diff --git a/docs/GroupFasterRisk.ipynb b/docs/GroupFasterRisk.ipynb new file mode 100644 index 0000000..d9a6c75 --- /dev/null +++ b/docs/GroupFasterRisk.ipynb @@ -0,0 +1,1041 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GroupFasterRisk Tutorial \n", + "GroupFasterRisk is FasterRisk with added functionalities designed to empower the algorithm to be more broadly applicable. We describe the usage of each added functionalities below. \n", + "\n", + "Note: We conducted a study on applying GroupFasterRisk to predicting in-hospital mortality rate in critical care. The paper is accepted by *Journal of the American Medical Informatics Association* and can be accessed at arXiv: https://arxiv.org/abs/2311.13015. This tutorial demonstrates how to use the new functionalities in practice." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group Sparsity\n", + "We first demonstrate the group sparsity constraint functionality added to FasterRisk. Group sparsity allows the users to define an arbitrary partition on the feature indices, and the user can control the risk score output using group sparsity constraint hyperparameter. In plain words, group sparsity constraints allow users to define their own grouping of the features and control the number of such groups. \n", + "\n", + "Group sparsity can be particularly useful on some specific settings such as healthcare, where summary statistics of a longitudinal measurement are used for prediction. To demonstrate how this is useful, we would like to provide an example below.\n", + "\n", + "*Note: due to privacy concerns, we use a synthetic healthcare dataset created by ourselves for the sake of this tutorial*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create synthetic data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create a synthetic dataset consisted of 5000 patients, each having 10 variables, and the label is in-hospital mortality\n", + "\n", + "| Variable Name | Abbreviation | Data Type |\n", + "| :------------ | ------------- | --------- |\n", + "| Mean blood pressure | `mbp` | Numerical |\n", + "| Age | `age` | Numerical |\n", + "| Urine output | `urine_output` | Numerical |\n", + "| Blood pH | `pH` | Numerical |\n", + "| Partial pressure of carbon dioxide | `pco2` | Numerical |\n", + "| Partial pressure of oxygen | `po2` | Numerical |\n", + "| Heart rate | `heartrate` | Numerical |\n", + "| Respiratory rate | `resprate` | Numerical |\n", + "| Glascow Coma Score | `gcs` | Discrete |\n", + "| Whether on mechanical ventilation | `mechvent` | Categorical |\n", + "\n", + "For each numerical feature, we have a minimum and a maximum statistic. We used maximum and minimum because extreme measurement deviations can be meaningful. (Generally, any number of summary statistics can work, but for sake of an example, we choose two for now.)\n", + "\n", + "Lastly, although each patients have 10 variables (measurements), the number of features is 17 due to the minimums and maximums on numerical features." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of columns: 18\n", + "Class ratio: 10.00%\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mbp_min mbp_max age urine_output ph_min ph_max \\\n", + "0 88.285451 37.774670 45.829026 365.981897 7.118860 7.317997 \n", + "1 171.317298 216.136067 54.424770 152.342177 7.016893 7.576222 \n", + "2 45.578771 159.949396 63.214146 1788.602639 7.323981 6.911997 \n", + "3 106.049858 150.372011 60.947875 396.310247 7.403791 7.215892 \n", + "4 97.947074 171.549639 49.208235 410.365580 7.281169 7.694581 \n", + "\n", + " pco2_min pco2_max po2_min po2_max heartrate_min heartrate_max \\\n", + "0 34.894704 47.099252 72.324957 115.533579 40.297151 106.758528 \n", + "1 29.606498 44.317206 66.382848 101.407490 63.424919 127.254880 \n", + "2 40.745520 50.286053 53.996778 112.071994 57.634115 121.159133 \n", + "3 36.571289 52.591003 68.190921 99.042468 64.518768 105.817592 \n", + "4 33.438987 47.124516 77.279352 109.797908 52.316980 121.275120 \n", + "\n", + " resprate_min resprate_max gcs_min gcs_max mechvent mortality \n", + "0 15.517411 21.264031 7.0 15.0 1 0 \n", + "1 14.547308 20.542515 10.0 15.0 0 0 \n", + "2 12.482416 19.315983 9.0 15.0 0 0 \n", + "3 16.936002 20.246226 8.0 15.0 1 0 \n", + "4 11.143535 17.950662 9.0 15.0 1 0 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.manifold import TSNE\n", + "import pandas as pd\n", + "\n", + "assumed_stats = {\n", + " \"mbp_min\": { # numerical\n", + " \"mean\": 120,\n", + " \"std\": 40,\n", + " \"diff\": -40, \n", + " },\n", + " \"mbp_max\": { # numerical\n", + " \"mean\": 160,\n", + " \"std\": 40,\n", + " \"diff\": -40,\n", + " },\n", + " \"age\": { # numerical\n", + " \"mean\": 50,\n", + " \"std\": 30,\n", + " \"diff\": 30,\n", + " },\n", + " \"urine_output\": { # numerical\n", + " \"mean\": 1000,\n", + " \"std\": 500,\n", + " \"diff\": 1000,\n", + " },\n", + " \"ph_min\": { # numerical\n", + " \"mean\": 7.2,\n", + " \"std\": 0.2,\n", + " \"diff\": -0.2,\n", + " },\n", + " \"ph_max\": { # numerical\n", + " \"mean\": 7.4,\n", + " \"std\": 0.2,\n", + " \"diff\": -0.2,\n", + " },\n", + " \"pco2_min\": { # numerical\n", + " \"mean\": 35,\n", + " \"std\": 5,\n", + " \"diff\": -5,\n", + " },\n", + " \"pco2_max\": { # numerical\n", + " \"mean\": 45,\n", + " \"std\": 5,\n", + " \"diff\": -5,\n", + " },\n", + " \"po2_min\": { # numerical\n", + " \"mean\": 70,\n", + " \"std\": 10,\n", + " \"diff\": -10,\n", + " },\n", + " \"po2_max\": { # numerical\n", + " \"mean\": 100,\n", + " \"std\": 10,\n", + " \"diff\": -20,\n", + " },\n", + " \"heartrate_min\": { # numerical\n", + " \"mean\": 60,\n", + " \"std\": 10,\n", + " \"diff\": 20,\n", + " },\n", + " \"heartrate_max\": { # numerical\n", + " \"mean\": 100,\n", + " \"std\": 10,\n", + " \"diff\": 20,\n", + " },\n", + " \"resprate_min\": { # numerical\n", + " \"mean\": 12,\n", + " \"std\": 2,\n", + " \"diff\": -2,\n", + " },\n", + " \"resprate_max\": { # numerical\n", + " \"mean\": 20,\n", + " \"std\": 2,\n", + " \"diff\": -4,\n", + " },\n", + " \"gcs_min\": { # discrete\n", + " \"mean\": 10,\n", + " \"std\": 2,\n", + " \"diff\": -7,\n", + " },\n", + " \"gcs_max\": { # discrete\n", + " \"mean\": 15,\n", + " \"std\": 2,\n", + " \"diff\": -10,\n", + " },\n", + " \"mechvent\": { # categorical\n", + " \"prob\": 0.8,\n", + " }\n", + "}\n", + "\n", + "def create_synthetic_dataset(n_samples, stats_dict, class_ratio):\n", + " sample = {}\n", + " n_pos, n_neg = int(n_samples * class_ratio), int(n_samples * (1-class_ratio))\n", + "\n", + " for key, value in stats_dict.items():\n", + " if key == \"mechvent\":\n", + " s_pos = np.random.choice([1, 0], n_pos, p=[value[\"prob\"], 1-value[\"prob\"]])\n", + " s_neg = np.random.choice([1, 0], n_neg, p=[1-value[\"prob\"], value[\"prob\"]])\n", + " sample[key] = np.concatenate([s_pos, s_neg])\n", + "\n", + " elif key == \"gcs_min\" or key == \"gcs_max\":\n", + " gcs_pos = np.random.normal(value[\"mean\"] + value[\"diff\"], value[\"std\"], n_pos)\n", + " gcs_neg = np.random.normal(value[\"mean\"], value[\"std\"], n_neg)\n", + " sample[key] = np.clip(np.floor(np.concatenate([gcs_pos, gcs_neg])), 3, 15)\n", + "\n", + " else:\n", + " s_pos = np.clip(np.random.normal(value[\"mean\"] + value[\"diff\"], value[\"std\"], size=n_pos), 0, None)\n", + " s_neg = np.clip(np.random.normal(value[\"mean\"], value[\"std\"], size=n_neg), 0, None)\n", + " sample[key] = np.concatenate([s_pos, s_neg])\n", + " \n", + " sample[\"mortality\"] = np.repeat([1], n_pos).tolist() + np.repeat([0], n_neg).tolist()\n", + " sample = pd.DataFrame(sample)\n", + "\n", + " # shuffle rows\n", + " sample = sample.sample(frac=1).reset_index(drop=True)\n", + " return sample\n", + "\n", + "np.random.seed(1)\n", + "df_sample = create_synthetic_dataset(5000, assumed_stats, 0.1)\n", + "print(f\"Number of columns: {len(df_sample.columns)}\")\n", + "print(f\"Class ratio: {len(df_sample[df_sample['mortality'] == 1]) / len(df_sample)*100:.2f}%\")\n", + "df_sample.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "tsne = TSNE(n_components=2, random_state=0)\n", + "X = df_sample.drop(columns=\"mortality\")\n", + "X = StandardScaler().fit_transform(X)\n", + "X_embedded = tsne.fit_transform(X)\n", + "\n", + "plt.figure(figsize=(5, 5))\n", + "plt.scatter(\n", + " X_embedded[df_sample[\"mortality\"] == 0, 0], \n", + " X_embedded[df_sample[\"mortality\"] == 0, 1], \n", + " color=\"blue\", \n", + " label=\"Survived\",\n", + " alpha=0.2,\n", + ")\n", + "plt.scatter(\n", + " X_embedded[df_sample[\"mortality\"] == 1, 0], \n", + " X_embedded[df_sample[\"mortality\"] == 1, 1], \n", + " color=\"red\", \n", + " label=\"Died\",\n", + " alpha=0.2,\n", + ")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train GroupFasterRisk with Group Sparsity Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mbp_minmbp_maxageurine_outputph_minph_maxpco2_minpco2_maxpo2_minpo2_maxheartrate_minheartrate_maxresprate_minresprate_maxgcs_mingcs_maxmechvent
213567.547025115.22906871.909501977.3861477.0255427.37950036.03558952.29974552.223609101.82895263.62634698.93968914.19159319.1953736.014.00
4125107.941496137.9571700.0000000.0000007.2237567.39809741.21152347.16453962.197140104.03675075.282804100.59788015.12115517.26419611.015.01
235474.597672171.25972519.268117870.6690417.3738497.48169631.08014549.02701655.96619683.71533661.90563083.78021610.76322419.4536368.012.01
155171.202324184.98359363.330171831.2898067.2645177.03050542.08683738.17039589.59835293.27371654.52617992.94650510.65317116.7550888.010.00
487366.27765374.58732094.071833239.1971677.2756427.06123127.58097059.72311067.41167497.99220156.333157108.7816079.82873519.5762887.015.00
\n", + "
" + ], + "text/plain": [ + " mbp_min mbp_max age urine_output ph_min ph_max \\\n", + "2135 67.547025 115.229068 71.909501 977.386147 7.025542 7.379500 \n", + "4125 107.941496 137.957170 0.000000 0.000000 7.223756 7.398097 \n", + "2354 74.597672 171.259725 19.268117 870.669041 7.373849 7.481696 \n", + "155 171.202324 184.983593 63.330171 831.289806 7.264517 7.030505 \n", + "4873 66.277653 74.587320 94.071833 239.197167 7.275642 7.061231 \n", + "\n", + " pco2_min pco2_max po2_min po2_max heartrate_min \\\n", + "2135 36.035589 52.299745 52.223609 101.828952 63.626346 \n", + "4125 41.211523 47.164539 62.197140 104.036750 75.282804 \n", + "2354 31.080145 49.027016 55.966196 83.715336 61.905630 \n", + "155 42.086837 38.170395 89.598352 93.273716 54.526179 \n", + "4873 27.580970 59.723110 67.411674 97.992201 56.333157 \n", + "\n", + " heartrate_max resprate_min resprate_max gcs_min gcs_max mechvent \n", + "2135 98.939689 14.191593 19.195373 6.0 14.0 0 \n", + "4125 100.597880 15.121155 17.264196 11.0 15.0 1 \n", + "2354 83.780216 10.763224 19.453636 8.0 12.0 1 \n", + "155 92.946505 10.653171 16.755088 8.0 10.0 0 \n", + "4873 108.781607 9.828735 19.576288 7.0 15.0 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from fasterrisk.wrapper import FasterRisk\n", + "from fasterrisk.binarization_util import BinBinarizer\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "X, y = df_sample.drop('mortality', axis=1), df_sample['mortality']\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y)\n", + "X_train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply binarization, `max_num_thresholds_per_feature` can be any (as long as it divides 100), `max_num_thresholds_per_feature` controls the width of the binarization intervals. For instance, if `max_num_thresholds_per_feature=10`, then each interval will be between nth and (n+10)th percentile\n", + "\n", + "If `group_sparsity=True`, returns group indices for every feature as well, else `None`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of binarized features: 162\n" + ] + }, + { + "data": { + "text/plain": [ + "['mbp_min<=79.58360205198792',\n", + " 'mbp_min<=86.15969780779426',\n", + " 'mbp_min<=91.47203697495456',\n", + " 'mbp_min<=102.29378361319388',\n", + " 'mbp_min<=116.10856618793571',\n", + " 'mbp_min<=118.85023577262267',\n", + " 'mbp_min<=128.3382531420785',\n", + " 'mbp_min<=132.8327508723682',\n", + " 'mbp_min<=159.96779440061323',\n", + " 'mbp_min<=187.5353513198002',\n", + " 'mbp_max<=71.22975257951565',\n", + " 'mbp_max<=124.35177097841765',\n", + " 'mbp_max<=128.8651400227865',\n", + " 'mbp_max<=154.38601218824647',\n", + " 'mbp_max<=157.42009528755486',\n", + " 'mbp_max<=159.5867464223987',\n", + " 'mbp_max<=162.83083014591665',\n", + " 'mbp_max<=185.18685735156615',\n", + " 'mbp_max<=188.40273979761903',\n", + " 'mbp_max<=189.70245573738563',\n", + " 'age<=33.51932478141815',\n", + " 'age<=37.05872798931772',\n", + " 'age<=42.503759561525634',\n", + " 'age<=43.413065024184725',\n", + " 'age<=54.66693302517876',\n", + " 'age<=55.09296084805071',\n", + " 'age<=57.32363590282395']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "binarizer = BinBinarizer(max_num_thresholds_per_feature=10, whether_interval=False, group_sparsity=True)\n", + "X_train_binarized, group_idx = binarizer.fit_transform(X_train) # binarize features based on training data\n", + "print(f\"Number of binarized features: {len(X_train_binarized.columns)}\")\n", + "list(X_train_binarized.columns[:27]) # 3*9=27 for the first three features" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,\n", + " 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5,\n", + " 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6,\n", + " 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8,\n", + " 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10,\n", + " 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n", + " 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13,\n", + " 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15,\n", + " 15, 15, 15, 15, 15, 15, 15, 16, 16])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "group_idx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Train GroupFasterRisk" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AUROC: 1.000, AUPRC: 1.000\n" + ] + } + ], + "source": [ + "from sklearn.metrics import roc_auc_score, precision_recall_curve, auc\n", + "fasterrisk = FasterRisk(\n", + " k=25, # the total number of binarized features to select\n", + " lb=-90, # lower bound of the coefficients\n", + " ub=90, # upper bound of the coefficients\n", + " group_sparsity=4, # the total number of groups (in our case, features) to select\n", + " featureIndex_to_groupIndex=group_idx, # the mapping from feature index to group index\n", + " gap_tolerance=50, # tolerance for the logistic loss between alternative models\n", + ")\n", + "fasterrisk.fit(X_train_binarized, y_train.replace({0:-1})) # replace 0 to -1 for binary classification\n", + "\n", + "X_test_binarized, _ = binarizer.transform(X_test) # binarize test data based on thresholds obtained from training data\n", + "y_prob = fasterrisk.predict_proba(X_test_binarized) # predict probability of death for test data\n", + "auroc = roc_auc_score(y_test, y_prob) # compute AUC\n", + "pre, rec, _ = precision_recall_curve(y_test, y_prob) # compute precision-recall curve\n", + "auprc = auc(rec, pre) # compute AUC of precision-recall curve\n", + "print(f\"AUROC: {auroc:.3f}, AUPRC: {auprc:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize card (`model_idx` specifies which model you want to visualize, `0` being the top model with smallest logistic loss, `1` being the second top one with smallest loss..etc)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display\n", + "from fasterrisk.binarization_util import BinBinarizer\n", + "names = list(X_train_binarized.columns)\n", + "for i in range(2):\n", + " card = fasterrisk.visualize_risk_card(names, X_train_binarized, model_idx=i, title=f'Risk Score Card for Top {i+1} Model') \n", + " display(card)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obtain model parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multipliers: [2.07672079 2.61517234 2.61501319 4.23148299 3.15323781 4.23104228\n", + " 2.07706743 2.07634628]\n", + "Intercept: [ -8. -10. -10. -16. -13. -16. -8. -8.]\n", + "Coefficients: [[0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "multipliers, beta0, betas = fasterrisk.get_model_params()\n", + "print(f\"Multipliers: {multipliers}\")\n", + "print(f\"Intercept: {beta0}\")\n", + "print(f\"Coefficients: {betas}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monotonicity Constraint\n", + "Monotonicity constraint can be particularly useful when one wants to include domain knowledge in the risk score output. This could matter in empirical scenarios where the bias in the training dataset contributes to unrealistic findings that may not generalize well. To demonstrate how this is useful, we would like to use the example below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example\n", + "Suppose there is a consistent data collection error for patients with Glascow Coma Score of 3, causing all patients with `gcs = 3` to be labelled as negatives (alive). " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "y_train_w = y_train.copy()\n", + "y_train_w[ (X[\"gcs_min\"] == 3) | (X[\"gcs_max\"] == 3) ] = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The GroupFasterRisk risk scores reflect this underlying consistent error, thanks to its interpretable risk score card:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AUROC: 0.696, AUPRC: 0.455\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fasterrisk = FasterRisk(\n", + " k=25, # the total number of binarized features to select\n", + " lb=-90, # lower bound of the coefficients\n", + " ub=90, # upper bound of the coefficients\n", + " group_sparsity=4, # the total number of groups (in our case, features) to select\n", + " featureIndex_to_groupIndex=group_idx, # the mapping from feature index to group index\n", + " gap_tolerance=50, # tolerance for the logistic loss between alternative models\n", + ")\n", + "fasterrisk.fit(X_train_binarized, y_train_w.replace({0:-1})) # replace 0 to -1 for binary classification\n", + "\n", + "X_test_binarized, _ = binarizer.transform(X_test) # binarize test data based on thresholds obtained from training data\n", + "y_prob = fasterrisk.predict_proba(X_test_binarized) # predict probability of death for test data\n", + "auroc = roc_auc_score(y_test, y_prob) # compute AUC\n", + "pre, rec, _ = precision_recall_curve(y_test, y_prob) # compute precision-recall curve\n", + "auprc = auc(rec, pre) # compute AUC of precision-recall curve\n", + "print(f\"AUROC: {auroc:.3f}, AUPRC: {auprc:.3f}\")\n", + "\n", + "card = fasterrisk.visualize_risk_card(names, X_train_binarized, model_idx=0, title=f'Risk Score Card') \n", + "display(card)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe that the component function for `gcs_max` generally has a negative correlation with risk (lower `gcs`, higher risk), except for `gcs_max <= 3`. We also have a similar observation for `gcs_min`. After consulting with relevant experts, we found that this is generally contradictory to the domain knowledge that `gcs <= 3` highly correlates with high mortality risk. To include such domain knowledge, we can apply monotonicity constraints on `gcs_min` and `gcs_max`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['gcs_min<=3.0',\n", + " 'gcs_min<=6.0',\n", + " 'gcs_min<=8.0',\n", + " 'gcs_min<=9.0',\n", + " 'gcs_min<=10.0',\n", + " 'gcs_min<=11.0',\n", + " 'gcs_min<=12.0',\n", + " 'gcs_min<=13.0',\n", + " 'gcs_min<=14.0',\n", + " 'gcs_min<=15.0',\n", + " 'gcs_max<=3.0',\n", + " 'gcs_max<=4.0',\n", + " 'gcs_max<=5.0',\n", + " 'gcs_max<=7.0',\n", + " 'gcs_max<=9.0',\n", + " 'gcs_max<=10.0',\n", + " 'gcs_max<=11.0',\n", + " 'gcs_max<=13.0',\n", + " 'gcs_max<=14.0',\n", + " 'gcs_max<=15.0']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[e for e in X_train_binarized.columns if \"gcs\" in e]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply monotonicity constraint" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# first entry is lower bound, second entry is upper bound, so setting to (0, 90) \n", + "# forces coefficient to be positive, which enforces monotonically decreasing relationship \n", + "# between GCS and mortality risk\n", + "user_defined_pairs = {e: (0, 90) for e in X_train_binarized.columns if \"gcs\" in e}\n", + "lb_list, ub_list = fasterrisk.define_bounds(X_train_binarized, feature_bound_pairs=user_defined_pairs, lb_else=-90, ub_else=90)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AUROC: 0.999, AUPRC: 0.984\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fasterrisk = FasterRisk(\n", + " k=25, # the total number of binarized features to select\n", + " lb=lb_list, # use lower bounds derived from domain knowledge\n", + " ub=ub_list, # use upper bounds derived from domain knowledge\n", + " group_sparsity=4, # the total number of groups (in our case, features) to select\n", + " featureIndex_to_groupIndex=group_idx, # the mapping from feature index to group index\n", + " gap_tolerance=50, # tolerance for the logistic loss between alternative models\n", + ")\n", + "fasterrisk.fit(X_train_binarized, y_train_w.replace({0:-1})) # replace 0 to -1 for binary classification\n", + "\n", + "X_test_binarized, _ = binarizer.transform(X_test) # binarize test data based on thresholds obtained from training data\n", + "y_prob = fasterrisk.predict_proba(X_test_binarized) # predict probability of death for test data\n", + "auroc = roc_auc_score(y_test, y_prob) # compute AUC\n", + "pre, rec, _ = precision_recall_curve(y_test, y_prob) # compute precision-recall curve\n", + "auprc = auc(rec, pre) # compute AUC of precision-recall curve\n", + "print(f\"AUROC: {auroc:.3f}, AUPRC: {auprc:.3f}\")\n", + "\n", + "card = fasterrisk.visualize_risk_card(names, X_train_binarized, model_idx=0, title=f'Risk Score Card') \n", + "display(card)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The corrected risk score more accurately reflects the domain knowledge and performs better! Although this is just a naive toy example, it demonstrates the core idea behind GroupFasterRisk's new functionalities." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "fasterrisk", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/added_functionalities_example.ipynb b/docs/added_functionalities_example.ipynb deleted file mode 100644 index 047e91b..0000000 --- a/docs/added_functionalities_example.ipynb +++ /dev/null @@ -1,948 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Additional Functionalities" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "data = pd.read_csv(\"MIMIC-WHOLE.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Newly added functionalities need those packages (for drawing risk score card):\n", - "* `Pillow==9.4.0`\n", - "* `matplotlib==3.7.0`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " preiculos age gcs_min mechvent urineoutput heartrate_min \\\n", - "0 4777.0 43.0 15.0 0 3980.0 68.0 \n", - "1 1.0 50.0 15.0 0 1050.0 77.0 \n", - "2 238.0 38.0 15.0 1 2000.0 101.0 \n", - "3 286.0 80.0 15.0 0 1145.0 46.0 \n", - "4 1.0 75.0 13.0 0 3040.0 67.0 \n", - "\n", - " heartrate_max meanbp_min meanbp_max resprate_min ... \\\n", - "0 118.0 77.000000 103.0 14.0 ... \n", - "1 120.0 56.000000 119.0 9.0 ... \n", - "2 116.0 56.666698 91.0 8.0 ... \n", - "3 76.0 51.000000 73.0 12.0 ... \n", - "4 92.0 69.000000 104.0 12.0 ... \n", - "\n", - " pao2fio2_vent_min admissiontype pao2_max pao2_min paco2_max paco2_min \\\n", - "0 NaN 2 NaN NaN NaN NaN \n", - "1 NaN 1 NaN NaN NaN NaN \n", - "2 NaN 0 458.0 139.0 52.0 34.0 \n", - "3 NaN 0 168.0 80.0 41.0 27.0 \n", - "4 NaN 1 NaN NaN NaN NaN \n", - "\n", - " ph_min ph_max aado2_min aado2_max \n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 7.21 7.47 284.0 284.0 \n", - "3 7.36 7.42 NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "\n", - "[5 rows x 49 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from fasterrisk.wrapper import FasterRisk\n", - "from fasterrisk.binarization_util import BinBinarizer\n", - "\n", - "X, y = data.drop('hospital_expire_flag', axis=1), data['hospital_expire_flag']\n", - "X.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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preiculosagegcs_minmechventurineoutputheartrate_minheartrate_maxmeanbp_minmeanbp_maxresprate_min...pao2fio2_vent_minadmissiontypepao2_maxpao2_minpaco2_maxpaco2_minph_minph_maxaado2_minaado2_max
268141089.086.014.00455.061.0102.064.0113.012.0...NaN2300.063.049.036.07.277.31NaNNaN
55891.040.015.014145.083.0121.078.0124.011.0...188.000000194.094.038.038.07.387.38NaNNaN
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5 rows × 49 columns

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" - ], - "text/plain": [ - " preiculos age gcs_min mechvent urineoutput heartrate_min \\\n", - "26814 1089.0 86.0 14.0 0 455.0 61.0 \n", - "5589 1.0 40.0 15.0 1 4145.0 83.0 \n", - "17447 175.0 66.0 15.0 1 750.0 71.0 \n", - "11968 1.0 22.0 15.0 0 500.0 92.0 \n", - "29616 576.0 64.0 15.0 1 1140.0 73.0 \n", - "\n", - " heartrate_max meanbp_min meanbp_max resprate_min ... \\\n", - "26814 102.0 64.0 113.0 12.0 ... \n", - "5589 121.0 78.0 124.0 11.0 ... \n", - "17447 114.0 51.0 84.0 10.0 ... \n", - "11968 116.0 56.0 89.0 16.0 ... \n", - "29616 95.0 61.0 98.0 13.0 ... \n", - "\n", - " pao2fio2_vent_min admissiontype pao2_max pao2_min paco2_max \\\n", - "26814 NaN 2 300.0 63.0 49.0 \n", - "5589 188.000000 1 94.0 94.0 38.0 \n", - "17447 242.499996 0 469.0 97.0 47.0 \n", - "11968 NaN 1 NaN NaN NaN \n", - "29616 209.999997 0 389.0 84.0 54.0 \n", - "\n", - " paco2_min ph_min ph_max aado2_min aado2_max \n", - "26814 36.0 7.27 7.31 NaN NaN \n", - "5589 38.0 7.38 7.38 NaN NaN \n", - "17447 39.0 7.32 7.38 NaN NaN \n", - "11968 NaN NaN NaN NaN NaN \n", - "29616 37.0 7.25 7.38 562.0 562.0 \n", - "\n", - "[5 rows x 49 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y)\n", - "X_train.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Apply binarization, `interval_width` can be any (as long as it divides 100)\n", - "\n", - "If `group_sparsity=True`, returns group indices for every feature as well, else `None`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "492\n" - ] - }, - { - "data": { - "text/plain": [ - "['preiculos_isNaN',\n", - " 'preiculos<=348.0',\n", - " 'preiculos<=906.0',\n", - " 'preiculos<=1502.0',\n", - " 'preiculos<=2403.0',\n", - " 'preiculos<=3306.0',\n", - " 'preiculos<=4451.0',\n", - " 'preiculos<=6050.0',\n", - " 'preiculos<=8405.0',\n", - " 'preiculos<=12933.0',\n", - " 'preiculos<=152477.0',\n", - " 'age_isNaN',\n", - " 'age<=22.0',\n", - " 'age<=30.0',\n", - " 'age<=37.0',\n", - " 'age<=45.0',\n", - " 'age<=52.0',\n", - " 'age<=59.0',\n", - " 'age<=67.0',\n", - " 'age<=74.0',\n", - " 'age<=82.0',\n", - " 'age<=89.0',\n", - " 'gcs_min_isNaN',\n", - " 'gcs_min<=3.0',\n", - " 'gcs_min<=5.0',\n", - " 'gcs_min<=6.0',\n", - " 'gcs_min<=7.0',\n", - " 'gcs_min<=9.0',\n", - " 'gcs_min<=10.0',\n", - " 'gcs_min<=11.0',\n", - " 'gcs_min<=12.0',\n", - " 'gcs_min<=14.0',\n", - " 'gcs_min<=15.0']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "binarizer = BinBinarizer(interval_width=10, whether_interval=False, group_sparsity=True)\n", - "X_train_binarized, group_idx = binarizer.fit_transform(X_train) # binarize features based on training data\n", - "print(len(X_train_binarized.columns))\n", - "list(X_train_binarized.columns)[:33] # note that NaN is created as an additional column for every feature, in case there is missing value" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n", - " 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5,\n", - " 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n", - " 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8,\n", - " 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n", - " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11,\n", - " 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13,\n", - " 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14,\n", - " 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16,\n", - " 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17,\n", - " 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19,\n", - " 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n", - " 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22,\n", - " 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n", - " 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25,\n", - " 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,\n", - " 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28,\n", - " 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30,\n", - " 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31,\n", - " 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33,\n", - " 33, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 34,\n", - " 34, 34, 34, 35, 35, 35, 36, 36, 36, 37, 37, 37, 38, 38, 38, 39, 39,\n", - " 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 41, 41, 41, 41,\n", - " 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42,\n", - " 42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 44, 44, 44, 44, 44,\n", - " 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n", - " 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 47,\n", - " 47, 47, 47, 47, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "group_idx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Train FasterRisk" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8050932293201706" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics import roc_auc_score\n", - "fasterrisk = FasterRisk(k=25, lb=-90, ub=90, group_sparsity=10, featureIndex_to_groupIndex=group_idx)\n", - "fasterrisk.fit(X_train_binarized[:5000], y_train.iloc[:5000]) # fit on 5000 patients for demo purpose\n", - "\n", - "X_test_binarized, _ = binarizer.transform(X_test) # binarize test data based on thresholds obtained from training data\n", - "y_prob = fasterrisk.predict_proba(X_test_binarized) # predict probability of death\n", - "roc_auc_score(y_test, y_prob) # compute AUC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualize card (`model_idx` specifies which model you want to visualize, `0` being the top model with smallest logistic loss, `1` being the second top one with smallest loss..etc)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display\n", - "names = list(X_train_binarized.columns)\n", - "for i in range(3):\n", - " card = fasterrisk.visualize_risk_card(names, X_train_binarized, model_idx=i, title=f'Risk Score Card for Top {i+1} Model') \n", - " display(card)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Obtain model parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50, 50, 50)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multipliers, beta0, betas = fasterrisk.get_model_params()\n", - "len(multipliers), len(beta0), len(betas) # number of models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get card in printed format" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================== Model 1 ==============================\n", - "The Risk Score is:\n", - "1. age<=59.0 -3 point(s) | ...\n", - "2. age<=74.0 -2 point(s) | + ...\n", - "3. gcs_min<=3.0 -5 point(s) | + ...\n", - "4. gcs_min<=7.0 8 point(s) | + ...\n", - "5. mechvent<=0 -8 point(s) | + ...\n", - "6. urineoutput<=461.0 4 point(s) | + ...\n", - "7. urineoutput<=1218.0 3 point(s) | + ...\n", - "8. urineoutput<=1602.0 3 point(s) | + ...\n", - "9. urineoutput<=5305.0 -6 point(s) | + ...\n", - "10. resprate_min<=8.800000190734863 -2 point(s) | + ...\n", - "11. resprate_min<=14.0 -4 point(s) | + ...\n", - "12. resprate_min<=18.0 -2 point(s) | + ...\n", - "13. resprate_max<=15.0 11 point(s) | + ...\n", - "14. resprate_max<=27.0 -2 point(s) | + ...\n", - "15. resprate_max<=33.0 -3 point(s) | + ...\n", - "16. bun_min<=16.0 -4 point(s) | + ...\n", - "17. bun_min<=32.0 -2 point(s) | + ...\n", - "18. bun_min<=64.0 -4 point(s) | + ...\n", - "19. bun_min<=232.0 42 point(s) | + ...\n", - "20. albumin_min<=1.9 4 point(s) | + ...\n", - "21. albumin_min<=5.2 3 point(s) | + ...\n", - "22. electivesurgery<=0 7 point(s) | + ...\n", - "23. paco2_max<=25.0 5 point(s) | + ...\n", - "24. paco2_max<=40.0 4 point(s) | + ...\n", - "25. paco2_max<=69.0 -2 point(s) | + ...\n", - " SCORE | = \n", - "SCORE | -28.0 | -24.0 | -21.0 | -18.0 | -15.0 | -12.0 | -9.0 | -6.0 | -2.0 | 2.0 | 5.0 | 8.0 | 11.0 | 14.0 | 17.0 |\n", - "RISK | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.1% | 0.1% | 0.3% | 0.4% | 0.7% | 1.1% | 1.7% | 2.7% |\n", - "SCORE | 19.0 | 22.0 | 25.0 | 28.0 | 31.0 | 34.0 | 37.0 | 40.0 | 43.0 | 46.0 | 49.0 | 52.0 | 55.0 | 58.0 | 64.0 |\n", - "RISK | 3.7% | 5.7% | 8.8% | 13.4% | 19.8% | 28.2% | 38.5% | 50.0% | 61.5% | 71.8% | 80.2% | 86.6% | 91.2% | 94.3% | 97.7% |\n", - "\n", - "\n", - "============================== Model 2 ==============================\n", - "The Risk Score is:\n", - "1. age<=59.0 -4 point(s) | ...\n", - "2. age<=74.0 -2 point(s) | + ...\n", - "3. gcs_min<=3.0 -6 point(s) | + ...\n", - "4. gcs_min<=7.0 10 point(s) | + ...\n", - "5. mechvent<=0 -9 point(s) | + ...\n", - "6. urineoutput<=461.0 5 point(s) | + ...\n", - "7. urineoutput<=1218.0 3 point(s) | + ...\n", - "8. urineoutput<=1602.0 4 point(s) | + ...\n", - "9. urineoutput<=5305.0 -8 point(s) | + ...\n", - "10. resprate_min<=8.800000190734863 -3 point(s) | + ...\n", - "11. resprate_min<=14.0 -5 point(s) | + ...\n", - "12. resprate_min<=18.0 -2 point(s) | + ...\n", - "13. resprate_max<=15.0 14 point(s) | + ...\n", - "14. resprate_max<=27.0 -3 point(s) | + ...\n", - "15. resprate_max<=33.0 -3 point(s) | + ...\n", - "16. bun_min<=16.0 -5 point(s) | + ...\n", - "17. bun_min<=32.0 -2 point(s) | + ...\n", - "18. bun_min<=64.0 -5 point(s) | + ...\n", - "19. bun_min<=232.0 50 point(s) | + ...\n", - "20. albumin_min<=1.9 4 point(s) | + ...\n", - "21. albumin_min<=3.3 2 point(s) | + ...\n", - "22. albumin_min<=5.2 3 point(s) | + ...\n", - "23. electivesurgery<=0 9 point(s) | + ...\n", - "24. paco2_max<=25.0 5 point(s) | + ...\n", - "25. paco2_max<=40.0 4 point(s) | + ...\n", - " SCORE | = \n", - "SCORE | -32.0 | -29.0 | -23.0 | -20.0 | -16.0 | -13.0 | -9.0 | -5.0 | -2.0 | 3.0 | 6.0 | 10.0 | 13.0 | 17.0 | 20.0 |\n", - "RISK | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.1% | 0.1% | 0.1% | 0.2% | 0.3% | 0.6% | 0.8% | 1.3% | 1.9% |\n", - "SCORE | 24.0 | 28.0 | 31.0 | 35.0 | 38.0 | 42.0 | 45.0 | 49.0 | 53.0 | 56.0 | 60.0 | 63.0 | 67.0 | 70.0 | 78.0 |\n", - "RISK | 3.2% | 5.2% | 7.4% | 11.7% | 16.2% | 24.3% | 31.9% | 43.7% | 56.3% | 65.3% | 75.7% | 82.0% | 88.3% | 91.7% | 96.8% |\n", - "\n", - "\n", - "============================== Model 3 ==============================\n", - "The Risk Score is:\n", - "1. age<=59.0 -3 point(s) | ...\n", - "2. age<=74.0 -2 point(s) | + ...\n", - "3. gcs_min<=3.0 -5 point(s) | + ...\n", - "4. gcs_min<=7.0 8 point(s) | + ...\n", - "5. mechvent<=0 -7 point(s) | + ...\n", - "6. urineoutput<=461.0 4 point(s) | + ...\n", - "7. urineoutput<=1218.0 2 point(s) | + ...\n", - "8. urineoutput<=1602.0 4 point(s) | + ...\n", - "9. urineoutput<=3025.0 -2 point(s) | + ...\n", - "10. urineoutput<=5305.0 -5 point(s) | + ...\n", - "11. resprate_min<=8.800000190734863 -2 point(s) | + ...\n", - "12. resprate_min<=14.0 -4 point(s) | + ...\n", - "13. resprate_min<=18.0 -2 point(s) | + ...\n", - "14. resprate_max<=15.0 11 point(s) | + ...\n", - "15. resprate_max<=27.0 -2 point(s) | + ...\n", - "16. resprate_max<=33.0 -3 point(s) | + ...\n", - "17. bun_min<=16.0 -4 point(s) | + ...\n", - "18. bun_min<=32.0 -2 point(s) | + ...\n", - "19. bun_min<=64.0 -4 point(s) | + ...\n", - "20. bun_min<=232.0 42 point(s) | + ...\n", - "21. albumin_min<=1.9 4 point(s) | + ...\n", - "22. albumin_min<=5.2 3 point(s) | + ...\n", - "23. electivesurgery<=0 8 point(s) | + ...\n", - "24. paco2_max<=25.0 4 point(s) | + ...\n", - "25. paco2_max<=40.0 3 point(s) | + ...\n", - " SCORE | = \n", - "SCORE | -27.0 | -24.0 | -21.0 | -18.0 | -15.0 | -12.0 | -9.0 | -5.0 | -2.0 | 1.0 | 4.0 | 7.0 | 10.0 | 13.0 | 16.0 |\n", - "RISK | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.1% | 0.1% | 0.2% | 0.3% | 0.4% | 0.7% | 1.1% | 1.7% |\n", - "SCORE | 19.0 | 22.0 | 25.0 | 28.0 | 31.0 | 34.0 | 37.0 | 41.0 | 44.0 | 47.0 | 50.0 | 53.0 | 56.0 | 59.0 | 64.0 |\n", - "RISK | 2.7% | 4.3% | 6.6% | 10.2% | 15.3% | 22.4% | 31.5% | 46.1% | 57.7% | 68.5% | 77.6% | 84.7% | 89.8% | 93.4% | 96.8% |\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for i in range(3):\n", - " print(f\"{'='*30} Model {i+1} {'='*30}\")\n", - " fasterrisk.print_risk_card(names, X_train_binarized, model_idx=i) # print risk card for the top model (with smallest logistic loss)\n", - " print(\"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Further modify card with user-defined box constraints (to include domain knowledge)\n", - "**Example**:\n", - "Due to issue with training data, we observe that `gcs_min` gets lower risk when patient has a score with 3, which doesn't make sense. Let us try to change by adding user-defined constraints. In addition, those constraints can also be used to enforce monotonicity in component scores, in particular:\n", - "- **monotonically decreasing**: set all box constraints for a particular feature **positive**\n", - "- **monotonically increasing**: set all box constraints for a particular feature **negative**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, since `gcs_min<=3.0` is the outlier due to problems with training data, we enforce its box constraint to be positive" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "user_defined_pairs = {\n", - " \"gcs_min<=3.0\": (0, 90) # first is lb, second is ub, setting to (0, 90) forces coefficient for gcs_min<=3.0 to be positive, thus must have higher score than gcs_min>3.0 if feature gets selected\n", - "}\n", - "lb_list, ub_list = fasterrisk.define_bounds(X_train_binarized, feature_bound_pairs=user_defined_pairs, lb_else=-90, ub_else=90)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8059317294489612" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fasterrisk = FasterRisk(k=25, lb=lb_list, ub=ub_list, group_sparsity=10, featureIndex_to_groupIndex=group_idx) # this time we pass a list of lb and ub\n", - "fasterrisk.fit(X_train_binarized[:5000], y_train.iloc[:5000]) # fit on 5000 patients for demo purpose\n", - "\n", - "X_test_binarized, _ = binarizer.transform(X_test) # binarize test data based on thresholds obtained from training data\n", - "y_prob = fasterrisk.predict_proba(X_test_binarized) # predict probability of death\n", - "roc_auc_score(y_test, y_prob) # compute AUC" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Hra2tT506JYRo3rx5y5Yts9h/3ryxzEvZ/qPrwvUOZEXdunXr1q17586dxYsXP3z4cMuWLS9evJg/f7624/oErk0AAAAA+oaUJgAAoL8MDAzatGnTpk2bCxcubN26NTAw8OLFixcvXnR3dx8wYECzZs2y8h1uRlQq1ezZs6WBBFq0aNGrV6/P2rxChQrLli0bMGBASkrKyZMnJ0+eLM1fkwkzMzOpkO7jOknaH9pmbysUMPp5ul66dOnNmzdp6zt27Jj5tb9u3bqXL1+amZnNnDkzi/8lNH6h6cIBRL7A6SpxcnIyNjZOSkqKjo5euXLly5cvO3fuLGfP5ESqqdPUyWMIpfuv5s2bNz4+PtevX3/69GlkZGRcXJz8VDit0NBQaW2qGe7S9fr1a+nIZN5YHrgo3RncZDm8QWQko+Mmn6UZDfgnN0j3cH3WUVXvc+bMmQMHDkxISPDy8hJC2NraTp06NSs7kqqf3HtjmZdy8kfXhesdyIqUlBQ/P789e/Y8fPhQqsnk/7mO4NoEAAAAoIdIaQIAABANGzZs2LDhrVu3tm7devHixXv37k2fPt3FxaV///4dO3aUf9n/WX777TdfX18hROXKlWfNmpWNHlxdXRs1anTmzBmVSnX9+vUWLVpk3t7CwkIqxMXFZdTm48ePUsHa2jonW6GA0c/T1cvL69KlS2nr27Ztm8kjmVu3bu3evVsIMW7cOBcXl8xfQqbxC00XDiB0H6errFGjRgcPHvznn3/27t0bFha2du3ajRs3tmrVqmfPnp8c9ChzlpaWGa2So5LjlG3atGnLli3yY+BP+qwDLk2a9snG8vxKHz58yKRZzm8Q6TI3N8+8QTYejX/uUVVXokSJUaNGrV69WlqcPHlyTjLecuONZV7KyR9dF653IHPh4eH79+/fv39/eHi4EMLExKRNmzZ9+/bV/ZkiuTYBAAAA6CFSmgAAAP6f6tWr//bbb48fP962bZuPj09ISMiSJUs2btw4bty4Tp06fVZXf/75p/QU2dXVddmyZdn+xaq7u/uZM2eEEG/fvv1kY/mHxZk0lr64F0I4ODjkZCsUJJyuWZeQkCDN4VWzZs2ePXtmfUONR55PDyDyEqdrKnZ2diNHjhwyZMiJEyd27dr18OHDI0eOHDlyxN3dvUePHq1btzY2Nv5kJ2klJydntColJUUqpJrkbt26dVu3bpXKLi4utWvXLl68uIWFhRRAQEDA8ePHU3Ulz+aTlfny5MaZzwEkD19kaGiYURtN3SDS0vh4Rdk4qqncvXtXvdy6deschqTBN5Z5KYd/dB253oF03bhxY8+ePX5+ftL/ZwcHh+7du/fo0UMjg/blNq5NAAAAAPqJlCYAAID/4ebmNnv27DFjxvz999+HDh2KjIy8ePHiZz158vb2/vPPP4UQzs7OK1euzMlX5PLvYjN/KikpUaKEVAgJCcmojbyqZMmSOdkKBYY+n67Lly//ZJyp+Pv7SzM03bhxo379+pm0nDFjxowZM4QQLVu2nDdvnsYvNF04gNBxnK7pMjY27tChQ4cOHQICAry8vC5cuHDv3r179+6tWrWqU6dOPXr0cHZ2zmJXkrQjMMmioqKkgpWVlVwZFha2fft2qTxlypSePXumylJKSEhIm3wj/4ONiYn5ZEjyOBlyAJmHJw/XlIoGbxC5LXtHVd3x48dPnz4thDA0NExJSfH29m7SpEmtWrVyHlvO31jmpZz/0XXqegckcXFxR48e3bNnT3BwsFRTvnz5vn37tmrVKnvJrHmPaxMAAACA3tLwr+IAAAAKhmLFik2dOnX//v2DBw/+rCH0Dx8+/PvvvwshHB0d16xZU7hw4ZyEERERIRXs7e0/2djS0rJUqVJCiAcPHmTU5t69e1KhfPnyOdkKBQOn6+eSBzX5XBqPPJ8eQOQlTtfMffHFF0uWLNm9e3evXr3Mzc0jIyO3b9/evXv377//PjAwMOv9vHz5MqNVjx49kgrqaVL+/v7Sn6ZZs2a9evVKO+pSbGxs2q7kHp48efLJkIoWLSo9pH/69GkmOabyo335ibU6zd4gclv2jqosPDx82bJlQggrK6s1a9aYm5urVKq5c+dmvtVnyfYby7ykkT+6bl7v0FuvX79eunRphw4dFi9eHBwcbGBg8OWXX65bt27btm3t27fPL/lMXJsAAAAA9BmjNAEAAGTI3t5+7NixWRlyRnLmzJn58+erVCpbW9vVq1e7uLjkMIAbN25IBXd396y09/DwePbsWURExH///VepUqVUa5OSki5duiSEsLGxKVu2bA63Qn7H6ZoNxYoV69y5cyYNnj17duvWLSHEF198IR1S+WhoPPL8eACRlzhds8LFxWXKlCljxow5ePDg7t27X716de7cOSMjo2rVqmWxh3v37iUlJaX7XFwKTwhRsWJFuVKeoCejEYAeP36cttLc3LxMmTLBwcGRkZEPHjyoUKFCJiFJ8QcEBERHR9+6datGjRrpNrtw4YJUSLuzGr9B5LbsHVXZokWLPnz4IISYOHFijRo1vvrqq99///3Vq1fLly//6aefNBjn576xzEsa/KPr7PUOPXThwoU9e/YIISwsLDp27NinTx/d/4eWCtcmAAAAAD1HShMAANBHL168kNMvPqlo0aJ16tT5ZLOrV6/+/PPPSqXS2tp61apVrq6uOQpRiICAAClINze3dEdQSKtdu3a7du0SQuzZs0eaRUjd0aNHo6OjhRAtWrQwMDDI4VbI1zhds6dixYqZP94+ePCglCPStWvXVq1aqa/SeOT58QAiL3G6Zp2VlVX//v379u3r5+fn5eWVdoyfTMTHxx8/frxjx46p6iMjIw8ePCiVmzZtKtebmprKDdL29vr16zNnzqT7Qs2aNZPGVdq2bdv8+fMzj6p9+/YBAQFS43RTmnx9faV5CatWrZrqn7bGbxB5INtHVQhx6NCh8+fPCyHq1asnZQH26tXr5MmTt2/fPnjwYJMmTRo2bPjJAHLjjWVe0uwfXcevd+ibYsWK9erVq3PnzpaWltqO5bNxbQIAAAAAKU0AAEAfBQYGLliwIIuNmzVr9sknT3fv3p06dWpSUpKFhcWKFSvKlSuXlZ6nTJkyYMCAGjVqpP3+99y5c3PmzJHKX331lfqqoKCgGTNmhIWFtWvXburUqeqrKlasWKdOnatXrx45cqR27drt2rWTVz148GD16tVCCENDw379+uV8K+RfnK5akb3IOYDQCv08XQ0MDJo3b968efP3799/1oYrV64sX768+oQ7MTExU6dOjYuLE0LUrFlTfZU8iMXRo0f79u2rPgfZs2fPpk2blpCQkO6r9OzZc9euXTExMb6+vuXKlRsyZEiq1Ku7d++6u7tLle3atdu3b9+dO3cuXry4fv360aNHq//rvnPnzqJFi6TyiBEjUnWSjRuE1mX7qL5+/Xr58uVCCAsLix9//FGqNDAw+PnnnwcOHJiUlLRw4cIdO3YUKlQo8wA0/sYyL2Xvj57fr3foiebNm3ft2jWfptpwbQIAAACAIKUJAAAg54KDg7/99lvpyWWzZs2CgoKCgoIyaly8eHF5VhR/f/8LFy7Y29tXq1atbNmytra2KpUqLCzs8uXLcg+DBg1q3Lixeg87d+588uSJEGLv3r3dunVL9e32tGnTBg8e/PHjx9mzZx87dqx27drGxsYPHjw4efJkUlKSEGLkyJFpB9HJ3lbIjzhdtSgbkXMAoS36fLra29tnvXG1atUCAwOHDRvWqFGjKlWqmJmZhYaGHj16VBouyMLCYtq0aert69SpU6RIkdevX4eGhvbt27d9+/bOzs4fP34MDAz09/dXqVRjxoz5448/0r6QnZ3djz/++PPPP6tUqvXr1/v6+jZu3NjR0TEhISEkJOTKlSvPnz+/cOGCoaGhEMLAwGDmzJkjR4788OHDli1b/Pz8mjRpUrhw4djY2MDAwAsXLiiVSiHEwIEDPTw85JfI9g1C67J9VOfPnx8TEyOEmDBhgrOzs1xfqlSpESNGrF+/Pjw8fMmSJXPnzs27nclb2f6jF5jrHQWbra2ttkPIJq5NAAAAAJCQ0gQAAPRRhw4dOnTooKnezp8/L423L4Q4fPjw4cOHM2ncpk0b+RtnCwuLmJiY9+/f+/n5+fn5pWppbm4+bty4nj17pqpXH5Uh7W+OXVxcli5dOn369Pfv31++fPny5cvqa/v37z9s2LC0UWVvK+RHnK5alI3IOYDQFk7XLGrRokWLFi1WrVp15syZVLOb2dvbL1mypFSpUuqVxsbG8+bNmzx5cnR09Lt377Zv3y6vMjU1/emnn1q1arVly5Z0RxVq0aKFEGLRokXR0dGPHj169OhRJoGVLFly8+bN33333ZMnT54+ffr06dNUYYwYMWLo0KHqldm+QWhd9o7q3r17r1y5IoSoXbt2165dU/U5aNCgU6dOPXz40MfHp2nTptLBz4hm31jmpWz/0fXzegfyDNcmAAAAAEhIaQIAANCa/fv3nzx58urVq0+fPn3z5k1sbKyRkZGNjY2bm1udOnU6deqkPnOKrH///vfu3Xv16lWHDh3c3NzSNqhZs6anp+e+ffv8/PxevXoVHx/v4OBQrVq1Hj16VK9ePaNgsrcV9Aenq0Z8buQcQGgRp2tWfPz4cfjw4bVr1/b29r527Vp4eLiRkVHx4sWbNGnSu3dvKyurtJtUrVrV09PT09Pz4sWLYWFhxsbGjo6OdevW7dWrl5T/VLZs2bt376b7ci1atKhbt+6JEydOnTr18uXLd+/eGRoaSuPntWzZUhqiSebi4rJt27YjR474+vo+fPgwOjrawsKiaNGi9erV69atW7FixXLjgGjL5x7V0NDQVatWCSHMzc2nT5+eahY/IYShoeHPP/88bNiwlJSUxYsX16hRw8HBIS/3SMfp5/UO6D6uTQAAAAAFjEKlUmk7BgAAoEkKheLSpUvajgLQXx4eHlyDgBZxDSIPXLp06dtvvxVCDBky5Ouvv9Z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7d+44WciVK1d0r7p9+3ZGv4juFj+FCxe+fPmyZA725s+fr3tr+aW1a9euhMa7777rZOa0tLQZM2aonRYOBQYGvvfee3FxcfLjv3r1ao8ePZwsUwjRqVOnc+fOZbooXSNOSEiIzWbbvXu3bszK9Izs2LGjatWqTgaTL1++sWPHJiUlyf+Ozj3yyCPa5b/66quGXr5u3TrdCE+ePOn8JatWrSpXrpyT39HHx+e5556LiopyvpysB37ixAndBdi0ihQpsmTJEpvN9uGHH2qnd+vWzVBEOj4+Pk5+d60mTZo4X5TZ1l7X2PfALVu2LCsLdGMsutKqfPSxsbF9+vRxsvAuXbpcu3YtK79CViQkJOjG8++//2Z9sbpvz6FDh7711lvaKWPHjpVclO6CfDVr1ty0aZN2SufOnWWW48YPWitby4K7KrwLOxsAAAAAgFyNqzQBAAAAADJkfxEjJ01I8rZs2VK3bt0VK1bYbDYnbz179ux69eodO3Ysi2+XkpLSu3fv+Ph4dYqXl9ecOXPKli3r8jJLlSqlm7Jr1y7J165bt+6Gxvjx4zOaMy4urnXr1v37979165aTBSYmJo4fP7558+b2t8NzaOXKlVWrVv3tt9+cz7Zq1aoaNWrMnTtXZpmq5OTkM2fOtG7dWjfm9PT0jF6iXKnLvn9OKz4+/sMPP2zRokVcXJyh8TiUkpJy5MgR7ZRMb2ulY7/yOLlWVmJi4iuvvNKpU6dLly45WWZaWtr8+fPr1q0rc+EuldHA169fX7du3T179mS0wKioqO7du0+cOFF+DDnJbGuvSWRrLPfu3YuJiWnRosWcOXOczLZs2bI6deqcO3fO0MLdRbcBent7lyhRwu3vkpSU9Mwzz2inyN97bunSpdqnzzzzTGJiotEBZNMHna1lIecrPAAAAAAgz6ClCQAAAACQIft7wUjeXs2Jffv2PfXUU1FRUTIzX7hwoX379hEREVl5x48++kh3U6T33nuvY8eOWVlmrVq1dN1d33//fVYWaC82NrZNmzbbtm2TnP/AgQOtW7e+f/++89mWLl3arVs3yT8bJyUl9e3bd/r06ZJjUF7y/PPP2w8jo/a1Tz755NNPP3XS3Ka1a9eujh07pqSkyI/HoUuXLiUlJWmnVK9e3dASHnzwwbX/lVFTlM1m69u378yZMyWXfPny5aeeeirTz1FlKPD9+/d369ZN97s7NGzYsPXr10uOIceYbe01ieyO5e7du88995zMreVu3br1+OOPS5Z399I1lVavXl13ZT63SExMbNKkSenSpdUp+/fvt79eoEO65icXWpqy6YPO1rLgkQoPAAAAAMgzfD09AAAAAACAee3fv1/71MvLq3z58llZYHJycq9evXQdA2FhYU888cQDDzyQlJS0f//+DRs2pKWlqT+9du1anz59Nm/e7No7bt++/auvvtJOadq06Weffeba0lSFCxdu2LDh7t27tW80derUQYMGZXHJqnfffdf+mhkPPfRQ8+bNS5YsmZSUdPz48Q0bNmivPnXixIlBgwbNnj07o2UeOXKkZ8+euj8Y58uXr2PHjpUrV/by8jp79uyqVau0H5DNZhswYECVKlVatGghM2ybzbZv3z776Q6vc7NmzZpRo0bpJtapU6d58+bFixePjo7es2fPn3/+qX3tjh07vvjii48//lhmMBm5d++ebkpYWJihJfj7+7dv315mzo8++kh3SRUfH5/WrVvXr1+/QIECd+7c2bNnz86dO7V/8j9x4sTkyZNHjBghs3z5wNPS0l588UX7fp0iRYo88sgjxYsXj4qKOn78+MWLF5WX6zb/LDpw4ID6O3bp0kV5F8W4ceO095MqUKCAwyWYbe01iRyI5dChQwcPHlSXXK9evfLly9+/f//ff//V3UxNCHHhwoXRo0dPmTIlS7+VcT///LP2adeuXbPjXRITE728vLp166b9BZctW6a7G529c+fOHT9+XH1as2bNqlWrqqnKyKYPOlvLgqcqPAAAAAAg78j5e90BAAAAAHLYDz/8oD0S9PHxkXzhY489pn1h5cqVHc6mbUcQQnzxxRcZLXDSpEm6w9IRI0YkJydr5zl69GilSpV0s61fv95+afbXxrh9+7Z2hrt37+puEFa0aNGrV69K/vrO2XcOeXt7f/PNN25Z+J9//qm7ClSVKlX+/PNP3WwRERFt27bVDePIkSMOl5mcnFynTh3dzH369Ll37552ttjY2Ndff103W7ly5WJiYhwu9sKFC8IRPz+/3r17z5kzZ8OGDYsXL54xY4buhQkJCRUqVNC+pGzZslu3btXNtm/fPl0jXYECBSIjI+XDtLdx40bdaC9evJiVBWbkxIkTPj4+2jeqXbv2yZMndbPt3btX11NVrFixtLQ0h8t0OfBp06bpXlK0aNH58+enpKSo86Snp//xxx+6z0XRrVs3d8VSpUoV7ZIXLFiQ6UvMtvZmXWxsrO5Nly1bZnQh2RSLw8sOeXl5vffee7pN7+LFi927d7dP79y5c0Z/l6xYsWKFdgCBgYHuqvO6b09lK9ixY4d2YosWLTJdzpdffql9ySeffGKz2XQXb+vcuXNGL8+mD9qWnWUhOyq8/M4GAAAAACBvoKUJAAAAAPI+11qa/vrrL90fL4cOHepwTvm/Mj766KPaOV944QWHs126dKlw4cLaOXv37m0/W6YtTc8++6z2p15eXuvWrZP53WWkpKTUrFnT/k+87dq1O3PmTBYX3qFDB+0yK1SocOPGDYdzJiUlNWjQINOsbDbb5MmTJT9Qm832ySef6Gb+6KOPHM7psCmkTJkyhw4dcv47TpgwQfuS0NBQ+14fxdmzZ3X3kPr666+dL9y5xYsX6wacxR6pjOjaC8LDwzN6I/s7DO7fv9/hnC4HrmskKliw4OnTpx3Oef36dft127MtTWZbe7POLS1N2RSLfWn18vL65ZdfMlrykCFDdPN//PHHRn8Xl23evFl3R9SRI0e6a+G6b8+OHTvabLa0tLRSpUqpE318fHRfPfYaNWqkXY5S67777jvtRCctTdn0QduysyxkR4WnpQkAAAAArMZbAAAAAABgJzo6um/fvrqJzz//fFaWmZqaqruT2tChQx3OGR4ePnz4cO2UDRs2GH27OXPmLFq0SDvlgw8+0P1BNCt8fX3nzp0bFBSkm75+/frq1av37dv38OHDri352rVr69ev106ZPHlyiRIlHM7s7+///fffa6f88ccfqamp9nPqZmvYsOHXX3+d0Rg++uijJ554QjtlxowZycnJmQ5eCBEYGLhmzRr7a4ro6C5zNWLEiKpVqzqc88EHH3zzzTe1U+x7kgxJTEzUTQkICMjKAjOycuVK7dMPP/ywSJEiDuds3rx55cqVtVMc3gHNIZnAjx49evr0ae2Ur7/+WveOqpIlSy5atCibMnGN2dZek8ixWF577bU+ffpk9NNvvvmmevXq2ilLliyRWazL0tLSbt26tWbNmueee65t27bR0dHqjxo2bJh9ty1TSoe3t3e3bt20g9Ft6TrXr1/Xfvc99NBDSq1LSkqSfN9s+qCztSx4sMIDAAAAAPIMWpoAAAAAAHqXLl1q3rz5uXPntBM7duz48MMPZ2WxN27cSE9P107R/RVc6+mnn9Y+vX37dnx8vPx7XbhwQfcn0ubNm48ZM0Z+CTJq167922+/+fv766anpqbOmTOnbt26DRo0mDZt2r179wwtdsmSJWlpaerTypUrd+zY0cn89evXr1u3rvo0NjbWvhtm+/btp06d0k756quvvL2dnRbQzXDz5s2lS5fKjH/o0KEOr1+ldfjw4WPHjqlP/f39BwwY4GT+F154Qft03759RlPNeffu3YuIiNBOsb9LoJaujebGjRuSbyQT+PLly7VPS5Uq5aRDRQhRrVq1V199VXIA2c1sa69J5GQs9hf+0fL29h4xYoR2yokTJxzevS6LChQo4OXl5eXl5evrW6JEiY4dOy5YsED7tfLwww+vXr3az8/P7W+tUJuQnnnmGe30ZcuWOXmVcv0t9an6WvveSoey74POvrJghQoPAAAAAMgBtDQBAAAAAIQQIj09/fbt29u2bRs8eHDVqlWPHDmi/Wn+/PknTZqUxbfw8vLSTYmMjMxo5nLlyvX+L8m//goh0tLSnn/++ZiYGHVK8eLFFyxY4OPj48KwnevUqdPatWt1t8lT7d+/f8CAAaVKlXruuecOHDggucy9e/dqn3bu3DnTl7Rs2VL79OjRo7oZ1q1bp31ao0aNZs2aOV9mjRo1HnvsMe2UTZs2ZToSIYTzP10rNm/erH3aqlWrkJAQ54MpXbq0+jQ9PX3//v0yg/GggICAxf9VpkwZJ/Pr1qI7d+5IvpFM4LrVr0OHDpm2fehu5uVBZlt7TSInY7G/HJ3Ok08+6evrq52i7WjJAb6+vm+++ebOnTszuhCaW6hfQ02bNtXee27jxo1xcXEZvUrX8KS2NElepSn7PujsKwtWqPAAAAAAgBxASxMAAAAAWE5aWpqXHR8fn+LFi7ds2XLy5Mm65iEvL68ff/yxYsWKWXzfkiVL6i4ssXbt2oxmDggImPdfGbUN2fvss8/+/vtv9am3t/e8efO0f352r1atWh09etTJhZQSExMXLFhQv379J598Utcr5tDBgwe1T+vXr5/pS3Q39Ll27ZpuBt0t/7R3TXKie/fu2qfaVJ0oVKhQpvPofsdGjRpl+pJatWppn548eVJmMB4UFBTU/b+ctwvoWu50lzRzQibw48ePa5+2aNFCcuFmYLa11yRyMpZMhYaG6iqVbpXLbvny5QsNDZW/lZtr1C9Hb2/vrl27aqdn9HV29+7dbdu2qU8feuihatWq6ZbmXPZ90NlXFqxQ4QEAAAAAOYCWJgAAAACAM35+fj/++GOvXr2yvigfH58GDRpop7z//vtuv5LH7t27P/vsM+2UkSNHOr/hV9aFhYWtWrVq3bp1ul9QZ9WqVfXr1//4449TUlIymic9Pf306dPaKbp2JYeaNGkyXEN3i0Cbzaa74sUjjzyS6TKFELpf59SpU7GxsTIvzNShQ4e0T3V/zHaoQoUK2qcXLlxwy0isIDEx8eLFi9opVapU8dBYDDPh2msGJoylcuXK2qf//vuvWxYrKSYm5rPPPqtZs+bOnTuz7120TUiS955btWpVamqqw1fJtDRl3wedrWWBCg8AAAAAcAvfzGcBAAAAAFhVtWrVZs6c+eijj7prgS+++KL2ghNRUVGPPPLIe++9N2zYsODg4KwvPy4urnfv3tq/H7ds2XLUqFFZX7KMdu3atWvXbtu2bVOmTFm5cqXDvqXU1NRPP/1069atq1atcngHn5iYGN3leUqUKJHpW1evXn3cuHEZ/TQ6Olr3x+waNWpkukz72Ww2282bN93ySUVERGifhoWFZfqSYsWKaZ/evn0768OwiHv37ulWqvLly3tqMEaZcO01AxPGomtJuXfvXtaXqTNhwoTk5GQhRHJyclRU1JEjR3bs2KHN4erVq+3atVu3bl2mt2ZzjfYqUM2aNStZsuSNGzeUp6tXr05OTvb399e9ZOnSpdqn2pYmmWtKZd8Hna1lgQoPAAAAAHALrtIEAAAAAHDskUceOXbsmBv7mYQQr7zySsOGDbVTEhMTP/nkkwoVKnz66ad37tzJ4vLffPNN3aVBPvzwQ93d7rJbixYtfv/996tXr3799dcZXWBp586dLVq0cHhxlOjoaN2UkJCQLA7JvrdA98fjjOTPnz9fvnzaKZGRkVkcjBDCZrPFxcVppxQtWjTTVxUsWFD7NDsaJnLA/v37x48f/9RTT1WvXr148eK+vr7qzR+/++67bHpT+zUtF3X2mG3tNQkTxqKrVPalLOv69+8/aNCgQYMGvf3222PHjl21atXVq1dHjRqlvWljfHx8z549s/5t4pD2ukq6e8/FxMRs2bJFN398fPyGDRvUpzVq1FDvOifkrtKUfR909pUFK1d4AAAAAIB70dIEAAAAAJbj5eX1miNNmjTRznbgwIGrV6+69619fX0XLVpUqVIl3fTIyMiPP/64bNmyr732WlZuRbdq1SrdlI8//thms7m8QJcVL178nXfeOXny5NatWx9//HH7GQ4fPjxw4ED76fZ/Zg4KCsriYHR/G/b29s6fP7/ka3VtClFRUVkcjBAiNjZWd3WQihUremVm6NCh2pckJCS4PAAvLy+XX+uy3377rUGDBg0aNBg+fPjKlStPnjx5+/bttLS0HHhrXXuBr69vQEBADryvW5ht7TUJE8aiG0BMTIxbFutcSEjI6NGjlyxZot2or127NmHChOx4O10TUqb3nlu/fn18fLz6tEePHk6W5lD2fdDZVxY8XuEBAAAAAHkGLU0AAAAAYDne3t7THJk7d672gkZpaWnffvut29+9XLlyf//9d8uWLe1/FB8fP2PGjFq1arVr12737t1uebu///575syZblmUa1q0aLF+/fqtW7faX7Fp7ty5mzdv1k3U3jXPXXR/OPfz85N/re6P3A7vpmeUW/5WnZWg7LvE3PJ7ZSQqKqpbt27PPvvs/v37s+9dnNDd30p7SRvzM9vaaxImjEV3z7WcaddTPP300wMGDNBOmT59enYMQJfVY489pr0x6IoVK3StPE7uOifkilj2fdDZVxY8XuEBAAAAAHkGLU0AAAAAgP9Tvnz5p556Sjtl5syZ2XHzoKJFi27evPmXX34pV66cwxk2bNjw6KOPPv300265TNT777/v8RtOtWjRYv/+/Z06ddJN/+qrrzwyHs/yyHWztEJDQ3VTdBcscaO7d++2aNFC19mg8Pb2Llq0aKVKlapUqVKlShXdfZcAyBs2bJi2JTcyMnLXrl1ufxdd7dLde+7mzZt///23+jQlJUV74UDdXefsl5Zn5NXfCwAAAACQ82hpAgAAAAD8z+DBg7VPY2Njp0+fnh1v5OXl1adPnzNnzvz0009VqlRxOM+KFSuqV6++aNEiowuvXr26r6+v+jQqKmr48OGuj9VN8ufPv2jRourVq2snrl+//vbt254akmXZtzTdv38/O94oPT39qaeeOn78uHZiwYIFhwwZ8ueff8bGxt6+ffvMmTOnTp06depU7969s2MMgBWUL19eV10PHjyYA+/r5N5z27Zt0942TjcnAAAAAADIlG/mswAAAAAALKNFixZ16tQ5fPiwOmXy5MlDhw41dLMbef7+/i+//HK/fv3Wr18/YcKEjRs36maIjY3t1atXZGTkwIEDJZf5wAMPbNmy5fPPP588ebI6cfbs2f369WvSpInbhu6SfPnyjR49ukePHtqJO3fu7NKli/rUPur09HTt1UdcoLujkKH7TOmutxEYGJiVkWTk008/NbrksmXLuvx29tdDunHjhv2dAZ1IT0+/fPmydkrx4sXz5cunm+2XX37ZuXOndkrnzp1//PHHYsWKGRhulmk7/ERuu6OT+ddejzBhLLobvenuepYD6tWrp20fPHfuXA686WOPPVa8ePFbt24pT5ctW/bNN98oj53fdU5S9n3QOVkWcrjCAwAAAADyDFqaAAAAAAD/MXjw4Jdeekl9GhERsXDhwhdeeCH73tHLy6t9+/bt27ffv3//yJEj169fr/2pzWYbPHhw3bp1GzdunOmiChUqtHbt2hIlSnzyyScLFy5U/8xss9kGDBhw8OBB3R9xc17Hjh39/Py0f5Y+fPiwtqUpODhY95KEhIT8+fNn5U0LFy6sfZqenh4fH2/ff+NQTEyM9mmhQoWyMhKF/d+2X3/99aJFi2Z9yZIeeOCBwMDAxMREdcqpU6datGghv4QzZ87o7iG1YcOGtm3b6mYbM2aM9mmnTp1+//13XY9CDggJCdE+TUtLS05O9vf3z+FhuMZsa69JmDCW2NhY7dMCBQq4ZbHydJ2C2XHXVHs+Pj5du3adNm2a8vTChQtHjhypXbu2zWZbsWKFOluNGjV0F5GSlH0fdPaVBY9XeAAAAABAnsGN5wAAAAAA/9GrV6/ixYtrp6jXnMhu9evXX7du3erVq8PCwrTT09LSJO8c99tvvynX2gkNDR03bpz2R8eOHZs0aZJbxvnVV18V1MjoxnkO5cuXr3z58topUVFR2qe6PzMLIeLj410eqsL+mkCRkZEyL0xPT9e1Bdhf38gFoaGhuutO6Zohspuvr2+tWrW0U06dOmVoCRcvXtRNCQ8P1005ePDgpUuXtG86bdq0nO9nEo5utJfDgWeF2dZekzBhLB5vadK1fhq6oFFW6C6/pFycaffu3devX89oHnnZ90FnX1nweIUHAAAAAOQZtDQBAAAAAP4jICDg9ddf1045cuTIpk2bcmwAHTp0OHjwYI0aNbQTd+7cqbvPl0N16tRRH7/44ouNGjXS/nT06NFXr17N+ggDAgKiNS5cuGDo5bq/K+supBESEqLrerl27ZqrI/3fMoOCgrRTTpw4IfPCS5cuaW9d5OXlVapUqSwORlmO7q/pd+7cyfpiDalXr5726ebNmw29/OzZs7op9rdJ2rp1q/Zp8+bNdb16OaZQoUJeXl7aKdpeK5Mz29prEiaMRdfnl5VOqcjIyIf+S6ZExMXFaZ/m2EW5mjdvru0DXrZsmXDTXedEdn7Q2VcWzFDhAQAAAAB5Ay1NAAAAAAC9N954Q3f3maxfqCk9Pf3Gf6Wnp2c0c/HixRcuXKj7a+uuXbsMvaOXl9fUqVO114qIi4sbMmSIwYE7ULFiRe3TlJSU06dPy79cd70K3R/+vb29dZd9OnfuXKbLPHDgwIsav/76q24GXQfP/v37ZYZ67Ngx7dMqVarYX0TKNbrmnn/++ccti5X32GOPaZ8eP37c0J/z//77b+3TihUr2t8KKiIiQvvU0NW83Mvf379ChQraKWfOnPHUYFxgtrXXJMwWi64MVqpUyeVFBQcH//NfMpcmUu80qihSpIjLAzDEx8dHe/PQY8eOnT9/fvny5eqU6tWru3bXOUU2fdDZWhY8XuEBAAAAAHkDLU0AAAAAAL0SJUo8++yz2inr1q07fvx4VpaZmJhY6r9OnjzpZH7lyhzaKTdv3jT6pvXq1XvllVe0U37//fd169YZXY5O/fr1de1WGzZskHxtSkqKrnVGd5s/IUTdunW1T2X+gL179+5fNK5cuaKboWnTptqnynVEMqW7Otcjjzwi8yoZukXt2bPHXUuW1LlzZ911RH766SfJ16anp+/YsUM7pW3btvaz6ZowAgMDDY7RnXQrlW78Jme2tdckTBXL/fv3dZVKd6U9QwICAoKDg7VTZHpijhw5on2ak02EuoswjRkzRtuK2qNHj6wsPPs+6OwrCx6v8AAAAACAvIGWJgAAAACAA/ZXM5owYUJWFpgvX74CBQpop2T6J2rdNTb8/PxceN/PP/+8cOHC2imDBg1KTEx0YVGqEiVKNGzYUDtl0qRJaWlpMq/dunVrfHy8dkrt2rV18zRo0ED7dM2aNZkuVnfRoAcffFA3Q+vWrbVPDx48eODAAefLTE1N/f3337VTHn/88UxHIkkX4JIlS1JTU921cBlBQUG6vr0pU6bcvXtX5rVr167V3Q3QYUuTrofp9u3bzhcbFRUl8+6usV+pMl1jZa6LkzPMtvaahKliWbduXVJSknZKzZo1s7LA+vXra59u3LjR+fxXrlzR9d3qrsSWrVq0aFGsWDH16dy5c7U/dfmuc4rs+6Czryx4vMIDAAAAAPIGWpoAAAAAAA48/PDDzZo100759ddfr1+/nsVlap8uWrTI+fxnz57VPi1ZsqQLb1qkSJHPPvtMO+X8+fOff/65C4vS0l386fz586NGjcr0VTabbezYsdopAQEBrVq10s3WvXt3Hx8f9enRo0e3bt3qZLF37txZuXKl+tTX11f3J3AhROvWrXV9TsOGDbPZbE4WO2PGDG3jTtGiRbt37+5kfkM6dOig/R1v3bo1bdq0TF+VlpbWrVs33dVHXPb6669r70sYHR392muvyYzh008/1U4pVapUx44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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "names = list(X_train_binarized.columns)\n", - "for i in range(3):\n", - " card = fasterrisk.visualize_risk_card(names, X_train_binarized, model_idx=i, title=f'Risk Score Card for Top {i+1} Model') \n", - " display(card)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "474", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/fasterrisk/binarization_util.py b/src/fasterrisk/binarization_util.py index a081736..10abe3f 100644 --- a/src/fasterrisk/binarization_util.py +++ b/src/fasterrisk/binarization_util.py @@ -176,7 +176,7 @@ def fit(self, df: pd.DataFrame) -> None: if self.whether_interval: # do it in intervals - for i in range(0, len(perctile)-1): + for i in range(0, len(perctile)): # NOTE: changed due to inconsistency issue with number of bins if i == 0: name = f"{col}<={perctile[i]}" threshold = perctile[i] @@ -192,7 +192,7 @@ def fit(self, df: pd.DataFrame) -> None: GroupMap[col] = col_idx else: # do it in <= - for i in range(0, len(perctile)-1): + for i in range(0, len(perctile)): # NOTE: changed due to inconsistency issue with number of bins binarizers.append({ "name": f"{col}<={perctile[i]}", "col": col, diff --git a/src/fasterrisk/score_visual.py b/src/fasterrisk/score_visual.py index c2bd3dc..9250a5d 100644 --- a/src/fasterrisk/score_visual.py +++ b/src/fasterrisk/score_visual.py @@ -118,7 +118,10 @@ def compute_offset(df_interval: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFram return df_offset, pd.concat([df_offset['feature'], feature_offsets], axis=1).groupby('feature').tail(1) def output_to_score_intervals(input_text: str, feature_max_dict: Dict[str, float]) -> Tuple[pd.DataFrame, pd.DataFrame]: - return compute_offset(compute_cumulative(scrape_to_df(input_text), feature_max_dict = feature_max_dict)) + scrape_df = scrape_to_df(input_text) + cumul_df = compute_cumulative(scrape_df, feature_max_dict = feature_max_dict) + offset = compute_offset(cumul_df) + return offset class ScoreCardVisualizer: diff --git a/src/fasterrisk/wrapper.py b/src/fasterrisk/wrapper.py index 1884ff8..d619e6c 100644 --- a/src/fasterrisk/wrapper.py +++ b/src/fasterrisk/wrapper.py @@ -49,7 +49,9 @@ def shift_coefficients(feature_offsets: pd.DataFrame, features_and_betas: List) if 'isNaN' in feature: # every NaN needs to be 0 features_and_betas[1][i] = 0 continue - if 'isNaN' in next_feature and feature.split('<=')[0] in selected_features: # every last feature of selected ones needs to be shifted + + # every last feature of selected ones needs to be shifted + if ('isNaN' in next_feature or feature.split('<=')[0] != next_feature.split('<=')[0]) and feature.split('<=')[0] in selected_features: # NOTE: changed to include the edge case where isNaN is not available (no missing values in dataset) features_and_betas[1][i] -= feature_offsets.loc[feature.split('<=')[0], 'interval_points'] return features_and_betas[1] @@ -76,7 +78,9 @@ def get_max_features(X_train: pd.DataFrame) -> Dict[str, float]: if 'isNaN' in binarized_feature: continue split = binarized_feature.split('<=') - if 'isNaN' in next_binarized_feature: # if encountered a new feature, this assumes that bins for one feature is altogether + + # if encountered a new feature, this assumes that bins for one feature is altogether + if 'isNaN' in next_binarized_feature or split[0] != next_binarized_feature.split('<=')[0]: # NOTE: changed to include the edge case where isNaN is not available (no missing values in dataset) feature_max_dict[split[0]] = float(split[1]) return feature_max_dict