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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Data-sets\" data-toc-modified-id=\"Data-sets-1\"><span class=\"toc-item-num\">1 </span>Data sets</a></span><ul class=\"toc-item\"><li><span><a href=\"#Synthetic-data-with-unobservables\" data-toc-modified-id=\"Synthetic-data-with-unobservables-1.1\"><span class=\"toc-item-num\">1.1 </span>Synthetic data with unobservables</a></span></li><li><span><a href=\"#Data-without-unobservables\" data-toc-modified-id=\"Data-without-unobservables-1.2\"><span class=\"toc-item-num\">1.2 </span>Data without unobservables</a></span></li></ul></li><li><span><a href=\"#Algorithms\" data-toc-modified-id=\"Algorithms-2\"><span class=\"toc-item-num\">2 </span>Algorithms</a></span><ul class=\"toc-item\"><li><span><a href=\"#Contraction-algorithm\" data-toc-modified-id=\"Contraction-algorithm-2.1\"><span class=\"toc-item-num\">2.1 </span>Contraction algorithm</a></span></li><li><span><a href=\"#Causal-approach---metrics\" data-toc-modified-id=\"Causal-approach---metrics-2.2\"><span class=\"toc-item-num\">2.2 </span>Causal approach - metrics</a></span></li></ul></li><li><span><a href=\"#Performance-comparison\" data-toc-modified-id=\"Performance-comparison-3\"><span class=\"toc-item-num\">3 </span>Performance comparison</a></span><ul class=\"toc-item\"><li><span><a href=\"#With-unobservables-in-the-data\" data-toc-modified-id=\"With-unobservables-in-the-data-3.1\"><span class=\"toc-item-num\">3.1 </span>With unobservables in the data</a></span></li><li><span><a href=\"#Without-unobservables\" data-toc-modified-id=\"Without-unobservables-3.2\"><span class=\"toc-item-num\">3.2 </span>Without unobservables</a></span></li></ul></li></ul></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Our model is defined by the probabilistic expression \n",
"\n",
"P(Y=0 | \\text{do}(R=r)) = \\sum_x \\underbrace{P(Y=0|X=x, T=1)}_\\text{1} \n",
"\\overbrace{P(T=1|R=r, X=x)}^\\text{2} \n",
"\\underbrace{P(X=x)}_\\text{3}\n",
"\\end{equation}\n",
"\n",
"which is equal to \n",
"\n",
"\\begin{equation}\\label{model_cont}\n",
"P(Y=0 | \\text{do}(R=r)) = \\int_x P(Y=0|X=x, T=1)P(T=1|R=r, X=x)P(X=x)\n",
"\\end{equation}\n",
"\n",
"for continuous $x$. In the model Z is a latent, unobserved variable, and can be excluded from the expression with do-calculus by showing that $X$ is admissible for adjustment. Model as a graph:\n",
"\n",
"\n",
"For predicting the probability of negative outcome the following should hold because by Pearl $P(Y=0 | \\text{do}(R=r), X=x) = P(Y=0 | R=r, X=x)$ when $X$ is an admissible set:\n",
"\n",
"\\begin{equation} \\label{model_pred}\n",
"P(Y=0 | \\text{do}(R=r), X=x) = P(Y=0|X=x, T=1)P(T=1|R=r, X=x).\n",
"\\end{equation}\n",
"\n",
"Still it should be noted that this prediction takes into account the probability of the individual to be given a positive decision ($T=1$), see second term in \\ref{model_pred}.\n",
"\n",
"----\n",
"\n",
"### Notes\n",
"\n",
"* Equations \\ref{model_disc} and \\ref{model_cont} describe the whole causal effect in the population (the causal effect of changing $r$ over all strata $X$).\n",
"* Prediction should be possible with \\ref{model_pred}. Both terms can be learned from the data. NB: the probability $P(Y=0 | \\text{do}(R=r), X=x)$ is lowest when the individual $x$ is the most dangerous or the least dangerous. How could we infer/predict the counterfactual \"what is the probability of $Y=0$ if we were to let this individual go?\" has yet to be calculated.\n",
"* Is the effect of R learned/estimated correctly if it is just plugged in to a predictive model (e.g. logistic regression)? **NO**\n",
"* $P(Y=0 | do(R=0)) = 0$ only in this application. <!-- My predictive models say that when $r=0$ the probability $P(Y=0) \\approx 0.027$ which would be a natural estimate in another application/scenario (e.g. in medicine the probability of an adverse event when a stronger medicine is distributed to everyone. Then the probability will be close to zero but not exactly zero.) -->\n",
"\n",
"Imports and settings."
"metadata": {},
"outputs": [],
"source": [
"# Imports\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from datetime import datetime\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats as scs\n",
"import scipy.integrate as si\n",
"import seaborn as sns\n",
"import numpy.random as npr\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Settings\n",
"\n",
"%matplotlib inline\n",
"\n",
"plt.rcParams.update({'font.size': 16})\n",
"plt.rcParams.update({'figure.figsize': (14, 7)})\n",
"\n",
"# Suppress deprecation warnings.\n",
"\n",
"import warnings\n",
"\n",
"def fxn():\n",
" warnings.warn(\"deprecated\", DeprecationWarning)\n",
"\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\")\n",
" fxn()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data sets\n",
"\n",
"### Synthetic data with unobservables\n",
"\n",
"In the chunk below, we generate the synthetic data as described by Lakkaraju et al. The default values and definitions of $Y$ and $T$ values follow their description.\n",
"\n",
"**Parameters**\n",
"\n",
"* M = `nJudges_M`, number of judges\n",
"* N = `nSubjects_N`, number of subjects assigned to each judge\n",
"* betas $\\beta_i$ = `beta_i`, where $i \\in \\{X, Z, W\\}$ are coefficients for the respected variables\n",
"\n",
"**Columns of the data:**\n",
"\n",
"* `judgeID_J` = judge IDs as running numbering from 0 to `nJudges_M - 1`\n",
"* R = `acceptanceRate_R`, acceptance rates\n",
"* X = `X`, invidual's features observable to all (models and judges)\n",
"* Z = `Z`, information observable for judges only\n",
"* W = `W`, unobservable / inaccessible information\n",
"* T = `decision_T`, bail-or-jail decisions where $T=0$ represents jail decision and $T=1$ bail decision.\n",
"* Y = `result_Y`, result variable, if $Y=0$ person will or would recidivate and if $Y=1$ person will or would not commit a crime.\n",
"\n",
"The generated data will have M\\*N rows."
" '''Return value of sigmoid function (inverse of logit) at x.'''\n",
"\n",
"def generateDataWithUnobservables(nJudges_M=100,\n",
" nSubjects_N=500,\n",
" beta_X=1.0,\n",
" beta_Z=1.0,\n",
" beta_W=0.2):\n",
"\n",
" df = pd.DataFrame()\n",
"\n",
" # Assign judge IDs as running numbering from 0 to nJudges_M - 1\n",
" df = df.assign(judgeID_J=np.repeat(range(0, nJudges_M), nSubjects_N))\n",
"\n",
" # Sample acceptance rates uniformly from a closed interval\n",
" # from 0.1 to 0.9 and round to tenth decimal place.\n",
" acceptance_rates = np.round(npr.uniform(.1, .9, nJudges_M), 10)\n",
"\n",
" # Replicate the rates so they can be attached to the corresponding judge ID.\n",
" df = df.assign(acceptanceRate_R=np.repeat(acceptance_rates, nSubjects_N))\n",
"\n",
" # Sample the variables from standard Gaussian distributions.\n",
" df = df.assign(X=npr.normal(size=nJudges_M * nSubjects_N))\n",
" df = df.assign(Z=npr.normal(size=nJudges_M * nSubjects_N))\n",
" df = df.assign(W=npr.normal(size=nJudges_M * nSubjects_N))\n",
" probabilities_Y = sigmoid(beta_X * df.X + beta_Z * df.Z + beta_W * df.W)\n",
"\n",
" # 0 if P(Y = 0| X = x; Z = z; W = w) >= 0.5 , 1 otherwise\n",
" df = df.assign(result_Y=1 - probabilities_Y.round())\n",
" # For the conditional probabilities of T we add noise ~ N(0, 0.1)\n",
" probabilities_T = sigmoid(beta_X * df.X + beta_Z * df.Z)\n",
" probabilities_T += npr.normal(0, np.sqrt(0.1), nJudges_M * nSubjects_N)\n",
"\n",
" df = df.assign(probabilities_T=probabilities_T)\n",
" # Sort by judges then probabilities in decreasing order\n",
" # Most dangerous for each judge are at the top.\n",
" df.sort_values(by=[\"judgeID_J\", \"probabilities_T\"],\n",
" ascending=False,\n",
" inplace=True)\n",
"\n",
" # Iterate over the data. Subject is in the top (1-r)*100% if\n",
" # his within-judge-index is over acceptance threshold times\n",
" # the number of subjects assigned to each judge. If subject\n",
" # is over the limit they are assigned a zero, else one.\n",
" df['decision_T'] = np.where(\n",
" (df.index.values % nSubjects_N) <\n",
" ((1 - df['acceptanceRate_R']) * nSubjects_N), 0, 1)\n",
" # Halve the data set to test and train\n",
" train, test = train_test_split(df, test_size=0.5)\n",
" train_labeled = train.copy()\n",
" test_labeled = test.copy()\n",
" # Set results as NA if decision is negative.\n",
" train_labeled.loc[train_labeled.decision_T == 0, 'result_Y'] = np.nan\n",
" test_labeled.loc[test_labeled.decision_T == 0, 'result_Y'] = np.nan\n",
" return train_labeled, train, test_labeled, test, df"
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data without unobservables\n",
"\n",
"In the chunk below, we generate a simplified data. The default values and definitions of $Y$ and $T$ values follow the previous description.\n",
"\n",
"**Parameters**\n",
"\n",
"* M = `nJudges_M`, number of judges\n",
"* N = `nSubjects_N`, number of subjects assigned to each judge\n",
"\n",
"**Columns of the data:**\n",
"\n",
"* `judgeID_J` = judge IDs as running numbering from 0 to `nJudges_M - 1`\n",
"* R = `acceptanceRate_R`, acceptance rates\n",
"* X = `X`, invidual's features observable to all (models and judges), now $X \\sim \\mathcal{N}(0, 1)$\n",
"* T = `decision_T`, bail-or-jail decisions where $T=0$ represents jail decision and $T=1$ bail decision.\n",
"* $p_y$ = `probabilities_Y`, variable where $p_y = P(Y=0)$\n",
"* Y = `result_Y`, result variable, if $Y=0$ person will or would recidivate and if $Y=1$ person will or would not commit a crime. Here $Y \\sim \\text{Bernoulli}(\\frac{1}{1+exp\\{-\\beta_X \\cdot X\\}})$"
"def generateDataWithoutUnobservables(nJudges_M=100, nSubjects_N=500, beta_X=1.0):\n",
"\n",
" df = pd.DataFrame()\n",
"\n",
" # Assign judge IDs as running numbering from 0 to nJudges_M - 1\n",
" df = df.assign(judgeID_J=np.repeat(range(0, nJudges_M), nSubjects_N))\n",
"\n",
" # Sample acceptance rates uniformly from a closed interval\n",
" # from 0.1 to 0.9 and round to tenth decimal place.\n",
" acceptance_rates = np.round(npr.uniform(.1, .9, nJudges_M), 10)\n",
"\n",
" # Replicate the rates so they can be attached to the corresponding judge ID.\n",
" df = df.assign(acceptanceRate_R=np.repeat(acceptance_rates, nSubjects_N))\n",
"\n",
" # Sample feature X from standard Gaussian distribution, N(0, 1).\n",
" df = df.assign(X=npr.normal(size=nJudges_M * nSubjects_N))\n",
"\n",
" # Calculate P(Y=0|X=x) = 1 / (1 + exp(-beta_X * x)) = sigmoid(beta_X * x)\n",
" df = df.assign(probabilities_Y=sigmoid(beta_X * df.X))\n",
"\n",
" df = df.assign(\n",
" result_Y=1-npr.binomial(n=1, p=df.probabilities_Y, size=nJudges_M * nSubjects_N))\n",
" # Invert the probabilities. ELABORATE COMMENT!\n",
" df.probabilities_Y = 1 - df.probabilities_Y\n",
"\n",
" # Sort by judges then probabilities in decreasing order\n",
" # I.e. the most dangerous for each judge are first.\n",
" df = df.sort_values(by=[\"judgeID_J\", \"probabilities_Y\"], ascending=False)\n",
"\n",
" # Iterate over the data. Subject is in the top (1-r)*100% if\n",
" # his within-judge-index is over acceptance threshold times\n",
" # the number of subjects assigned to each judge. If subject\n",
" # is over the limit they are assigned a zero, else one.\n",
" df.reset_index(drop=True, inplace=True)\n",
"\n",
" df['decision_T'] = np.where((df.index.values % nSubjects_N) <\n",
" ((1 - df['acceptanceRate_R']) * nSubjects_N),\n",
" 0, 1)\n",
"\n",
" # Halve the data set to test and train\n",
" train, test = train_test_split(df, test_size=0.5)\n",
" train_labeled = train.copy()\n",
" test_labeled = test.copy()\n",
" # Set results as NA if decision is negative.\n",
" train_labeled.loc[train_labeled.decision_T == 0, 'result_Y'] = np.nan\n",
" test_labeled.loc[test_labeled.decision_T == 0, 'result_Y'] = np.nan\n",
" return train_labeled, train, test_labeled, test, df"
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Algorithms\n",
"\n",
"### Contraction algorithm\n",
"\n",
"Below is an implementation of Lakkaraju's team's algorithm presented in [their paper](https://helka.finna.fi/PrimoRecord/pci.acm3098066). Relevant parameters to be passed to the function are presented in the description."
]
},
{
"cell_type": "code",
"def contraction(df, judgeIDJ_col, decisionT_col, resultY_col, modelProbS_col,\n",
" accRateR_col, r):\n",
" '''\n",
" This is an implementation of the algorithm presented by Lakkaraju\n",
" et al. in their paper \"The Selective Labels Problem: Evaluating \n",
" Algorithmic Predictions in the Presence of Unobservables\" (2017).\n",
" \n",
" Parameters:\n",
" df = The (Pandas) data frame containing the data, judge decisions,\n",
" judge IDs, results and probability scores.\n",
" judgeIDJ_col = String, the name of the column containing the judges' IDs\n",
" in df.\n",
" decisionT_col = String, the name of the column containing the judges' decisions\n",
" resultY_col = String, the name of the column containing the realization\n",
" modelProbS_col = String, the name of the column containing the probability\n",
" scores from the black-box model B.\n",
" accRateR_col = String, the name of the column containing the judges' \n",
" acceptance rates\n",
" r = Float between 0 and 1, the given acceptance rate.\n",
" \n",
" Returns the estimated failure rate at acceptance rate r.\n",
" most_lenient_ID_q = df[judgeIDJ_col].loc[df[accRateR_col].idxmax()]\n",
" # Subset. \"D_q is the set of all observations judged by q.\"\n",
" D_q = df[df[judgeIDJ_col] == most_lenient_ID_q].copy()\n",
" # All observations of R_q have observed outcome labels.\n",
" # \"R_q is the set of observations in D_q with observed outcome labels.\"\n",
" R_q = D_q[D_q[decisionT_col] == 1].copy()\n",
" # Sort observations in R_q in descending order of confidence scores S and\n",
" # assign to R_sort_q.\n",
" # \"Observations deemed as high risk by B are at the top of this list\"\n",
" R_sort_q = R_q.sort_values(by=modelProbS_col, ascending=False)\n",
"\n",
" number_to_remove = int(\n",
" round((1.0 - r) * D_q.shape[0] - (D_q.shape[0] - R_q.shape[0])))\n",
"\n",
" # \"R_B is the list of observations assigned to t = 1 by B\"\n",
" R_B = R_sort_q[number_to_remove:R_sort_q.shape[0]]\n",
"\n",
" return np.sum(R_B[resultY_col] == 0) / D_q.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Generalized performance:\n",
"\n",
"$$\n",
"\\mathbf{gp} = \\sum_{x\\in\\mathcal{X}} f(x)\\delta(F(x) < r)P(X=x)\n",
"$$\n",
"\n",
"and empirical performance:\n",
"\n",
"$$\n",
"\\mathbf{ep} = \\dfrac{1}{n} \\sum_{(x, y) \\in \\mathcal{D}} \\delta(y=0) \\delta(F(x) < r)\n",
"$$\n",
"\n",
"where\n",
"\n",
"$$\n",
"$$\n",
"\n",
"and\n",
"\n",
"$$\n",
"F(x_0) = \\int P(x)~\\delta(P(Y=0|T=1, X=x) > P(Y=0|T=1, X=x_0)) ~ dx = \\int P(x)~\\delta(f(x) > f(x_0)) ~ dx.\n",
"$$\n"
"def getProbabilityForClass(x, model, class_value):\n",
" Function (wrapper) for obtaining the probability of a class given x and a \n",
" predictive model.\n",
" \n",
" x = individual features, an array, shape (observations, features)\n",
" model = a trained sklearn model. Predicts probabilities for given x. Should\n",
" accept input of size (observations, features)\n",
" class_value = the resulting class to predict (usually 0 or 1).\n",
" The probabilities of given class label for each x.\n",
" '''\n",
" if x.ndim == 1:\n",
" # if x is vector, transform to column matrix.\n",
" f_values = model.predict_proba(np.array(x).reshape(-1, 1))\n",
" else:\n",
" f_values = model.predict_proba(x)\n",
"\n",
" # Get correct column of predicted class, remove extra dimensions and return.\n",
" return f_values[:, model.classes_ == class_value].flatten()\n",
"\n",
"def cdf(x_0, model, class_value):\n",
" '''\n",
" Cumulative distribution function as described above.\n",
" \n",
" '''\n",
" prediction = lambda x: getProbabilityForClass(\n",
" np.array([x]).reshape(-1, 1), model, class_value)\n",
"\n",
"\n",
" x_values = np.linspace(-10, 10, 40000)\n",
"\n",
" x_preds = prediction(x_values)\n",
"\n",
" y_values = scs.norm.pdf(x_values)\n",
"\n",
" y_copy[prediction(x_values) > prediction_x_0[i]] = 0\n",
" \n",
" results[i] = si.simps(y_copy, x=x_values)\n",
"\n",
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Performance comparison\n",
"\n",
"Below we try to replicate the results obtained by Lakkaraju and compare their model's performance to the one of ours."
"def fitLogisticRegressionModel(x_train, y_train, x_test, class_value):\n",
" '''\n",
" Fit logistic regression model with given inputs. Checks their shape if \n",
" incompatible.\n",
" \n",
" Parameters:\n",
" \n",
" \n",
" Returns:\n",
" (1) Trained LogisticRegression model\n",
" (2) Probabilities for given test inputs for given class.\n",
" '''\n",
" # Instantiate the model (using the default parameters)\n",
" logreg = LogisticRegression(solver='lbfgs')\n",
"\n",
" # Check shape and fit the model.\n",
" if x_train.ndim == 1:\n",
" logreg = logreg.fit(x_train.values.reshape(-1, 1), y_train)\n",
" else:\n",
" logreg = logreg.fit(x_train, y_train)\n",
" # Check shape and predict probabilities.\n",
" if x_test.ndim == 1:\n",
" label_probs_logreg = logreg.predict_proba(x_test.values.reshape(-1, 1))\n",
" else:\n",
" label_probs_logreg = logreg.predict_proba(x_test)\n",
" return logreg, label_probs_logreg[:, logreg.classes_ == class_value]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### With unobservables in the data\n",
"Lakkaraju says that they used logistic regression. We train the predictive models using only *observed observations*, i.e. observations for which labels are available. We then predict the probability of negative outcome for all observations in the test data and attach it to our data set."
{
"name": "stdout",
"output_type": "stream",
"text": [
"1| 0 1 2| 0 1 3| 0 1 4| 0 1 5| 0 1 6| 0 1 7| 0 1 8| 0 1 "
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"failure_rates = np.zeros((8, 5))\n",
"failure_sems = np.zeros((8, 5))\n",
"for r in np.arange(1, 9):\n",
" print(r, end=\"| \")\n",
" \n",
" f_rate_true = np.zeros(nIter)\n",
" f_rate_label = np.zeros(nIter)\n",
" f_rate_human = np.zeros(nIter)\n",
" f_rate_cont = np.zeros(nIter)\n",
" f_rate_caus = np.zeros(nIter)\n",
" \n",
" for i in range(nIter):\n",
" \n",
" print(i, end=\" \")\n",
" \n",
" train_labeled, train, test_labeled, test, df = generateDataWithUnobservables()\n",
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"\n",
" logreg, predictions = fitLogisticRegressionModel(\n",
" train_labeled.dropna().X,\n",
" train_labeled.dropna().result_Y,\n",
" test.X, 0)\n",
" test = test.assign(B_prob_0_logreg = predictions)\n",
"\n",
" logreg, predictions_labeled = fitLogisticRegressionModel(\n",
" train_labeled.dropna().X,\n",
" train_labeled.dropna().result_Y,\n",
" test_labeled.X, 0)\n",
" test_labeled = test_labeled.assign(B_prob_0_logreg = predictions_labeled)\n",
"\n",
" #### True evaluation\n",
" # Sort by failure probabilities, subjects with the smallest risk are first.\n",
" test.sort_values(by='B_prob_0_logreg', inplace=True, ascending=True)\n",
"\n",
" to_release = int(round(test.shape[0] * r / 10))\n",
"\n",
" # Calculate failure rate as the ratio of failures to those who were given a\n",
" # positive decision, i.e. those whose probability of negative outcome was\n",
" # low enough.\n",
" f_rate_true[i] = np.sum(\n",
" test.result_Y[0:to_release] == 0) / test.shape[0]\n",
"\n",
" #### Labeled outcomes only\n",
" # Sort by failure probabilities, subjects with the smallest risk are first.\n",
" test_labeled.sort_values(by='B_prob_0_logreg',\n",
" inplace=True,\n",
" ascending=True)\n",
"\n",
" to_release = int(round(test_labeled.shape[0] * r / 10))\n",
"\n",
" f_rate_label[i] = np.sum(\n",
" test_labeled.result_Y[0:to_release] == 0) / test_labeled.shape[0]\n",
"\n",
" #### Human evaluation\n",
" # Get judges with correct leniency as list\n",
" correct_leniency_list = test_labeled.judgeID_J[\n",
" test_labeled['acceptanceRate_R'].round(1) == r / 10].values\n",
"\n",
" # Released are the people they judged and released, T = 1\n",
" released = test_labeled[test_labeled.judgeID_J.isin(correct_leniency_list)\n",
" & (test_labeled.decision_T == 1)]\n",
"\n",
" # Get their failure rate, aka ratio of reoffenders to number of people judged in total\n",
" f_rate_human[i] = np.sum(\n",
" released.result_Y == 0) / correct_leniency_list.shape[0]\n",
"\n",
" #### Contraction, logistic regression\n",
" f_rate_cont[i] = contraction(test_labeled, 'judgeID_J',\n",
" 'decision_T', 'result_Y',\n",
" 'B_prob_0_logreg',\n",
" 'acceptanceRate_R', r / 10)\n",
"\n",
" #### Causal model - empirical performance\n",
" #\n",
" f_rate_caus[i] = np.sum((test_labeled.dropna().result_Y == 0) & (\n",
" cdf(test_labeled.dropna().X, logreg, 0) < r /\n",
" 10)) / test_labeled.dropna().result_Y.shape[0]\n",
" \n",
" failure_rates[r - 1, 0] = np.mean(f_rate_true)\n",
" failure_rates[r - 1, 1] = np.mean(f_rate_label)\n",
" failure_rates[r - 1, 2] = np.mean(f_rate_human)\n",
" failure_rates[r - 1, 3] = np.mean(f_rate_cont)\n",
" failure_rates[r - 1, 4] = np.mean(f_rate_caus)\n",
" \n",
" failure_sems[r - 1, 0] = scs.sem(f_rate_true)\n",
" failure_sems[r - 1, 1] = scs.sem(f_rate_label)\n",
" failure_sems[r - 1, 2] = scs.sem(f_rate_human)\n",
" failure_sems[r - 1, 3] = scs.sem(f_rate_cont)\n",
" failure_sems[r - 1, 4] = scs.sem(f_rate_caus)\n",
"x_ax = np.arange(0.1, 0.9, 0.1)\n",
"plt.errorbar(x_ax, failure_rates[:, 0], label='True Evaluation', c='green', yerr=failure_sems[:, 0])\n",
"plt.errorbar(x_ax, failure_rates[:, 1], label='Labeled outcomes', c='magenta', yerr=failure_sems[:, 1])\n",
"plt.errorbar(x_ax, failure_rates[:, 2], label='Human evaluation', c='red', yerr=failure_sems[:, 2])\n",
"plt.errorbar(x_ax, failure_rates[:, 3], label='Contraction, log.', c='blue', yerr=failure_sems[:, 3])\n",
"plt.errorbar(x_ax, failure_rates[:, 4], label='Causal model, ep', c='black', yerr=failure_sems[:, 4])\n",
"plt.title('Failure rate vs. Acceptance rate with unobservables')\n",
"plt.xlabel('Acceptance rate')\n",
"plt.ylabel('Failure rate')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"### Without unobservables"
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1| 0 1 2| 0 1 3| 0 1 4| 0 1 5| 0 1 6| 0 1 7| 0 1 8| 0 1 "
"image/png": 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\n",
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f_rates = np.zeros((8, 5))\n",
"f_sems = np.zeros((8, 5))\n",
"\n",
"\n",
"for r in np.arange(1, 9):\n",
"\n",
" print(r, end=\"| \")\n",
"\n",
" s_f_rate_true = np.zeros(nIter)\n",
" s_f_rate_human = np.zeros(nIter)\n",
" s_f_rate_cont = np.zeros(nIter)\n",
" s_f_rate_caus = np.zeros(nIter)\n",
"\n",
" for i in range(nIter):\n",
"\n",
" print(i, end=\" \")\n",
"\n",
" s_train_labeled, s_train, s_test_labeled, s_test, s_df = generateDataWithoutUnobservables(\n",
" )\n",
"\n",
" s_logreg, predictions = fitLogisticRegressionModel(\n",
" s_train_labeled.dropna().X,\n",
" s_train_labeled.dropna().result_Y, s_test.X, 0)\n",
" s_test = s_test.assign(B_prob_0_logreg=predictions)\n",
"\n",
" s_logreg, predictions_labeled = fitLogisticRegressionModel(\n",
" s_train_labeled.dropna().X,\n",
" s_train_labeled.dropna().result_Y, s_test_labeled.X, 0)\n",
" s_test_labeled = test_labeled.assign(\n",
" B_prob_0_logreg=predictions_labeled)\n",
"\n",
" s_f_rate_cont[i] = contraction(s_test_labeled, 'judgeID_J',\n",
" 'decision_T', 'result_Y',\n",
" 'B_prob_0_logreg', 'acceptanceRate_R',\n",
" r / 10)\n",
"\n",
" s_f_rate_caus[i] = np.sum(\n",
" (s_test_labeled.dropna().result_Y == 0)\n",
" & (cdf(s_test_labeled.dropna().X, s_logreg, 0) < r /\n",
" 10)) / s_test_labeled.dropna().result_Y.shape[0]\n",
"\n",
" #### True evaluation\n",
" # Sort by failure probabilities, subjects with the smallest risk are first.\n",
" s_sorted = s_test.sort_values(by='B_prob_0_logreg',\n",
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" inplace=False,\n",
" ascending=True)\n",
"\n",
" to_release = int(round(s_sorted.shape[0] * r / 10))\n",
"\n",
" # Calculate failure rate as the ratio of failures to successes among those\n",
" # who were given a positive decision, i.e. those whose probability of negative\n",
" # outcome was low enough.\n",
" s_f_rate_true[i] = np.sum(\n",
" s_sorted.result_Y[0:to_release] == 0) / s_sorted.shape[0]\n",
"\n",
" #### Human error rate\n",
" # Get judges with correct leniency as list\n",
" correct_leniency_list = s_test_labeled.judgeID_J[\n",
" s_test_labeled['acceptanceRate_R'].round(1) == r / 10].values\n",
"\n",
" # Released are the people they judged and released, T = 1\n",
" released = s_test_labeled[\n",
" s_test_labeled.judgeID_J.isin(correct_leniency_list)\n",
" & (s_test_labeled.decision_T == 1)]\n",
"\n",
" # Get their failure rate, aka ratio of reoffenders to number of people judged in total\n",
" s_f_rate_human[i] = np.sum(\n",
" released.result_Y == 0) / correct_leniency_list.shape[0]\n",
"\n",
" f_rates[r - 1, 0] = np.mean(s_f_rate_true)\n",
" f_rates[r - 1, 2] = np.mean(s_f_rate_human)\n",
" f_rates[r - 1, 3] = np.mean(s_f_rate_cont)\n",
" f_rates[r - 1, 4] = np.mean(s_f_rate_caus)\n",
"\n",
" f_sems[r - 1, 0] = scs.sem(s_f_rate_true)\n",
" f_sems[r - 1, 2] = scs.sem(s_f_rate_human)\n",
" f_sems[r - 1, 3] = scs.sem(s_f_rate_cont)\n",
" f_sems[r - 1, 4] = scs.sem(s_f_rate_caus)\n",
"\n",
"x_ax = np.arange(0.1, 0.9, 0.1)\n",
"\n",
"plt.figure(figsize=(14, 8))\n",
"plt.errorbar(x_ax,\n",
" f_rates[:, 0],\n",
" label='True Evaluation',\n",
" c='green',\n",
" yerr=f_sems[:, 0])\n",
"# plt.errorbar(x_ax, f_rates[:, 2], label='Human evaluation', c='red', yerr=f_sems[:, 2]) No interpretation\n",
"plt.errorbar(x_ax,\n",
" f_rates[:, 3],\n",
" label='Contraction, log.',\n",
" c='blue',\n",
" yerr=f_sems[:, 3])\n",
"plt.errorbar(x_ax,\n",
" f_rates[:, 4],\n",
" label='Causal model, ep',\n",
" c='magenta',\n",
" yerr=f_sems[:, 4])\n",
"\n",
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"plt.title('Failure rate vs. Acceptance rate without unobservables')\n",
"plt.xlabel('Acceptance rate')\n",
"plt.ylabel('Failure rate')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14, 8))\n",
"plt.errorbar(x_ax,\n",
" f_rates[:, 0],\n",
" label='True Evaluation',\n",
" c='green',\n",
" yerr=f_sems[:, 0])\n",
"plt.errorbar(x_ax, f_rates[:, 2], label='Human evaluation', c='red', yerr=f_sems[:, 2])\n",
"plt.errorbar(x_ax,\n",
" f_rates[:, 3],\n",
" label='Contraction, log.',\n",
" c='blue',\n",
" yerr=f_sems[:, 3])\n",
"plt.errorbar(x_ax,\n",
" f_rates[:, 4],\n",
" label='Causal model, ep',\n",
" c='magenta',\n",
" yerr=f_sems[:, 4])\n",
"\n",
"plt.title('Failure rate vs. Acceptance rate without unobservables')\n",
"plt.xlabel('Acceptance rate')\n",
"plt.ylabel('Failure rate')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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",
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": true,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "300.7px"
},
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"position": {
"height": "352.85px",
"left": "1070px",
"right": "20px",
"top": "120px",
"width": "350px"
},