzMzMAABmZmbw8vKCl5cX7Ozs8iTm3DZ58mTUrl0bI0eOzLcxK1asCC8vL9hwERwRfSQkJ00jRoxAXFwchgwZgkePHqnt8+jRIwwePBgXLlzAuHHjJAdJeUuI/54HBKj+vzFS3Fg6PDwcu3btyrb/smXLkJiYiDZt2uR1aHlm6NChOHPmDHx8fPQdChFRgaXV1XO+vr5qt6elpWHJkiVYvHgxypYtC2dnZ5iamiIlJQWPHz/Gv//+CyEEqlWrhg4dOuDgwYO5Gjzljoz/LOfPAyEhQIsW+otHV926dcORI0dw+/ZtTJw4MctkKCkpCdOmTcOwYcNw586dfIySiIiMjVYzTYcPH1b7OHr0KFJTU5GWloabN2/i+PHjOHz4MI4fP44bN24gLS0NQghcuHABhw8fzuOXQgoPHwKBgVlX+1YQAsh4FksuB8aPz362KSdj5DdTU1OMGTMGABAWFoY9e/Zo7LtixQrEx8dj+PDhWR5TCIFDhw5h6NChqFGjBooUKQIzMzMUL14crVu3xrZt29TuFxgYqLJYWnFroVWrVqF+/fooWrSosq1nz5651l+dFy9eICAgAFWrVkWhQoVgbW2N8uXLo3fv3oiIiMjy9W/duhWNGjWCra0tChUqhKpVq+KPP/5ASkpKlvsRERUkWtdpGjRoEJycnCQN8vjxYyxheel88/AhMGFCeuHK7ApUhoSkzy4ppKYCYWHZzzblZAx98Pf3x2+//YZ79+5hwoQJaNWqVaY+ycnJmDp1Kr777jvY29tnebw7d+6gSZMmMDU1xffff48ZM2bA2toa0dHRmDFjBjp27IjvvvsOCxYsUNnvu+++Q5cuXbB9+3blKephw4bh1q1bGDt2LIoVK4bg4GBMnTo11/qrExERgVatWuHRo0cYPnw4ZsyYAXNzc5w6dQqTJ0/G6tWrMXfuXHz33XeZ9h01ahRmzJgBR0dHTJ48GXXq1EF8fDyCgoLQtm1bpKWlZfneEREVGEILMplMXL58WZuuakVGRgoTExPJ+xuD8PBwAUCEh4drvU90dLTw9vYW0dHRuRyLEIAQx48L8fq15serV0LUqCGEXJ7eX/GQy9O3v3qled/jx9P75uDl5rnQ0FABQISGhgohhJg3b54AIACIvXv3Zur/119/CWtra/HkyRMhhBD+/v4CgHBzc8vU9/bt2wKAmDlzZqa2V69eiQoVKggAYseOHWpjCwoKEgCEXC4Xbdu2FWlpaSrtFSpUEP7+/jr3Vxd7XFycKFmypAAg/vrrr0ztZ8+eFTKZTJiYmIjjx4+rtG3cuFEZx4ULFzLt26dPH2FiYiIAiICAALWvPSt59TNARKStnHx/a3V6rn379ihSpIjkxMzW1hbt2rWTvD9JU78+UKiQ5kfhwumzTB+W0EpNTd9euLDmfRW3XTFkffv2hbOzMwBgwoQJKm0pKSmYMmUKBgwYgGLFimV7LDs7OwQEBKBPnz6Z2goVKoRu3boBAP7+++8sj5Oamopff/0VMplMZfvRo0cxY8YMnfurM2PGDNy/fx/ly5dH3759M7XXqlULTZs2RVpaGqZNm6bSpqi91L59e1SrVi3TvoGBgRDGfuUAEZGWtEqatm7dilKlSkkexNXVFVu3bpW8P5EUlpaWykvwT58+jZCQEGXb6tWr8eDBA4waNUqrY9nZ2SEwMBC2trZq293c3AAA0dHRWR7HyspKbeFNZ2dnODo66txfnY0bNwIAGjZsmCn5Uvj0008BpK9fVJxuu3nzJq5cuQJA88UgpUqVUpZ6ICIq6CSXHMiJGzduoGzZsvkxFGVw/Djw+rX6h4Z1y5ls26Z+/+PH8zLy3DNw4EBlcqGYbUpNTcXkyZPRp08fuORgQdaTJ08wbtw41K5dG/b29jA3N4epqSlMTU2VM1CvX7/O8hgODg4wMdH+xy6n/T/0+vVr3Lp1C0B6EVBFvB8+5s+fDwB49eoVXrx4AQDKhAlAlgvMFbN5REQFneQb9uZEUlISL+fWAysr9TffFQKYNCn9Srms7m4jl6f3a9cO+HCCwsoqd2PNKzY2Nvjxxx8xduxYnDx5EgcOHMCDBw9w9+5d/Pzzz1of5/z582jWrBmeP3+OJk2a4K+//kKZMmVgYWEBAMqF2NmdqpLL5TmKP6f9P/Ty5Uvl8/79+2PIkCHZ7qM4FZ+QkKDcZpXFP7iiKCgRUUGnc9K0e/du/P3334iOjsbr16/VfmkkJyfrOgzlopCQ9CvksqPtlXSGbsiQIZg+fbrykvu4uDj4+/vn6NY+ffr0wfPnz1GvXj2EhIRkmv05d+5cboedKzKeTrS0tISXl5fW+2Zcx/jmzRuN/fjzTUQfC52SpqFDh2LhwoVaLQTVtJaCcp+LS3plb3VnnoRIr8OU3SyTgqJuU/PmqrNNWY1haAoXLoxhw4ZhwoQJOHnyJORyOfbu3av1/s+fP8fFixcBpC+I1uV0WX4rVKgQypYti1u3buHq1atZ9l27di1MTU3x1VdfAQA8PT2VbTExMRr303RHACKigkbyb//t27djwYIFKF++PAIDA7FixQqYmJjg999/R1BQEIKCghAQEIAyZcrA1tYWS5cuzc24KQsuLumFJ9UlNIpZJm0SJkB1tknbMQzR8OHDUbhwYQDp903MyRq7jHWINP2BkFVSoW+KJOjo0aMqp9wyioyMRPfu3bFz507ltvLlyysTp0OHDqnd7/79+7h9+3YuR0xEZJgkJ01Lly5FpUqVcP78efz666/o2bMnZDIZOnToAH9/f/j7+yMgIAAREREoWbIk3r9/n5txkwSKWaacTpSYmGhXJdyQFS1aFP/73/8wffp0rW/kq+Do6Ki8umzDhg2ZqmC/fv0aq1evzrVYc9vIkSNRqlQpvH37Fr/88kum9pSUFAwfPhxmZmb46aefVNoUJQd27NihnG37sJ0lB4joYyE5aTp37hxGjhyZ7R3OCxUqhDFjxmDt2rVSh6JckpQE3L0L5LSAc1oacO9e+v6G6P79+4iKilLOeNy+fRtRUVF48uSJSr82bdpg5MiRma4Eu379OqKiohAfHw8gfY1OVFQUoqKikJiYCACYP38+zM3Ncf78eTRu3BjBwcEICwvD2rVrUadOHTx79kxl3+vXrwMA4uPjERUVhfv372c69ofxSen/5MkTjf0V7O3tsWfPHri6umL+/Pnw8/PD3r17ce7cOaxfvx6ff/45jh49isWLF6NKlSoqx/fz88PIkSORmpqKZs2aYcGCBQgPD8fBgwfRvXt3nDx5Ulm/SRFLbGxs1v9gRETGSmoFTTMzM3HmzBmVbebm5uL8+fOZ+oaFhQk7OzupQxkFQ6oInpW7d9OreGd8KKp7K6qIf9geHi7EvXv5FmKOKSp5f/jQtkK1m5ub2v2Robq4EEJcvHhRfPXVV6J48eJCLpeLIkWKiLp164r58+eLpUuXquynqMytqNStbXw57R8QEKCx/4devnwpfv/9d1GjRg1RuHBhYWZmJlxdXUX37t3V/txmtGXLFtGwYUNRqFAhYWVlJSpWrChGjRolXr58KXx8fFTG/eqrr7R634VgRXAi0r+cfH/LhJA2t25ra4vt27ejUaNGym0ODg5YunQpOnXqpNJ369at+Oqrr5BkqFMVueD8+fPw9vZGeHi42mKE6ly9ehXdu3fHmjVrlKd/9CExMb3KN5BegymbyUOiXJPnPwOJd4H3cTnfz8IRsNH+6koiMl45+f6WfPVc+fLlsW/fPpWkqXz58liyZIlK0pSWloZZs2ZpXb2YiChXJN4Fdn4CpL3L+b4mlkDba0yciEiF5KSpXr16mDVrFlxcXNC7d28ULlwYzZo1w5QpU+Dj44N27dpBCIENGzbg/Pnzyit4yHA8fJj+ePv2v20XL/5XuNLFxXiujiPK5H2ctIQJSN/vfRyTJiJSIXkheMeOHZGcnIwff/wRs2bNApB+WbednR2OHz+On376CT///DPCw8Nhbm6OMWPG5FrQlDuWLAG8vVVvvlu/fvo2b+/0diIiIkoneaapcePGyquV7OzsAADFihXD/v370adPH0RERABIvxHo/PnzUblyZd2jpVw1YED6LVI04SwTERHRf3SqCK64s3tGNWrUwIULF/DixQukpaXBwcFBlyEoD/H0GxERkfby7Ia9RYsWzatDExEREeW7XEuabt++jStXriAhIQFFihRBpUqVMhURJCIiIjJWOidNwcHBCAwMxOXLlzO1eXl5ITAwEB07dtR1GCIiIiK90ul27T///DP8/PwQFRUFIUSmR2RkJLp06YLRo0fnVrwFikwmA6B6Q1iij4nis2+S0xsiEhHpgeSZpm3btmH69OkwMTFBu3bt0KRJE7i7u8Pa2hpv3rxBTEwMDhw4gF27dmH69OmoW7cu2rdvn5uxG73ChQsDAJ4/f67nSIj0Q/HZL6QoSU9EZMAkJ01z5syBo6Mj9u7dC29vb7V9hgwZgnPnzqFly5aYM2cOk6YPuLi4wNnZGcePH0f9jMWSiD4Sx44dg4uLC1x4GScRGQHJc+IXLlzAhAkTNCZMCjVr1kRgYCDCw8OlDlVgyWQyNGvWDHv27MGVK1f0HQ5Rvrpy5Qr27t2Lpk2bKk9VExEZMskzTampqahTp45WfevWrYvU1FSpQxVoffv2RUREBAYOHIiWLVuiQYMGsLe35xoPKpDS0tLw/PlzHDt2DHv37kX58uXRt29ffYdFRKQVyUlTuXLlEB8fr1Xf+Ph4lC7NezipY2Njg3nz5mHZsmXYv38/tmzZou+QiPKci4sLOnfujL59+8LGxkbf4RARaUVy0vTNN99g5cqV8PX1zbbvypUr0bZtW6lDFXg2NjYYPnw4hg0bhocPH+L169e8oo4KJBMTExQqVAguLi55f0rOwhEwsZR2014Ty/T9iYgykJw0jRgxAm3btsWQIUMwbtw4ODs7Z+rz6NEj/Pbbb7hw4QIWLFigU6AA8P79e8yePRvr16/HzZs3IZfL4eHhAX9/f/Tv31+nU1q7du3CsmXLEBYWhqdPn8LMzAzu7u5o0qQJvv/+e5QtW1bn+LMjk8lQokSJPB+H6KNgUxpoew14H5fzfS0c0/cnIspAJoQQ2XXSNJuUlpaGEydOQAiBsmXLwtnZGaampkhJScHjx4/x77//QgiBatWqwc7ODgcPHpQcaFxcHHx9fREZGYn+/fvj22+/RVJSEubPn4+tW7fC19cXu3fvhqWlZY6OK4RA3759sWLFChQpUgTjx49H3bp18fLlS6xbtw5r1qyBlZUV1q9fj3ZZ3N32/Pnz8Pb2Rnh4OGrUqCH5dRIREVH+ydH3t9CCTCbT+WFiYqLNUBo1atRIABDDhw9X2Z6Wlibat28vAIiePXvm+LhBQUECgJDJZOLEiROZ2vv37y8AiCJFioinT59qPE54eLgAIMLDw3McAxEREelHTr6/tZppMjExwaBBg+Dk5CQpi3v8+DGWLFki+Qq6LVu2oEuXLrC0tMTDhw9hZ2en0h4dHQ1PT0/IZDKEhYVlWwYhoyZNmuDQoUOoVasWzp49m6n9ypUrqFSpEoD0tVn+/v5qj8OZJiIiIuOTk+9vrdc0DR48GJ6enpICioqKwpIlSyTtCwDLli0DkH6a8MOECQA8PDzg4eGB6OhorFixIkdJ0/379wEAZcqUUdue8abDjx490j5oIiIiKlC0Wjndvn17FClSRPIgtra2Wa4HykpSUpJyLVStWrU09lO07d69O0fHd3NzA6A5Icq4vXz58jk6NhERERUcWs00bd26VadBXF1dJR8jOjoaycnJAFRnfT6kaLtz5w5evnwJW1tbrY7fo0cPhISE4MyZM7h161amq+TWrVsHID1hat26dc5fABGRVIl3efUfkQGRXHLgQw8ePEBERIQyYalatWquXD5/9+5d5fNixYpp7JexLTY2VuukqVu3brhy5QqmTp2Ktm3bYt68efjss8+QkJCAdevW4ffff0ft2rWxZs0ara7Mi46O1tjGe2wRkdYS7wI7P5FeZ6rtNSZORLlM56Tp9OnTGDlyJE6dOpWprV69epgxYwZq164t+fivXr1SPs8qacnYlpCQkKMxJk2ahC5dumDEiBFo0qSJcru5uTmGDRuGkSNHonjx4lodq3v37hrbAgICEBgYmKPYiCj3HTgADBsGzJ0LNG2q72g0eB8nLWEC0vd7H8ekiSiX6ZQ0bdy4ET169EBSUpLa9uPHj6Nhw4b4+++/4efnp8tQeSYpKQmBgYGYMWMGSpUqhSVLlsDLywsJCQk4dOgQZs+ejYULF+KPP/7A4MGDsz3emjVr4OHhobaNs0xE+icEMHYsEB2d/t8mTQDeL5iItCE5abp16xb8/f2RnJyM1q1bo3nz5ihTpgysra3x5s0b3Lp1CyEhIdi7dy/8/f3h7e0tqap24cKFlc/fvdP8V1fGtpwsWvfz88OOHTtQtmxZXLp0SeU+WF988QUaN26MVq1aYciQIZDL5Rg4cGCWx/Pw8GDJASIDFhIChIWlPw8LS///Fi30GxMRGQfJSdPMmTNhYWGBQ4cOoW7dumr7DBs2DCdOnECbNm3w559/Yv78+TkeJ+ONfp8+faqxX8a2UqVKaXXskydPYseOHQCAcePGqb1xaMuWLdGgQQMcO3YMv//+e7ZJExEZLiGA8eMBExMgLS39v+PHA82bc7aJiLIn+WZt+/fvx6+//qoxYVKoV68efvnlF4SEhEgax8PDA2ZmZgCAmJgYjf0UbW5ublovAj958qTyeZUqVTT2q1q1KoD0mk5PnjzR6thEZHgUs0yK+2Gnpf0320RElB3JSVNsbCwaNmyoVd9GjRohNjZW0jjm5ubKxdnnzp3T2C/s/+fbc1IWQIti6JmYmubaBYdElI8Us0xyuep2uTx9u4RfB0T0kZGcNJmZmeHt27da9X379q1ytkiKvn37AgAOHjyIly9fZmq/evUqoqOjIZPJ0Lt3b62P6+XlpXx+6dIljf0iIiIApJ/2s7e31/r4RGQ4Fi1Kn1X68G5Oqanp2xct0k9cRGQ8JCdN5cuXx5YtW7Tqu2nTJp2qaXfu3Bk+Pj549+4dJkyYoNImhMDYsWMBQLngPKOdO3eiWLFi8PLyynR6r2nTpvjkk08ApJcdSExMzDT23r17cezYMQDAkCFDJL8GItIfIYDRo7PuM3o0Z5uIKGuSk6b27dtjwYIF+PPPPzXeiDclJQVTp07FokWL0LFjR8lBAsDmzZtRuXJlzJo1C4MGDcKJEycQGhoKPz8/bN26Fb6+vlik5k/Fv/76C3Fxcbh8+TKCg4NV2szMzLB161aULl0a//77LypXroylS5fi5MmT+Oeff/Dzzz+jffv2ANITspEjR+r0GohIP0JCgAwl39R69Yprm4goazIhZWEP0gtIenp64uHDh3BwcICPjw/Kli0LKysrZcmBI0eO4Pnz5yhZsiSioqJ0un8dALx//x6zZ8/GunXrcPPmTcjlcnh4eMDf3x8DBgyAiUnmHHDnzp3o1asXihcvjt27d6u9Fcvr16+xdOlS7NixA1FRUYiPj4eZmRlcXFxQp04d9OrVC82aNcsytpzcJZmI8o8QQJ06wPnzmU/NZSSXAzVqAGfOGMiVdM/PA/u0v/l4Jl+EA/b8XUSUnZx8f0tOmoD0tT6tW7fGgwcPIFPzW0YIgZIlS2LPnj2oXLmy1GGMApMmIsP0zz/AF19o33/fPgOp28SkiShf5OT7W/LpOSD9UvyIiAj89NNPcHd3hxBC+XB3d8fo0aMRERFR4BMmIjJMmq6Y04RX0hFRVnS+ft7BwQFTp07F1KlTkZiYqLxhr7pCkURE+Slj9W9tKK6kY5VwIlJHctKU8dL+GTNmwN7eHjY2NkyWiMggfFj9W1sGUyXcwhEwsZR2014Ty/T9iShXSU6aVq5cCQCoWLEiUlJSciseIqJckZQE3L2bs4QJSO9/7176/hYWeRObVmxKA22vAe/jcr6vhWP6/kSUq3Q6PTdmzBhMmjQpt2IhIso1Fhbpp9qyuGWlRk5Oek6YFGxKM/khMiCSkyY7Ozt06tQpN2MhIspVrq7pDyKi3CA5aapUqRKeavkn3NOnT7Fo0SL8+uuvUocjIiKi7CTe5SndPCQ5aerbty8WLlyIL7QogPLkyRNMmDCBSRMREVFeSbwL7PxE+sUDba8xccqG5DpN/v7+KFOmDDp06IALFy7kZkxERESUU+/jpCVMQPp+UmaoPjKSZ5rKli0LAIiNjcXOnTthZWUFBwcHyNVUkUtOTpYeIRERUX7iKS7SQHLSFBMTo/L/b968wZs3bzT2V3ebFSIiIoPCU1yUBZ1KDvz+++8oWbJktv1iY2O5nomIiAxfbpziYtJUYOmUNHXo0AGenp7Z9rt8+TLGjx+vy1BEpC88VUFEBECHpCkgIABOTk5a9S1VqhSCgoKkDkVE+sJTFQbpwAFg2DBg7lygaVN9R0OUiwz8jzSdkiZt2drawt/fX+pQRKQvPFVhcIQAxo4FoqPT/9ukiZ7vkaeJgX/5kQEygj/SdDo9l9Ht27dx5coVJCQkoEiRIqhUqRLc3d1z6/BERAQgJCT99jBA+n9DQoAWLfQbUyZG8OVHBsgI/kiTXKdJITg4GFWqVEH58uXRrl07dO/eHe3atUO5cuVQtWpVbN26NTfiJCL66AkBjB8PmPz/b24Tk/T/F0K/cWXCekEG50BUE3iOuowDUU30HYpR0ylp+vnnn+Hn54eoqCgIITI9IiMj0aVLF4wePTq34iUi+mgpZpnS0tL/Py3tv9kmynvGmngIAYzdMBnRDzwxdsNkw0uyjYjkpGnbtm2YPn06ZDIZ2rdvj7lz52LHjh04cOAAduzYgblz56Jdu3aQyWSYPn06tm/fnptxE5GeGesXiLFSzDJ9WD9YLjfQ2aYCxpgTj5DI5gi7VRsAEHarNkIim+s5IuMlOWmaM2cOHB0dcebMGWzduhVDhgxBmzZt4OvrizZt2mDIkCHYtm0bTp8+DXt7e8yZMyc34yYiPTLmLxBjtWhR+qxSaqrq9tTU9O2LFuknrpwy1mTbWBMPIYDxm36D3CQFACA3ScH4Tb8Z3c+soXxuJCdNFy5cwIQJE+Dt7Z1lv5o1ayIwMBDh4eFShyIiA2OsXyDGSgggu1UOo0cb/myTsSbbxpx4KH5WU9PSr/tKTTM1up9ZQ/rcSE6aUlJSUKdOHa361q1bF2mKk/BEZNSM+QvEWIWEAK9eZd3n1SvDX9tkrMm20SQeFo7pVx/+vw9/VhXU/syaWKbvb4AM6XMjOWkqXbo04uPjteobHx+PMmXKSB2KiAyI0XyBFBCa1jJ9yNDXNhlrsp2jxEPfbEqnl2v4Ihz4IhzLn1xX+VlVUPzMLn9yXdnXUMs8GNrnRnLS1KFDB6xcuVKrvitXrkTXrl1Vtt24cQNly5aVOjwR6YFRfYEUEIor5j5cy/QhxdomQ51tMtZk+8O4FQw2fpvSgH0NiKI18GNAhSy7/hhQAaJoDcC+hkEmTIDhfW4kJ01jxozB+fPnMWTIEDx69Ehtn0ePHmHw4MGIi4vDTz/9pNKWlJSEO3fuSB2eiPTA6L5AjJy2s0wKhjrbZFTJdoZTXJriVsgUvwGd4ioIp3QN8XMjuSJ4+/btYW5ujkWLFmHx4sUoW7YsnJ2dYWpqipSUFDx+/Bj//vsvZDIZ6tWrh2bNmqnsn5iYqHPwRJR/Mv4C+zBpAv77Rda8cohh3tbDCGWs/q2NjLNNhlQlPOOalIwyJtstqhjIt7fiFNf7OIQcKoywW5pna5TxW1xHC99XBnMLmIzJdlYzlIoku3lzw7wVjyF+biQnTYcPH4ZMJlMWsrx58yZu3ryptu/Ro0fVbpcZ4r8SEaml6ReYgkF+ARqxjNW/c3IdjaJKuKF8ERplsm1TGsK6NMb/oWXi8UcFNO9sGO83oH2ybahJNmC4nxud7j03cOBAODk5Sdr38ePHWLJkiS7DE1Fe+/9TFSL1XZa/wBRUfpHJDedUhTFKSgLu3s1ZwgSk9793L31/C4u8iS0njDXZNtbEQ9tZJgVDnW0y1M+NTknT4MGD4enpKWnfqKgoJk1Ehu7/T1WE7H2f5WkKBZXTFS0tDOJUhbGysEj/Mn76NOf7OjkZRsKU3WyBgqHNNhlz4mHUp3SN4I80yUmTv78/ihYtKnlge3t79OjRQ/L+RJQ/tD1NoaByuiLvwyvQXF3TH0bn/7/8Qi42zHK2QEFl1qDaUb3PUBpr4mH0p3SN4I80yVfPBQUFwcXFRfLAJUqUQFBQkOT9iSh/aHvJu4KhX/pO+cCmNESbaxgfEgy5XLtLnORygfEhwRBt9FsvKGPikROKxEOfVwLmxildfUv/I61Czq4Y/aMChHX+fGZ0Oj1HRAWb0f/lSnoTcqI0wi5o3z81VYawCzYIOWGj19kaY15LVhBO6Rr6LB+TJiLSyJi/QEh/jDnZNvbEw2hP6cI4PjdMmohII2P/AiH9MPZk25gTD2NmDJ8bJk1ElCV+gVBOMdkmKYzhc8OkiYiIch2TbZLC0D83kq+eIyIiIvqYMGkiIiIi0kKeJk2xsbE4evQo3rx5k5fDEBEREeU5yUmTr68v7ty5k2WfEydOoFGjRvD09ERUVJTUoYiIiIj0TnLSdPjwYSQmJmbZp3Hjxli9ejUKFy6MMWPGSB2KiIiISO/y9Oo5JycndO/eHSVKlEDXrl3zcigiIiKiPJUvC8FTUlLw8uXL/BiKiIiIKE9oPdM0ceLETNsWLlwIJycnjfsIIfD8+XNs3boVbm5u0iIkIiIiMgBaJ02BgYGQfXBTl0WLFmW7n/j/Wz5Pnz49h6ERERERGQ6tk6aGDRuqJE1Hjx6Ft7c3bGxsNB/c1BTOzs5o164d/Pz8dIuUiIiISI+0TpoOHz6s8v8mJiZYuXIlPD09czsmIiIiIoMjeSG4j49PlrNMRERERAWJ5KQpNDQ03xd3v3//HtOmTUP16tVRuHBh2NnZoW7duli8eDHS0tJ0Pv7FixcxaNAgVKxYEYUKFULhwoVRoUIFdOjQATNnzsTr169z4VUQERGRMcqXkgMxMTHw9fXV6RhxcXGoVasWRo8ejdq1a2Pv3r0IDg6Gi4sLBg0ahGbNmuHdu3eSjz9+/HjUrFkTT58+xbRp03DkyBFs2bIFDRs2xPbt2zFy5EjExsbq9BqIiIjIeOVpcUuFxMREHDlyRKdj+Pn5ITIyEsOHD8fs2bOV2xs3boyOHTti+/btGDRoEIKCgnJ87MDAQPz++++YNWsWvv/+e5W25s2bw8zMDEuWLNEpfiIiIjJuWiVNvXv31mmQ+Ph4nfbfsmULDh8+DEtLSwQGBqq0yWQyTJkyBdu3b8eqVaswZMgQeHt7a33sS5cuYdKkSahbt26mhElh9OjRqFatGpydnXV4FURERGTMtEqaVq5cCZlMpqy5lBOK/T6s8ZQTy5YtA5B+k2A7O7tM7R4eHvDw8EB0dDRWrFiRo6Rp+vTpSElJQc+ePTX2cXd3x8CBA3MaNhERERUgWp+ea9eundqERRvx8fHYsWOHpH2TkpJw8OBBAECtWrU09qtVqxaio6Oxe/duLFiwQKtjv3//HsHBwQCAzz77TFJ8RERE9HHQOmmaNGmS5JpMUVFRkpOm6OhoJCcnA0if8dFE0Xbnzh28fPkStra22R770qVLePPmDQDAzc0NGzduxLJly3DhwgW8efMGzs7OaNSoEX744Qd4eXlJip+IiIgKBq2SJl1rMhUqVAgNGzaUtO/du3eVz4sVK6axX8a22NhYrZKmK1euKJ/369cP+/btw9ixY/Hbb78hOTkZ27dvx+zZs/H3339jyZIl6NWrV7bHjI6O1tjm4uICFxeXbI9BREREhkerpCk0NFSnQdzd3SUf49WrV8rnlpaWGvtlbEtISNDq2M+fP1c+37x5M44cOYIGDRoot9WvXx/lypXDoEGD0L9/f1SqVAm1a9fO8pjdu3fX2BYQEJBpITsREREZh3wpOfDgwQOMGzcOK1asyI/htJaYmKh83qxZM5WESWHAgAGYOnUq7ty5g0mTJmH79u1ZHnPNmjXw8PBQ28ZZJiIiIuOVL0nTixcvsGrVKklJU+HChZXPsypembGtSJEiWh3byspK+VzT6UOZTAYfHx+sXr0aBw8eRGpqKuRyucZjenh4oEaNGlqNT0RERMZDctI0ceJErfs+efJE6jAoXbq08vnTp0819svYVqpUKa2ObW9vr3xevHhxjf1KliwJIH1m6vnz51murSIiIqKCSXLSFBgYqHXtJV3qNHl4eMDMzAzJycmIiYnR2E/R5ubmptUicAAqV8SlpqZq7CelPhUREREVLDqdnvP29lZ7VV1KSgri4uJw8+ZNyGQyfP7555KTJnNzczRp0gT79u3DuXPnNPYLCwsDALRu3VrrY1etWhV2dnaIj49XuUrvQ/fv3weQftrPwcFB6+MTERFRwaFT0rRy5cosaze9ePECf/75J65cuYLNmzdLHqdv377Yt28fDh48qLYG09WrVxEdHQ2ZTJajW76Ym5uja9euWLRoEQ4ePIhJkyZl6iOEUN43r1WrVjAxyZd7HBMREZGBkZwBtGjRQmWRtjpFixbFb7/9hrJly+LPP/+UOhQ6d+4MHx8fvHv3DhMmTFBpE0Jg7NixAAB/f/9Mt1DZuXMnihUrBi8vL7Wn9wICAmBnZ4czZ85g586dmdqXLFmCu3fvwtraGgEBAZJfAxERERk3yUnT3r174erqqlXfzp0761xuYPPmzahcuTJmzZqFQYMG4cSJEwgNDYWfnx+2bt0KX19fLFq0KNN+f/31F+Li4nD58mXlLVMyKl68OHbt2gVbW1t8/fXX+O2333DmzBmcOHECo0aNwtChQ1GkSBFs3rwZn376qU6vgYiIiIxXvpxrevPmDW7fvq3TMRwdHREWFoapU6fi1KlTaNGiBTp06IDY2FgsXLgQ+/fvV1v8sn///nBwcICnpyc6deqk9tj16tVDdHQ0BgwYgDVr1qBx48Zo0aIF9u3bh++//x5XrlxBy5YtdYqfiIiIjJtM5MOlYT179sSuXbsQFxeX10Ppzfnz5+Ht7Y3w8HDWaSIiIjISOfn+lrwQfPXq1Vm2v3//Hg8ePMChQ4dw/PhxdOjQQepQRERERHonOWnq2bOnVmUEhBCws7NTe2UaERERkbHQqeSAi4sLzMzM1LZZWFjA2dkZ9erVw+DBg1GiRAldhiIiIiLSK52SppCQkCzrNBEREREVFJKvnitevLjGWSYiIiKigkbyTNPDhw9zMw4iIiIig5YvdZoePHiQo9ubEBERERmafEmaXrx4gVWrVuXHUERERER5QqeF4ADw9OlTbNmyBdHR0Xj9+jXU1cqMj4/XdRgiIiIivdIpaVq/fj369euHN2/eqGxXJE6KOk5CCK1qOhEREREZKslJ08WLF9GjRw/I5XL4+PjAzc0Nq1evRrt27WBnZwcAiImJwcmTJ2FtbY2OHTvmVsxERERE+U5y0jRr1izY2tri0KFDqFy5MgBgzZo1mDRpkkrtpgsXLqBZs2b48ssvdY+WiIiISE8kLwQ/duwYfvrpJ2XCpEn16tUxbtw4LFy4UOpQRERERHonOWl6+PAh6tSpo7JNJpMhNTU1U9/PPvsMZ8+elToUERERkd5JTppMTExgbm6uss3KygoPHjzI1DchIQEvXryQOhQRERGR3klOmlxdXREWFqayrVSpUtixY0emvhs2bICNjY3UoYiIiIj0TvJC8Fq1amHy5Mlo0KABqlWrBgCoX78+li5dCnt7e3To0AFCCKxZswarVq1C48aNcytmIiIionwneaapdevWePz4Mby9vTFv3jwAwHfffYe0tDRMnjwZtWvXRp06dTBv3jwIITB06NBcC5qIiIgov0meaerQoQOCgoIAQDnTVLVqVfz1118YPHgw3r9/nz6AqSkCAgLQrl073aMlIiIi0hOtkqaZM2diwYIFaNu2LebMmQMAsLS0hL+/f6a+vXv3Rtu2bXH8+HGkpaWhbt26KFGiRO5GTURERJTPtEqatm3bBrlcjpYtW2p10GLFirECOBERERUoWq1punHjBv7880988cUXym1yuRxXrlzRapA3b97g6NGj0iIkIiIiMgBaJU3Pnz9HyZIlVbYpbsqrjdu3b/PqOSIiIjJqWiVNRYsWxYULF/I6FiIiIiKDpdWapjp16mDEiBG4ceMGypcvr6wEvn37dpw7dy7b/WNjY3WLkoiIiEjPtEqafvrpJ+zbtw/Tp09X2T5u3Lg8CYqIiIjI0GiVNNWvXx///PMPpk+fjps3byI5ORl37tyBi4sLzMzMst0/OTkZDx8+1DlYIiIiIn3Rurhl48aNVRZzy+VyhISEwNPTM9t9o6KiULVqVWkREhERERkAybdRycnVczKZLEf9iYiIiAyN5NuopKWlad23UqVKOepPREREZGgkzzQRERERfUx0TppevXqF2bNno23btqhatSpu3rwJADh27BhWrFiBpKQknYMkIiIi0jedkqZTp06hYsWKGDFiBHbv3o2oqChlknT9+nX07dsXHh4euHjxYm7ESkRERKQ3kpOmx48fo127dnj8+DGsra1RuXJllfYuXbpgzpw5ePv2LVq0aIEnT57oHCwRERGRvkhOmubMmYPnz59j5syZeP78OSIiImBi8t/hbG1tMXToUJw7dw5mZmaYOXNmrgRMREREpA+Sk6a9e/fiu+++ww8//JBlgcsSJUpg9OjR2L17t9ShiIiIiPROctJ069YttG7dWqu+NWvWxO3bt6UORURERKR3kpOmpKQk2NraatU3JSVF6jBEREREBkFy0lSiRAmEhYVp1Xfnzp1wdXWVOhQRERGR3klOmpo0aYIJEybg6tWrWfYLDg7G3Llz0bx5c6lDEREREemd5NuojBo1CqtXr0a1atXQo0cP+Pj4QAiBs2fP4ubNm7h69Sp27dqFEydOwNLSEj/++GNuxk1ERESUryQnTRUqVMDy5cvRq1cvLF++HMuXLwcA9OnTR9lHCAFTU1OsXLkS7u7uOgdLREREpC86VQTv1q0bQkNDUbt2bQghMj3q1q2LI0eOwM/PL7fiJSIiItILyTNNCvXq1cOpU6cQGxuLiIgIvHz5Era2tqhatSpKlSqVGzESERER6Z3OSZNCqVKlmCQRERFRgaXT6Tlt3b9/H998801+DEVERESUJ/IlaYqPj8eGDRvyYygiIiKiPKHV6bmJEyfqNMiTJ0902p+IiIhI37RKmgIDAyGTySQPIoTQaX+F9+/fY/bs2Vi/fj1u3rwJuVwODw8P+Pv7o3///jAxyZ2Js9TUVHz++ec4e/YsgPT4iYiI6OOm9UJwb29v2NjYqGxLS0vDiRMnIIRAmTJl4OzsDDMzMyQnJ+PRo0e4ffs2hBCoVq2a1vep0yQuLg6+vr6IjIxE//79MW/ePCQlJWH+/PkYNGgQNm3ahN27d8PS0lKncQDgzz//VCZMREREREAOkqaVK1fC09NT+f+pqalo27YtevXqhcDAQJQsWTLTPg8ePEBAQABOnz6NnTt36hSon58fIiMjMXz4cMyePVu5vXHjxujYsSO2b9+OQYMGISgoSKdxrl27hl9//RWFChXC69evdToWERERFRxanc9yc3ODubm5yrZZs2bBwcEBS5cuVZswAek39V26dCmqVauGSZMmSQ5yy5YtOHz4MCwtLREYGKjSJpPJMGXKFADAqlWrEB4eLnmctLQ09O7dG87Ozhg4cKDk4xAREVHBo1XSdPv2bZQvX15l29q1a9GrVy+tBunVqxe2bduW4+AUli1bBgDw9fWFnZ1dpnYPDw94eHhACIEVK1ZIHmfu3Lk4efIkli5dmulUJBEREX3cJK+cvnnzptoERh07Ozvcu3dP0jhJSUk4ePAgAKBWrVoa+ynadu/eLWmcf//9F7/88gv69u2Lpk2bSjoGERERFVySkya5XK71YunTp09DLpdLGic6OhrJyckAkOVNfxVtd+7cwcuXL3M0hhACffr0QdGiRTFjxgxJcRIREVHBJjlpqlatGgICAhAZGZllv0uXLiEwMBA1atSQNM7du3eVz4sVK6axX8a22NjYHI2xcOFCHDlyBIsXL9b5Kj8iIiIqmCTfe2748OHo3LkzvL290b59e/j6+sLd3R1WVlZ48+YNYmJicOjQIezYsQOpqakYPny4pHFevXqlfJ5VOYGMbQkJCVofPyYmBqNHj0a3bt3Qpk0bSTFmFB0drbHNxcUFLi4uOo9BRERE+U9y0tSxY0eMGDECM2fORHBwMIKDg9X2E0Jg1KhR6NChg9Sh8lS/fv1gbW2NOXPm5MrxunfvrrEtICAg09V/REREZBwkJ00AMH36dNSuXRuBgYFqZ1g8PT0RGBiILl26SB6jcOHCyufv3r3T2C9jW5EiRbQ69tKlS3HgwAFs3LgRDg4OkmPMaM2aNfDw8FDbxlkmIiIi46VT0gSkF5308/PDv//+iytXriAhIQFFihSBp6cnypUrp3OApUuXVj5/+vSpxn4Z20qVKpXtcWNjYzFy5Eh07NgRfn5+ugWZgYeHh+T1W0RERGS4dE6aFMqVK5crSdKHPDw8lLdmiYmJ0dhP0ebm5qbVYu4DBw4gISEB27dvh6lp5rchLS1N+Txj+6+//opff/1V+xdAREREBUKuJU15xdzcHE2aNMG+fftw7tw5jf3CwsIAAK1bt9bquB06dEDNmjU1ti9cuBCLFi0CAFy8eFG53cnJSavjExERUcFi8EkTAPTt2xf79u3DwYMH8fLly0wzSVevXkV0dDRkMhl69+6t1THt7OyyLM6ZMTny8vKSFDeRUuJd4H1czvezcARsSmffj4iI8pxRJE2dO3eGj48Pjhw5ggkTJuDPP/9UtgkhMHbsWACAv78/vL29VfbduXMnevfujeLFi2PXrl1ZFsgkyhOJd4GdnwBpmi9k0MjEEmh7jYkTEZEBMIqkCQA2b94MX19fzJo1C2/fvkX37t2RlJSEBQsWYOvWrfD19VWeTsvor7/+QlxcHOLi4hAcHIwff/xR4xjx8fHKwphPnjxRbo+KigKQfqqwYsWKufzKqMB7HyctYQLS93sfx6SJiMgAGE3S5OjoiLCwMMyePRvr1q3D33//DblcDg8PDyxcuBADBgyAiUnmAuf9+/fHqVOnULx4cXTq1CnLMbZt26b2JsSVK1cGkL7IPKvF6ERERFRwyYQQQt9BFATnz5+Ht7c3wsPDWXKAVD0/D+zzzr6fJl+EA/b8TBER5YWcfH9LvvccEenuQFQTeI66jANRTfQdChERZYNJE5GeCAGM3TAZ0Q88MXbDZHDOl4jIsOVL0nT//n188803+TEUkdEIiWyOsFu1AQBht2ojJLK5niMiIqKs5EvSFB8fjw0bNuTHUERGQQhg/KbfIDdJAQDITVIwftNvnG0iIjJgWl09N3HiRJ0GyXj5PhGpzjIBQGqaqXK2qUWVED1GRkREmmiVNAUGBkImk0keRAih0/5EBUnGWabUtP9+BBWzTc0rh4A/LkREhkfrOk3e3t6wsbFR2ZaWloYTJ05ACIEyZcrA2dlZeXPdR48e4fbt2xBCoFq1alrdRJfoY/DhLJMCZ5uIiAyb1knTypUr4enpqfz/1NRUtG3bFr169UJgYCBKliyZaZ8HDx4gICAAp0+fxs6dO3MnYiIjpmmWSYGzTUREhkurheBubm4wNzdX2TZr1iw4ODhg6dKlahMmAChRogSWLl2KatWqYdKkSbpHS2TkFLNM6hImQHW2iYiIDItWSdPt27dRvnx5lW1r165Ve8sRdXr16oVt27blODiiguTDK+Y04ZV0RESGSXLJgZs3b8LOzk6rvnZ2drh3757UoYiMm4UjYGKZ7SyTgspsk4ll+v5ERKR3kpMmuVyOs2fPatX39OnTkMvlUociMm42pSHaXMP4kGDI5dpNH8nlAuNDgiHaXANsSudxgEREpA3JSVO1atUQEBCAyMjILPtdunQJgYGBvIktfdRCTpRG2AUbpKZqt7o7NVWGsAs2CDnBhImIyFBoffXch4YPH47OnTvD29sb7du3h6+vL9zd3WFlZYU3b94gJiYGhw4dwo4dO5Camorhw4fnZtxERkMIYPx4wMQESEvTfj8Tk/T9mjcHr6QjIjIAkpOmjh07YsSIEZg5cyaCg4MRHBystp8QAqNGjUKHDh2kDkVk1JKSgLt3c5YwAen9791L39/CIm9iIyIi7UlOmgBg+vTpqF27NgIDAxEdHZ2p3dPTE4GBgejSpYsuwxAZNQsLICwMePo05/s6OTFhIiIyFDolTQDg5+cHPz8//Pvvv7hy5QoSEhJQpEgReHp6oly5crkRI5HRc3VNfxARkfHSOWlSKFeuHJMkIiIiKrAkXz2nzosXL5CampqbhyQiIiIyCDonTadOnUKHDh1gZ2eHYsWK4dq1awCAjRs3okePHrh8+bLOQRIRERHpm05J06JFi9CwYUPs3LkTCQkJEBnu+5CSkoI1a9bA29sbq1at0jlQIiIiIn2SnDRdunQJw4YNQ1paGpo2bYqhQ4dClqGYzDfffIOIiAh8/vnn6NevHyIiInIlYCIiIiJ9kJw0zZkzB9bW1jhz5gz++ecfzJkzRyVpAoDKlStj//79qF69Ov7880+dgyUiIiLSF8lJ09GjR/HLL7+gZs2aWfaTy+X44YcfcOTIEalDEREREemd5KTpwYMHqFu3rlZ9K1SogEePHkkdioiIiEjvJCdNJiYmSEpK0qrv06dPYWlpKXUoIiIiIr2TnDSVK1cO+/bt06rvihUrUKFCBalDEREREemd5KSpffv2mD17NlasWKGxz+vXrzFs2DBs2bIFnTp1kjoUERERkd5Jvo3KDz/8gGXLlqFfv374448/0KBBA6SlpWH+/PmwtLTE1atXcfToUbx9+xaurq4YOnRobsZNRERElK8kJ012dnbYs2cPWrdujevXr+PGjRsAgCVLlij7CCFQqlQp7N69G4UKFdI9WiIiIiI90akieNWqVXHp0iX8/PPPKFOmDIQQykeZMmUwZswYREREoFKlSrkVLxEREZFeSJ5pUrC3t8eUKVMwZcoUJCYm4uXLl7C1tYWNjU1uxEdERERkEHROmjKysbFRSZZiY2Nx69Yt1KxZE9bW1rk5FBEREVG+knx6ztfXF3fu3Mmyz4kTJ9CoUSN4enoiKipK6lBEREREeic5aTp8+DASExOz7NO4cWOsXr0ahQsXxpgxY6QORURERKR3uXp67kNOTk7o3r07SpQoga5du+blUERERER5Sqer57SVkpKCly9f5sdQRERERHlC65mmiRMnZtq2cOFCODk5adxHCIHnz59j69atcHNzkxYhERERkQHQOmkKDAyETCZT2bZo0aJs9xNCAACmT5+ew9CIiIiIDIfWSVPDhg1VkqajR4/C29s7y3pMpqamcHZ2Rrt27eDn56dbpERERER6pHXSdPjwYZX/NzExwcqVK+Hp6ZnbMREREREZHMkLwX18fFj1m4iIiD4akksOhIaG5mYcRERERAYtz0oOpKam4t9//82rwxMRERHlK8lJ07Nnz1C8eHHY29vD3t4+08zTu3fvULFiRbRu3RpxcXE6B0pERESkT5KTpg0bNuDp06cQQsDPzw8VK1ZUabewsEDXrl0RGhqKevXqIT4+XtdYiYiIiPRGctK0b98+uLq6IiIiAkuWLEHJkiVV2k1NTbFmzRqcOnUKL168YJ0mIiIiMmqSk6aIiAj8/PPPKF26dJb9qlatip9++gnbtm2TOhQRERGR3klOmp48eYLq1atr1ffzzz/H7du3pQ6l9P79e0ybNg3Vq1dH4cKFYWdnh7p162Lx4sVIS0uTdMzExET8/fff+PLLL1G2bFlYWVnB2toa5cqVQ/fu3XH06FGd4yYiIiLjJzlpMjXVvlqBTCaDXC6XOhQAIC4uDrVq1cLo0aNRu3Zt7N27F8HBwXBxccGgQYPQrFkzvHv3LkfHDA8PR+nSpdGjRw9cuXIF48aNw8GDB7F//358++232Lp1K3x8fDB48GDl7WCIiIjo4yS5TlOZMmWwd+9e1K1bN9u+e/bsQZkyZaQOBQDw8/NDZGQkhg8fjtmzZyu3N27cGB07dsT27dsxaNAgBAUFaX3Mhw8f4vnz56hWrRpOnz4NCwsLZVu9evVQu3ZttG7dGgsXLkSZMmUwcuRInV4DERERGS/JM00tW7bEjBkzsHnz5iz7bdy4ETNnzkTr1q2lDoUtW7bg8OHDsLS0RGBgoEqbTCbDlClTAACrVq1CeHh4jo8/fvx4lYRJoVWrVmjQoAEAqCRqRERE9PGRPNP0448/4q+//sJXX32FKlWqoGnTpso1QW/fvsWtW7dw4MABXLp0CXZ2dvjhhx8kB7ls2TIAgK+vL+zs7DK1e3h4wMPDA9HR0VixYgW8vb21Om65cuUwYsQINGrUSGOfqlWr4tixY7h//z6ePXsGBwcHKS+BiIiIjJzkpKl48eLYvHkzOnbsiIiICFy6dClTHyEEihQpguDgYDg5OUkaJykpCQcPHgQA1KpVS2O/WrVqITo6Grt378aCBQu0OraHhwdmzJiRZR/FWiwTExNYWVlpGTUREREVNDrdRqVJkyY4f/48vvzyS1hYWEAIoXxYWlqia9euCA8Ph4+Pj+QxoqOjkZycDABwd3fX2E/RdufOHbx8+VLyeB+6ceMGAMDb2xvW1ta5dlwiIiIyLpJnmhTKly+P9evXIykpCTdu3MDLly9ha2uLChUqwNzcXOcA7969q3xerFgxjf0ytsXGxsLW1lbnsePi4nDgwAEAwE8//aTz8YiIiMh46Zw0KZibm6NSpUq5dTilV69eKZ9bWlpq7JexLSEhIVfGnjlzJpKSktCxY0d06dJFq32io6M1trm4uMDFxSVXYiMiIqL8lWtJU1Zu3LiBFi1a4NatW/kxXK44duwYZsyYgYoVK2L58uVa79e9e3eNbQEBAZmu/stXiXeB9xJunmzhCNhkXfmdiIiooMuXpCkpKQl37tyRtG/hwoWVz7MqXpmxrUiRIpLGUrh69So6deqEkiVL4sCBAyhatKjW+65ZswYeHh5q2/Q6y5R4F9j5CZCWswKgAAATS6DtNSZORET0UdMqaTp27Bg2b96MgQMHKhMCX19frQdJTEyUFh2gcm+7p0+fauyXsa1UqVKSx7t27Rp8fX1hY2ODgwcPwtXVNUf7e3h4oEaNGpLHzzPv46QlTED6fu/jmDQREdFHTaukqVOnTnj+/DnOnz+PY8eOAQAOHz6co4FkMlmOgwPSkxAzMzMkJycjJiZGYz9Fm5ubm+RF4JGRkWjatCkKFy6MQ4cOZXszYiIiIvp4aJU0NWjQANu2bUPDhg1Vtg8aNEir+kuPHz/GkiVLJAVobm6OJk2aYN++fTh37pzGfmFhYQAgufL4+fPn0bx5czg5OeHAgQMoUaKEsi0lJQWxsbFwdnbOcjE6ERERFVxaJU3BwcF4/vw57O3tVbYPHjwYnp6e2e4fFRUlOWkCgL59+2Lfvn04ePCgsqRBRlevXkV0dDRkMhl69+6d4+OfPn0aX3zxBdzc3HDgwIFMpQ1iY2NRpkwZhIaGZlk9nIiIiAourYtbfpgw+fv7a71A2t7eHj169MhZZBl07twZPj4+ePfuHSZMmKDSJoTA2LFjlTF9eAuVnTt3olixYvDy8lJ7eu/o0aNo3rw5KlSogNDQ0CxrQREREdHHS/LVc0FBQVr3LVGiRI76q7N582b4+vpi1qxZePv2Lbp3746kpCQsWLAAW7duha+vLxYtWpRpv7/++gtxcXGIi4tDcHAwfvzxR2Xb6dOn0bJlS7x58wZRUVEa1zAJIXSKnYiIiIxfvpQcuH37Nrp164aTJ09KPoajoyPCwsIwe/ZsrFu3Dn///Tfkcjk8PDywcOFCDBgwACYmmSfO+vfvj1OnTqF48eLo1KmTStvp06fx5s0bAFmXMyADwTpTRESkRzKRD9Moly9fRpUqVZCamprXQ+nN+fPn4e3tjfDwcMMsOfD8PLDPW23TgagmGLZqLub6D0NTr4Pq9/8iHLDX4+tinSkiIsoDOfn+1mqmScri6ozi4+N12t+ovIwGnqvZbqCzHUIAYzdMRvQDT4zdMBlNKtWBxOoQeYt1poiISM+0SppWrlwJmUymdm1PxvpLGds/3C61TpPROdkdeKhmu4HOdoRENkfYrdoAgLBbtRES2RwtqoToOSoiIiLDo/Wapnbt2sHOzk5l25s3b7BlyxYUKlQINWvWhLOzs7IQ5aNHj3Du3DkkJCSgSZMmOlXpLhAMcLZDCGD8pt8gN0lBapop5CYpGL/pNzSvHGKYs01ERER6pHXSNGnSJJWaTG/evEHDhg0xYcIEjBw5EhYWFpn2ef/+PWbMmIElS5Zg7dq1uRMx5ZqMs0wAkJpmytkmIiIiDbSq0+Tj4wMbGxuVbVOmTEG9evXwyy+/qE2YAMDCwgK//PILOnbsmKm+EulXxlmmjBSzTayyQEREpEqrpCk0NBRubm4q27Zs2QI/Pz+tBvHz88O+fftyHh3lHgvH9HVV/08xy5SapjrZmHG2ScnEMn1/IiKij5jkOk137tzR+j5sFhYWePhQ3epoyjc2pdMXor+PgxBA98GfABAA1C1eEui+JBhPbl5LX9tkoFf+KWhVMoGIiEhHWt9G5UOWlpY4eFC7L6iDBw/CyspK6lBG5cy/tfQdgmY2pQH7Ggg5VwNx8TZQnzABgAxx8TYIOVcjvTaTASdMH5ZM4GlFIiLKK5KTpjp16uD333/Hnj17suy3a9cuTJ48GZ999pnUoYzK/JAhBv3FLQQwfjwgl2fdTy5P72fIrwVQXzKBiIgoL0g+Pffzzz/jn3/+Qdu2bVGjRg34+vrC3d0dVlZWePPmDWJiYnDo0CFcuHBB2f9jcOW+l0FffRYSAoSFZd8vNTW9X0gI0KJF3sclBUsmEBFRfpKcNPn4+GDOnDn4/vvvER4ejvPnz2fqI4SAiYkJZs+ejYYNG+oUqLEwkRnuF3fGWSZt7mijmG1q3hwG91oAlkwgIqL8Jfn0HAAMGTIEx48fxxdffAFTU1MIIZQPU1NTtGrVCidOnMCQIUNyK16DlybUXH1mIBSzTNreAjDjbJOhYckEIiLKb5JnmhQ+++wz7NmzB+/evcONGzeQkJCAIkWKoEKFClpfXVfQGOJpIsUsk4kJkJam/X4mJoY52/ThLJMCZ5uIiCiv6DTTlJGlpSUqV66MevXqoXLlyh9twgRoqHWkZ0lJwN27OUuYgPT+9+6l769XGepMaZplUsg028Q6U0RElAt0nmlSePbsGe7evQsPD4+POmFSMLTZJguL9FNtT5/mfF8np/T99SpDnamQQ4URdquCxq7KpNXiOlr4vjL4OlNERGQcdE6aNm7ciMmTJyMyMhIAEBkZCU9PTwQFBSEoKAjjxo1D8+aGM+OSXzKdJjKA2Q5X1/SH0bIpDWFdGuP/yH4xu1wOjP+jApp3NqzTikREZLx0Oj33yy+/oGvXrrh06RLEBytvHRwccPbsWbRs2RK//fabTkEaK7lcYHxIMESL8PRZEs526EzbxeyGvIidiIiMk+Sk6dixY5gyZQqsrKzQr18/zJgxAyYm/x2uXbt2ePDgAbp3747AwEAcPXo0VwI2JqmpMoRd+P/K2kyYdKZtYU4FYynQSURExkFy0rRw4UI4Ozvj6tWrWLJkCX788cdMfezt7bFq1So0bdoU8+bN0ylQY8Uv7txTkEomEBGR8ZGcNJ08eRLjx49HqVKlsu07YMAAnDx5UupQRo1f3LkjY8mEnFCUTGDSSkREupKcND158gTVqlXTqq+7uzvi4uKkDmX0+MWtO6MvmUBEREZP8tVzFhYWePnypVZ97927BxsbG6lDGb2MX9x6v3TfSBl9yQQiIjJ6kpOmTz/9FJs3b8YXX3yRZT8hBObNmwcvLy+pQxmVNWsAD4/M2/nFrTujL5lARERGTXLS5Ofnh59++gklSpTAuHHjYG5uDgCQZSiKc+vWLYwYMQKhoaGYO3eu7tEaAQ8PoEYNfUdBREREuU1y0jR48GCsWLECkyZNwpw5c1C7dm0IITB69GjI5XJcvXoV165dAwBUrlwZ/fv3z7WgiYiIiPKb5KTJ0tIS+/btQ9u2bXHp0iUcPHgQMpkMu3btAgBlsctq1aphx44dMDMzy52IiYiIiPRAp4rgrq6uOHv2LBYtWgRfX1/Y29tDLpfD3t4evr6+WLJkCc6cOaNVWQIiIiIiQ6bzvefMzc0xYMAADBgwIDfiISIiIjJIkpMmX19f5fP169fDyckpVwIiIiIiMkSST88dPnwYR44cgZmZGUxNdZ6wIiIiIjJoOq1p+vPPP/HPP//A3t4+t+IhIiIiMkiSkyYHBwc0aNAgN2MhIiIiMliSk6bq1avj3r17WvV98OABevfuLXUoIiIiIr2TnDQNGzYM06dPR3JycrZ9X7x4gVWrVkkdioiIiEjvJCdNbdq0QadOndCwYUNs3boVT548URa0JCIiIipoJF/2JpfLlc+7dOmSK8EQERERGSrJSVNOZ5Uy3siXiIiIyNjoVGApKCgI7u7u2fa7desW+vbtq8tQRERERHqlU9JUq1YteHp6ZtvP0dGR652IiIjIqEleCB4UFKT1jXjLlCmD0NBQqUMRERER6Z3kmSZ/f3+t+1pbW8PHx0fqUERERER6l6OZpgsXLqB3796oXr06KleuDD8/P+zfvz+vYiMiIiIyGFrPNAUFBaF///5IS0tTbrty5QqCg4Px888/Y/LkyXkSIBEREZEh0GqmKTo6GoMGDUJqaiqEELC2toatrS2EEBBCYNq0adi9e3dex0pERESkN1olTXPmzEFSUhK+/PJL3Lx5E69evcLz58/x6NEjjBgxAjKZDH/88Udex0pERESkN1qdngsNDUXjxo2xfv16le1OTk7K+88tXLgQ7969g6WlZZ4ESkRERKRPWs00xcbGYsCAARrbBw4ciNTUVDx8+DDXAiMiIiIyJFolTW/fvkWFChU0tpcvX17Zj4iIiKgg0rrkgIWFhcY2U1NTmJhoPtTly5dVbvBLREREZGwkVwTPqdy4jcr79+8xbdo0VK9eHYULF4adnR3q1q2LxYsXq5RCkOLly5f45Zdf4OHhAWtrazg6OsLX1zfTOi5Nnj59qvJfyj8PHz5EYGAgTw/nM77v+sP3Xj/4vuuPobz3WidNDx8+xN27d9U+7ty5k2WfBw8eQCaT6RRoXFwcatWqhdGjR6N27drYu3cvgoOD4eLigkGDBqFZs2Z49+6dpGPfvHkTlStXxtSpU9GxY0ccOnQIq1evRlpaGrp27Yru3btnm5TFxcWp/Jfyz8OHDzFhwgS9/zB9bPi+6w/fe/3g+64/hvLea13csnnz5rnSRyo/Pz9ERkZi+PDhmD17tnJ748aN0bFjR2zfvh2DBg1CUFBQjo77/v17tG7dGvfu3cOsWbPw/fffK9uaNm2KevXqYe3atahQoQICAgJy6dUQERGRsdF6pklRyFLqQxdbtmzB4cOHYWlpicDAQJU2mUyGKVOmAABWrVqF8PDwHB17/vz5uH79OkqUKIGhQ4eqtJmbm2PixIkAgGnTpuHBgwfSXwQREREZNa1nmiZNmoQSJUpIGiQ2Nha//vqrpH0BYNmyZQAAX19f2NnZZWr38PCAh4cHoqOjsWLFCnh7e+f42B06dFC7WL158+YoXLgwXr16hbVr12LUqFHSXgQREREZNa2Tpvbt28PT01PSIJcvX5acNCUlJeHgwYMAgFq1amnsV6tWLURHR2P37t1YsGCBVse+ffs2rl69muWx5XI5qlevjqNHj2L37t1MmoiIiD5SWp2e8/f3R9GiRSUPUrRoUfTo0UPSvtHR0UhOTgYAuLu7a+ynaLtz5w5evnyp1bEvXbqUaf+sjp2xPxEREX1ctJppyuni6g+VKFFC8jHu3r2rfF6sWDGN/TK2xcbGwtbWNteP/eLFCyQmJsLGxiZTn/fv3wMATpw4ofE4jo6OWY5D0kRHR6v8l/IH33f94XuvH3zf9Scv33vFMbUp0K316Tl9efXqlfJ5Vve1y9iWkJCQp8dWlzS9efMGALBo0SIsWrRIq/Epd3Xv3l3fIXyU+L7rD997/eD7rj95+d7HxMSgXr16WfYx+KTJWHz99dcAAGtra43V0znTREREZFjevn2LmJgYtGjRItu+Bp80FS5cWPk8q+KVGduKFCmS78d2dHTEkCFDtBqXiIiIDEd2M0wK+XYbFalKly6tfJ7VLUoytpUqVSpPjl20aFG1p+aIiIio4DP4pMnDwwNmZmYA0s83aqJoc3Nz02oROABUqVIl0/5ZHTtjfyIiIvq4GHzSZG5ujiZNmgAAzp07p7FfWFgYAKB169ZaH7tMmTL49NNPszx2amoqLly4kONjA0BycjK2bNmCHj164NNPP4WNjQ0sLS1RunRpdO7cGTt37szR8eg/eXnzZlKPn2fD0qVLF8hkMshksiz/6KPc8e+//2LEiBHw8vKCra0tbGxsULZsWbRs2RKTJk3C48eP9R1igXP8+HF88803cHd3h6WlJaysrFChQgX06dMHERER+glKGIHNmzcLAMLS0lLEx8dnao+OjhYAhEwmE+fOncvRsWfMmCEAiJIlS4rU1NRM7Xv37lWOHRsbq/Vx7927J0qWLCkAiNKlS4t58+aJI0eOiNOnT4sZM2YIBwcHAUB06NBBvHv3Lkcxf+yePn0qKleuLACI/v37i2PHjomDBw+Kjh07CgDC19dXvH37Vt9hFij8PBuWjRs3CgDKx+3bt/UdUoG2cOFCYWlpKZo0aSLWrVsnwsLCxMGDB8WIESOEXC4XAMTevXv1HWaBEhAQIAAICwsL8euvv4rDhw+L/fv3i2HDhgkTExMhl8vFokWL8j0uo0iahBDCx8dHABA//PCDyva0tDTll2XPnj0z7bdjxw7h6OgoKlWqpPYXy7t370TFihUFADFnzhyVtqSkJFGrVi0BQAQGBuYo3sjISAFAlCpVSjx79ixTe0REhDA1NRUAxODBg3N07I9do0aNBAAxfPhwle1paWmiffv2Gj8LJB0/z4bj6dOnwsnJSRQqVIhJUz4ICgoSAMT333+vtn3KlClMmnJZaGio8rO9bt26TO2TJ08WAISpqam4cuVKvsZmNElTxtmFgQMHiuPHj4tDhw6Jzp07Zzm70KZNG+WbP3PmTLXHvnHjhnB1dRVyuVz88ssv4tSpU2LPnj3KL+du3bqpnYXKiuJLRtOYQgjx7bffKjPpV69e5ej4H6uMs44vXrzI1H7lyhXJs46kGT/PhuPrr78Wtra2YtKkSUya8tiDBw9EkSJFhJubm3j//r3aPnFxcWLRokXi7t27+RxdwdW7d28BQDg5Oaltf/36tZDJZJImNHRl8GuaFBwdHREWFoapU6fi1KlTaNGiBTp06IDY2FgsXLgQ+/fvV1ugsn///nBwcICnpyc6deqk9tjly5dHZGQkfv75Z2zZsgWNGzdG9+7dIZPJsG7dOqxZswYmJjl7qxwdHTFixAi0b99eY5+qVasCSF+fc+3atRwd/2Ol7c2bhRBYsWJFPkdXcPHzbBi2bduG9evXY+bMmZJvoE7aW7hwIRISEvDNN9/A3NxcbR8HBwcMHDgQrq6u+RxdwXX//n0Amm9vZmNjA0dHRwDAo0eP8iusdPmaopGKWbNmKf9SvHr1qr7DMXjv378XZmZmAoAICAjQ2K9Hjx4CgHBzc8u32Iif57z2/Plz4ezsLJo1ayaE+O+0ETjTlGfKli0rAIjt27frO5SPSv/+/ZXrJ9VJSkpSriWbMWNGvsZmNDNNBdGNGzcAAM7OzihfvryeozF8eXnzZtIdP895a/jw4Xj9+jWWLl2q71A+Ck+fPsWtW7cApP9O2b9/P9q3bw8XFxdYW1ujdOnS6Nq1K06ePKnnSAueb7/9FjKZDHfv3lV7P9eNGzciNTUV9vb2+Pbbb/M1NiZNepKSkoItW7YAAEaMGAG5XK7niAyf1Js3U97j5zlv7d69G3///TemTJkCNzc3fYfzUbhy5Yry+e+//442bdqgUqVK2Lx5M0JDQ9G/f3/s2LED9evXx2+//abHSAue+vXrY968ebCwsMDXX3+Nbdu24eXLl3j27BlWrVqFoUOHomLFiti3bx+cnJzyN7h8ndcipcWLFwsAonbt2iIpKUnf4RiFtWvXKk9HHDhwQGO/pUuXKvudPHkyHyP8ePHznHfi4+NFyZIlRYMGDURaWppyO0/P5a3g4GCVsg5r1qzJ1GfPnj3K9i1btughyoLtxo0byou9FA8TExPRu3dvvX3mOdOUwerVq2Fqair5ERISotU4169fx6hRo+Dk5IT169crK54TGSN+nvPWiBEj8OzZMyxbtgwymUzf4Xw0EhMTlc8rVqyIbt26ZerTsmVLNGjQAAAwYcKEfIutoEtLS8OsWbNQrVo1HD16FLNmzcKRI0dw4MABTJgwARs3bkSFChUQEBCA1NTUfI3N4G/Ym5/S0tJ0+gfQphL148eP0bp1a2WSVaZMGcnjfWzy8ubNJA0/z3krJCQEy5cvxx9//IGKFSvqO5yPipWVlfJ5w4YNNfZr3Lgxjh07hkuXLuHx48coXrx4foRXoP3www+YO3cu7OzsEBERARcXF2VbkyZN0K5dO3h7e2PixIl4//49pk6dmm+xcaYpg549e0Kk166S9Pjiiy+yPP6jR4/g6+uLZ8+e4Z9//lFeok3aycubN1PO8fOct169eoV+/fqhVq1a+PHHH/UdzkfH3t5e+TyrRKhkyZLK5xnXXZI09+7dw/z58wEAw4YNU0mYFKpUqYKuXbsCAGbPno3Xr1/nW3xMmvJJbGwsfHx88PTpU4SGhqJWrVr6Dsno5OXNmyln+HnOe+Hh4bh79y7Cw8NhYWGRaTlAnz59lH3Lly+vdjtJ5+XlpXye1RkIIUR+hPPROHPmjPKsTZUqVTT2y1gXLuOi/bzG03P5ICYmBr6+vnj37h0OHz4MT0/PTO2Ojo4oVKiQniI0DoqbN+/bty/Xb95M2uPnOX/UqlULkZGRGtu3b9+OcePGAQD27NmjLHZZtGjRfImvoCtWrBg8PT1x5cqVLGeQFIUYZTJZlqVQSDtSklBT0/xLZTjTlMdu3LiBhg0bIiUlBUePHs30BQMAZcqUwebNm/UQnfHp27cvAODgwYNqazBdvXoV0dHRkMlk6N27d36HV+Dx85x/bGxs4OXlpfGR8bRQxYoV1W4n3fj7+wMADh8+rHHNamhoKACgTp06WZZCIe1knOG7dOmSxn4REREAAAsLC3zyySd5HpcCk6Y8dOXKFfj4+MDMzAzHjh1jwb9c0LlzZ/j4+ODdu3eZrlYRQmDs2LEA0n/ZeXt76yPEAoufZ/rYDB06FGXLlsWDBw+wYMGCTO379u3D8ePHYWJigilTpughwoLHw8MDjRs3BgDMnTsXDx8+zNTn0qVLWL9+PQCgV69esLGxybf4eHouj/z7779o1KgRnj59CnNzc1SqVEnfIRUYmzdvhq+vL2bNmoW3b9+ie/fuSEpKwoIFC7B161b4+vpi0aJF+g6zQOHn2TAkJibi9u3bAP47LQSkl31QLIbN+Jc66cbKygp79uxBkyZN8MMPPyAmJgadOnWCqakpDhw4gMmTJ8Pc3ByLFy9Go0aN9B1ugfG///0PLVq0wKVLl1C1alWMHTsW3t7eSE5OxsmTJzFt2jQkJyejefPmmDlzZr7GxqQpj0RGRiqv4kpKSkJSUpKeIyo4FDdvnj17NtatW4e///4bcrkcHh4eWLhwIQYMGJDjGyxT1vh5NgxhYWHKv8IzatGihfI5Fybnrk8++QSXL1/GzJkzsW3bNixZsgSpqalwdXVFjx498P333+fr6aGPgbOzM86dO4dVq1Zh8+bNmDZtGp4/fw6ZTAYnJyc0bdoU3bp1Q+fOnfO9dplM8CeMiIiIKFv8c5yIiIhIC0yaiIiIiLTApImIiIhIC0yaiIiIiLTApImIiIhIC0yaiIiIiLTApImIiIhIC0yaiIiIiLTApImIiIhIC0yaiIiIiLTApImIiPQmJSUFR44c0XcYBuHkyZN4+/atvsOgLDBpIqPRs2dPyGSyLB8mJiaws7PD559/jtmzZ+P9+/f6DpuMzMuXLzF9+nT4+PjAyckJ5ubmsLKygqurK3x8fDB06FCsWbMG9+/f13eoRu/y5cuoVq0a5s+fr1X/du3aQSaTwcfHJ48jk2748OGwtbXF+vXrVbZ/+PurUaNGmfbdunUrKlasiGPHjuVTtJRjgshIxMbGisjISPH7778LAAKA+Oeff0RkZKSIjIwUly5dEnv27BE///yzMDc3FwBEjRo1RHx8vL5Dpzym+DyEhobqdJywsDDh7Ows5HK5+Prrr8WWLVvEmTNnxNmzZ8X69etF586dlWNVrVpV7TH8/f0FAOHv769TLFkJCAgQAISPj0+ejZHXTp06JQoVKiS++OIL8ebNm2z7P3jwQMjlcuX7f/369XyIMucKFSokAIg2bdqobFf8/ho0aJDGf7uUlBTh7+8vTE1NRXBwcD5FTDnBmSYyGiVLloSXlxdKliyp3FaxYkV4eXnBy8sLlStXRsuWLTF16lTs2rULAHD+/HmMHj1aXyGTEUlMTET79u3x6NEjzJkzB+vWrUOnTp1Qu3Zt1KpVC1999RU2b96MWbNm6TtUo/fo0SO0adMG9vb22LRpE6ysrLLdZ+XKlUhNTVX+/4oVK/IyRMkmT56M2rVrY+TIkSrbFb+/nJycNO4rl8uxbNkyVKlSBV27dkVkZGReh0s5xKSJCqRmzZqhUqVKAIB169ap/LIlUmfXrl148OABLCws0L9/f439hg8fDnd39/wLrAD6/vvv8ezZM/z2228oVKiQVvusWLECPj4+KF68OABg1apVBvlzPXToUJw5c0byKURTU1P88ccfeP/+fZafQ9IPJk1UYHl6egJIX6MSFxen52jI0N26dQsAYGlpCTMzM439ZDIZhg0bhmbNmuVXaAXKjRs3sHHjRhQqVAhfffWVVvscOXIEN2/eRL9+/dC9e3cAwMOHD7Fnz568DFVvmjRpgjJlyuD06dMIDQ3VdziUAZMmKrBMTU2Vz83NzdX2efv2LWbOnIk6derA1tYWlpaWKF26NL7++mscPXo0U/9GjRqpLObs2bMnHj16hCFDhqBcuXKwtLSEvb09WrVqpfGKoJSUFOzcuRP9+vWDl5cXChUqBHNzc5QqVQpdunTB4cOHM+0TGBioduF7YGCgss/hw4cztffs2RMrV67MtB0ANm3ahDp16sDGxgYuLi745ptv8O+//yqPt2HDBtSsWRM2NjZwdHREt27dsl38fP36dfTv3x9ly5aFpaUlihQpgmrVqmHMmDF49OhRpv4fxrVy5UrExMSgR48ecHFxgYWFBcqVK4dffvkFSUlJGvdXaNy4scb3JzuKGY+XL19mezXXDz/8gOnTp6tsUyz0XbVqFYD0mZCMsXw4O3X27Fn89NNP+Oyzz1C0aFGYmZnBwcEBvr6+CAoKQlpaWqZxFZ+DCRMmAEhPJtT92y5evDjLsU+fPq12vw+9ffsWCxcuxOeffw4XFxeYm5vDxcUFLVq0wB9//IGYmJgs3yd1Vq9eDSEEGjRoAAsLC632WbZsGYoUKYJOnTqhZ8+eyu3Lly/XuI+6hdcJCQkYO3YsPDw8YGNjA1tbWzRq1Ahbt27NtP+HP3OK93DVqlWoX78+ihYtqvJzpqm/VIqkPCgoSKfjUC7T96IqopwKCgpSLga9ffu2xn516tQRAISHh4fa9jt37ghPT08BQPTs2VPs3r1bHD9+XMyfP18UL15cABCjR49W2efWrVsiMjJStG/fXgAQjRs3FiVLlhRDhw4Vhw8fFmfOnBGTJk0S1tbWQiaTiXnz5mUaNzQ0VAAQhQoVEhMnThRHjhwRx44dE/Pnzxeurq4CgJg2bZrKPo8fPxaRkZHC3d1dABBffvmliIyMFI8fP1b2ef36tYiMjBRDhw4VVlZW4uzZsyI2Nla8ePFCREZGihUrVijftz/++EN89dVXIjQ0VBw+fFj069dPABDOzs4iNjZWzJgxQwwePFicOHFC/PPPP6JNmzYCgKhQoYLGRbtr1qwR5ubmws7OTvz555/i+PHjYvfu3eK7774TMplMODg4iBMnTqjso1jEX6JECQFAjBkzRlSpUkWsXLlShIWFifXr14uyZcsKAOLrr7/ONKZif8XrWrFihXLbh+9PdiIiIpTHcXJyEqtXrxZJSUla769Y6Kv4bLRv314llmvXrqn0V4zVp08fERISIs6cOSP+97//ic8++0wAEO3atRMpKSkq+yg+B4rFxDVr1lQZIzIyUgghxPPnz1UumnBzc1M5zps3bzJ9Jj6UmJgoqlWrJmQymRg2bJg4cOCACAsLExs2bFDG6O7urvX7o1CzZk21P1uaxMfHCysrK9G3b1/lNm9vbwFAmJqaikePHqnd78OF11WrVhWenp6iW7duYv/+/SIsLEzMnz9fODg4CABi5MiRKvsr3uuM7+HQoUNF69atxe7du8XZs2fF6NGjlYv+1fVXR9tF/AsXLlR+FslwMGkio6NN0nT58mVhYmIi5HK52LVrV6b2d+/eiapVqwoAYuzYsZna7969K6ytrQUAsW7dukztiiukAIipU6dmag8ODhYAhFwuF6dPn1ZpUyRNW7ZsybTf/fv3hYODg5DJZCIiIiJT+5QpUwQAYWdnpzZ5SUtLE+XLlxe9evXK1KYYF2qu7BFCiMaNGwsAomPHjmLYsGEqbSkpKaJMmTICgFi+fHmmfY8fPy5MTU2FmZmZiIqKytQ+Y8YM5ReAuqsZ3dzcBABhbW2dKbm4cuWKMu4bN25k2leI3Lt6rmfPnspjARCOjo6iT58+YsuWLeLFixdaHUPbq+cAiKFDh2banpKSIurXry8AiLlz56rdV9svXsXPiqYv8IyfiQ/NmTNHABDffPNNpra3b98KT09PjcfVJCUlRXll6+LFi7XaR5E8ZEy458+fr4z7wz8wPqR4rwCIgQMHZmoPCwsTJiYmAoDYtGlTpnbFeyiXy0Xbtm1FWlqaSnuFChVU/q2ze8+1/bfbs2ePMu7Y2Ngs+1L+4ek5KlAePXqEDRs2oGXLlmjYsCEOHTqE1q1bZ+q3cuVKREREoFChQhg3blymdldXV3Tr1g1A+tUwmhQpUgTff/99pu0dO3ZE1apVkZqaiokTJ6q0ubu747fffkOHDh0y7VeiRAm0bdsWQgisWbMmU3ufPn1gYWGB+Ph4/O9//8vUHhISgps3b2Lw4MEaYwagtr1p06YA0mvFDBo0SKVNLpfD19cXANSethw5ciRSUlLw7bffKhfgZzR06FDY2NjgyZMnWZ5SadWqFSpWrKiyzcPDQ3nF5PHjx7N8XbpatmyZyuLkuLg4LF++HJ07d4ajoyMaNWqEZcuW4d27dzqPFRAQgBEjRmTaLpfL0bdvXwDA33//rfM4Ul25cgUA1C7UtrS0xPDhw9GkSZMcHfPu3bvK06xZXUWW0bJly1CxYkV8/vnnym1du3ZVntrL6vOUkUwmw/jx4zNtr1mzJtq2bQsAWZ7OTU1Nxa+//prpVObRo0cxY8YMrWLIiYzvz40bN3L9+CQNkyYyauXLl4epqany4eLigq+//hq1a9fG+vXr0bBhQ7X7bdy4EQBQu3ZtjZc7f/rppwCAyMhIPH36VG2fWrVqaVyXofhC2b9/v8qXrLu7O8aNGwcTE/U/fm5ubgCA6OjoTG3FihVDly5dAAALFy7M1L5w4ULUrl0b3t7eao+toK5d8UvaxsZG+dozcnZ2BpC+ADeje/fu4fTp0wCgtmAfkL6mrGzZsgCAgwcPaoyrVq1aarcrkiZ166Jyk1wux7hx43Dv3j0sWrQIzZo1U/77pqam4siRI+jXrx8+/fRTnDhxQqexAgMDlf/WH8rqM5BfFMlrUFAQFi1alClR7N+/v9YJi8Lz58+Vz62trbPtf/HiRZw/fx7+/v4q2+3t7ZWJzvXr17UqBlm2bFmUKFFCbZviZ/Xy5csq6/oysrKyQo0aNTJtd3Z2hqOjY7bj55SNjY3y+YsXL3L9+CQNkyYyanv27MHFixdx8eJFHDp0CD/++CNMTEywefNmNGrUCImJiWr3i4iIAACEhoaqJF0ZH6NGjVL2v3v3rtrjKBIJdRQLQZOTkzP9pXjr1i388MMPqFatGuzs7GBmZqYcVzEz9fr1a7XH/e677wCk16BSJCuKGHfv3p3tLBMAODg4ZNqmWDhvb2+vdh9F+4dV1hXvJZC++FbT+6moOaPpvdQUFwBlYpsbMzzasLOzw8CBAxESEoLnz59jx44d6Nmzp/KL7M6dO/jiiy9w+/ZtyWO8evUK06ZNQ/369VGsWDFYWFgo3yvFl7imz0B+GDhwIGrXro3k5GR89913KF68OL766iusWrUKz549k3TMN2/eKJ9rujgjo+XLl8PExAQ9evTI1NarVy+VftnR5mcV+G+G7UMODg4a/9DJCxnfH02/xyj/mWbfhchwVaxYUeUXXsOGDeHk5ITRo0fj6tWr+OOPP5RXGmX08uVLAEDr1q0xZcqUbMdRzJJ8KOMVeh/K+Jd0QkKC8vm+ffvQsWNHvHv3Dn5+fpg4cSJcXV2Vl7kvXLgQixYtghBC7XE///xzVK1aFREREVi4cCE+++wzAMCSJUtgZ2eHL7/8MtvXk9Uv/5x+MSjeSwD466+/UKdOnSz7Z/VlKZfLczR2frC2tkbbtm3Rtm1bzJw5EwMHDsSmTZvw+vVrLFy4MNNVdNq4c+cOGjVqhJiYGNSsWROzZ89GhQoVlJ+ZsLAw9O7dO7dfSo5YW1vjxIkTCAoKwvLly3HmzBls3LgRGzduhKmpKb766itMnz4dLi4uWh8z46xsSkpKln3fvXuHtWvXomnTpihVqlSm9hYtWsDFxQUPHz7Epk2bMHfuXBQpUkTj8aT8rGaU35/NjO+PtlcZUt5j0kQFzsiRI7F27VpERkZi9uzZ+P7771G0aFGVPra2tnj27Bnkcjm8vLwkj5XVL/6Mf1UrfpknJyfD398f7969Q9euXdWuS9Jmrcd3332HAQMGYNOmTZg1axaKFCmC5cuXo0+fPrC0tJTwSqSztbVVPndwcNDp/dSn5ORkvHz5Evb29hoTR3t7e6xcuRL79+9HfHw8oqKiJI31/fffIyYmBuXKlcORI0cynarKr7pi2SUupqam6NevH/r164e7d+9iy5Yt+N///odz585h7dq1OH36NCIiIlROJWUlY1KT3axhcHAwXrx4gf3792tMeBRlGd68eYP169dnWQwypz+r+pbx/TGUmIin56gAksvlysXbCQkJmD17dqY+VatWBQBcvXo1y2Nt374dK1as0Djr8/jxY437KmrYmJmZoUKFCgDS10c9efIEQPpicam6desGW1tbvHv3DsuXL8eWLVvw9OnTTAu484PivQSyfj/j4+OxbNkytQvJDcGJEydQrFixbNcRWVtbK9f7aHOKSR3Fuq4WLVpotbZHKsUMhaYbV+ckOStdujR++OEHhIWFYd26dTAxMcG///6L4OBgrY/h5uamXEid3djLli1D6dKlcenSJeUp+A8foaGhygQ3u1N02vysAlB7IYM+ZFxHWaZMGT1GQhkxaaICqU2bNqhXrx4AYM6cOYiPj1dpV1QivnbtGm7evKn2GM+ePcOXX36JFStWaCz+d/bsWY1fSAcOHAAANG/eXDn7k7FgoaZETJuCgTY2Nsp1HosXL8aCBQvQsmVLvdzew9XVFXXr1gUA5T3/1Pn777/Rr18/XL9+PddjUJw6yfieRkZGYv369TleB3XmzJls+zx48AAAUKVKlUxtilmRjLG8fv0a69evVyZkis+B1M+AujGEEFi/fj3OnTun3KZYxxMXF4fk5ORMxzl79qzGMYYPH65xYf/XX3+tfO0fXhiQFSsrK+Wp7qwKpd66dQuHDx/GwIEDlfeWVPfw8fFRXh179uzZLGf+/v33X+W/24cUP6uVKlXSeCo+vyneH0tLS4OJiZg0UQE2depUAOlrbubMmaPS1rNnT1SrVg0AMGLECLXVl3/88UckJSXhl19+0TjG69ev1c5kbd26FZcuXYJcLle5zLly5cqws7MDALUlBe7fv4/t27dn99IA/Lcg/Pbt2zh+/LhWC8DzyowZM2BqaooTJ05gy5YtmdofPHiAyZMnw83NTXkbjNykSA4yXp01Z84cfPvtt1muZVFn0qRJGq+WBID58+cjNjYWVlZW6NOnj1axXLx4EV27dlUmZA0aNAAA7Ny5U2VNGJB+ld7ixYuzjFHdGPfv30fXrl1VEldvb29YWloiJSUl0wzf06dPsXbtWo1jvHz5EidOnFA785aYmIh79+4BSL8CNScyXqmmyYoVK2Bubq4svZCVIUOGKJ9nNdskk8nw22+/Zdp+7tw55XuWkwryeU2RADZq1CjHn2HKQ3qrEEWUQ4oKv4qKuwDEP//8IyIjI8WtW7fU7tO6dWsBQBQtWlSEh4eLyMhIZZHCu3fvisqVKwsAwtfXV2zdulWcO3dObNmyRTRv3lwAEAEBAWqPqyhg6OfnJ6pXry6GDh0qjhw5Is6ePSsmT56sLIypriL4qlWrhEwmU1Z93rlzpzh9+rRYunSpcHV1FUWKFFGp9qzptQnxX0HKsmXLZiq6p6CoFJ6x+nPG6tGKiuGK97VEiRIq42qqQv1hXOvXrxeWlpbC3NxcjBo1Shw9elScPHlSzJs3T5QoUUI4ODiIc+fOqexz7do1lYrgv//+u8q/kaICu6KK9KBBg9RW+lbE1rhxY3Hy5Emxfv16UbhwYdGhQweN792Hjh8/rnx/ihcvLgIDA8X+/fvFhQsXxNmzZ8XatWtFhw4dBABRuHBhsXPnTrXHOX36tLLPli1bxPHjx4WPj48oVKiQuHfvnhAivZK5ra2tACC8vLzEmjVrxNmzZ8XmzZvF559/rvwMfPhvpXD37l1hYWEhTExMxIoVK8Tp06eFn5+fkMvlmd7jUaNGKV9TUFCQCAsLE5s2bRLe3t4qP0uKcRRV0BWFPkuWLClmzZql/HyvW7dOWW0/Y5VubR08eFD5OfuQ4me8ZMmSolmzZtlWdb9165a4dOmScHZ2FgCEg4ODuHTpkkqBVEUxyQYNGogWLVqI7t27iwMHDohz585lWRFc08+Fppg+rAiesX/G1/bhz9GHxVwVatWqpaxyT4aDSRMZjYxVuD98aKquGxERoaz2q3gEBQUp29+9eyfmzp0r6tWrJ2xtbYWpqalwdnYWnTp1EocOHco2Fn9/f/Hq1Svx008/iQoVKghLS0thZ2cnvvjiC3H48GGN+x8+fFi0adNGODg4CLlcLuzt7YWvr69Ys2aNSgXjrF6bEEJs3LhRABDTp0/X2Cdj1ecPH0KoVlhXN+6H8WQV17///isGDx6sfC8sLS2Fh4eHGDFihHj48GGm/opK4B8+FP9GPj4+ats/TGYTEhJE//79hbOzszAzMxOurq6ib9++4unTpxrfF3ViYmLEwoULRbdu3US1atWEvb29MDMzE2ZmZqJYsWKiQYMGYuLEiWpfS0YrV64UXl5eytvKNGzYUBw5ckSlz61bt0Tv3r2Fq6urMDU1FYUKFRI1atQQkyZNErt27VL7b5XR7t27Ra1atYSlpaUoXLiwqFmzpggODs7ULzU1VUyfPl18+umnwtzcXDg4OIgOHTqIiIgItZ8NRZX9V69eieXLl4tOnTqJsmXLCktLS2FqaiqKFy8uWrVqJTZv3pyj9zaj6tWrCwCZ3hN1P+Oa/nARQvPnI2M17owVuJOSksSkSZOEl5eXsLa2FoUKFRINGzZUW51f08+Fppg0/Zwo/u00/f5SVzn8xo0bQiaTCRcXF/Hu3Tut3lPKH0yaiCTQ9lYZeW3evHnC0tJSPHv2TK9xEOXEqVOnhImJiWjUqFGej6XtbUsMSbdu3QQAsWHDBn2HQh/gmiYiI7Z06VJ8/fXXGgtSEhmizz77DLNnz8bhw4fVrjP6mK1cuRJr167FDz/8oFXNNcpfXF1GZCTmzZsHmUymXPgaGhqKS5cuYeXKlfoNjEiCoUOHwsrKCkOGDEFqaqpBLcLWl2XLlmHQoEEIDAzEr7/+qu9wSA0mTUQ5cPv2bSQmJipLGCgKHNrY2OR5LZWIiAjs2rULFSpUgKmpKQYNGoSvvvoK1atXz9NxifJK37594ePjg507d+b6se/fv48XL14o66IlJiYiKioK5ubmmW4KbSiEEDh16hRq1qyp71BIA5kQGgqFEFEmjRo1wpEjRzJt9/HxweHDh/N07FmzZuHPP//E48ePYWtri7Zt22LevHlaV2Mm+pj07NkTq1atyrTdzc1Nq1poROowaSIiIiLSAheCExEREWmBSRMRERGRFpg0EREREWmBSRMRERGRFpg0EREREWmBSRMRERGRFpg0EREREWmBSRMRERGRFv4PXpkq4AP6HrAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions_credit(dfs['credit_train'], nomon_lattice_model, 'PAY_0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0aokp7qLQBIr"
   },
   "source": [
    "## Train monotonic calibrated lattice model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:58.031245Z",
     "iopub.status.busy": "2024-12-15T12:26:58.030542Z",
     "iopub.status.idle": "2024-12-15T12:26:58.035230Z",
     "shell.execute_reply": "2024-12-15T12:26:58.034359Z"
    },
    "id": "MbB2ixYMC6Za"
   },
   "outputs": [],
   "source": [
    "model_config.feature_configs[0].monotonicity = 1\n",
    "model_config.feature_configs[1].monotonicity = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:26:58.037957Z",
     "iopub.status.busy": "2024-12-15T12:26:58.037558Z",
     "iopub.status.idle": "2024-12-15T12:27:37.653561Z",
     "shell.execute_reply": "2024-12-15T12:27:37.652895Z"
    },
    "id": "wWCG7YrLUZDH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/83 [..............................] - ETA: 12s - loss: 0.5312 - accuracy: 0.7734"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "41/83 [=============>................] - ETA: 0s - loss: 0.4553 - accuracy: 0.8190 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "82/83 [============================>.] - ETA: 0s - loss: 0.4547 - accuracy: 0.8188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83/83 [==============================] - 0s 1ms/step - loss: 0.4548 - accuracy: 0.8188\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/12 [=>............................] - ETA: 0s - loss: 0.4451 - accuracy: 0.8320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "12/12 [==============================] - 0s 1ms/step - loss: 0.4426 - accuracy: 0.8301\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4279 - accuracy: 0.8359"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4551 - accuracy: 0.8172\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracies for train: 0.818762, val: 0.830065, test: 0.817172\n"
     ]
    }
   ],
   "source": [
    "mon_lattice_model = tfl.premade.CalibratedLattice(model_config=model_config)\n",
    "\n",
    "mon_lattice_model.compile(\n",
    "    loss=keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    metrics=[\n",
    "        keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "    ],\n",
    "    optimizer=keras.optimizers.Adam(LEARNING_RATES),\n",
    ")\n",
    "mon_lattice_model.fit(datasets['credit_train'], epochs=NUM_EPOCHS, verbose=0)\n",
    "\n",
    "train_acc = mon_lattice_model.evaluate(datasets['credit_train'])[1]\n",
    "val_acc = mon_lattice_model.evaluate(datasets['credit_val'])[1]\n",
    "test_acc = mon_lattice_model.evaluate(datasets['credit_test'])[1]\n",
    "print(\n",
    "    'accuracies for train: %f, val: %f, test: %f'\n",
    "    % (train_acc, val_acc, test_acc)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-15T12:27:37.656433Z",
     "iopub.status.busy": "2024-12-15T12:27:37.655950Z",
     "iopub.status.idle": "2024-12-15T12:27:38.852516Z",
     "shell.execute_reply": "2024-12-15T12:27:38.851854Z"
    },
    "id": "JCQ2eMdndFhR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/657 [..............................] - ETA: 1:06"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/657 [=>............................] - ETA: 0s  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/657 [===>..........................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/657 [=====>........................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/657 [=======>......................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/657 [==========>...................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "295/657 [============>.................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "345/657 [==============>...............] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "395/657 [=================>............] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "445/657 [===================>..........] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "495/657 [=====================>........] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "544/657 [=======================>......] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "593/657 [==========================>...] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "643/657 [============================>.] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "657/657 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmpfs/tmp/ipykernel_73126/4037607942.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data = df[[x_col, y_col]].groupby(xbins).agg(['mean', 'sem'])\n",
      "/tmpfs/tmp/ipykernel_73126/4037607942.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  data = df[[x_col, y_col]].groupby(xbins).agg(['mean', 'sem'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions_credit(dfs['credit_train'], mon_lattice_model, 'PAY_0')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "shape_constraints_for_ethics.ipynb",
   "private_outputs": true,
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}