From 01a5188d74ef5ae54d73d892036e5187a158b396 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?= Date: Fri, 28 Nov 2025 23:31:40 +0900 Subject: [PATCH 1/5] add: overview --- book/_toc.yml | 3 ++- book/prescriptive_analytics/overview.md | 5 +++++ 2 files changed, 7 insertions(+), 1 deletion(-) create mode 100644 book/prescriptive_analytics/overview.md diff --git a/book/_toc.yml b/book/_toc.yml index 4c35a3e..beb9836 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -23,4 +23,5 @@ parts: sections: - file: cate_and_policy/parametric_cate.ipynb - file: cate_and_policy/nonparametric_cate.ipynb - - file: cate_and_policy/policy_learning.ipynb \ No newline at end of file + - file: cate_and_policy/policy_learning.ipynb + - file: prescriptive_analytics/overview.md \ No newline at end of file diff --git a/book/prescriptive_analytics/overview.md b/book/prescriptive_analytics/overview.md new file mode 100644 index 0000000..8bcc007 --- /dev/null +++ b/book/prescriptive_analytics/overview.md @@ -0,0 +1,5 @@ +# Prescriptive Analytics + +- Prescriptive Analytics는 데이터를 활용해 최적의 의사결정을 도출하는 분석 방식입니다. +- 접근 방식은 크게 **Prediction + Optimization**, **Causal Inference + Optimization** 으로 나눌 수 있습니다. +- 이 섹션에서는 **Causal Inference + Optimization** 에 집중하여, 개입의 인과효과(CATE)를 기반으로 **가장 효율적인 정책·전략을 선택하는 방법**을 다룹니다. From 88ffc8168767cd0825add97db53f5912d3e1d919 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?= Date: Sun, 7 Dec 2025 20:44:34 +0900 Subject: [PATCH 2/5] update: duality r-learner --- book/_toc.yml | 4 +- ...rning_for_effectiveness_optimization.ipynb | 1344 +++++++++++++++++ 2 files changed, 1347 insertions(+), 1 deletion(-) create mode 100644 book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb diff --git a/book/_toc.yml b/book/_toc.yml index 04c86ca..4b59776 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -24,4 +24,6 @@ parts: - file: scm/backdoor_criterion.ipynb - file: scm/frontdoor_criterion.ipynb - file: scm/causal_discovery.ipynb - - file: prescriptive_analytics/overview.md \ No newline at end of file + - file: prescriptive_analytics/overview.md + sections: + - file: prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb \ No newline at end of file diff --git a/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb b/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb new file mode 100644 index 0000000..c5b0644 --- /dev/null +++ b/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb @@ -0,0 +1,1344 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d6b70f64", + "metadata": {}, + "source": [ + "# Heterogeneous Causal Learning for Effectiveness Optimization" + ] + }, + { + "cell_type": "markdown", + "id": "b841ee2c", + "metadata": {}, + "source": [ + "- 기존 uplift 모델은 이질적 처치 효과를 추정할 수 있지만, 비용 대비 이익을 충분히 반영하지 못합니다.\n", + "\n", + "- 마케팅처럼 예산이 제한된 환경에서는, 비용을 고려하면서 효과를 최대화하는 처치 효과 최적화(treatment effect optimization) 접근이 필요합니다.\n", + "\n", + "- 이를 위해 다음 알고리즘들을 활용합니다.\n", + " - Duality R-learner\n", + " - Direct Ranking Model (DRM)\n", + " - Constrained Ranking Models" + ] + }, + { + "cell_type": "markdown", + "id": "21447276", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0f91658c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip -q install scikit-uplift" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9114f7da", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import Ridge, LogisticRegression\n", + "from sklearn.metrics import r2_score, roc_auc_score\n", + "\n", + "from sklift.datasets import fetch_hillstrom\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "RANDOM_STATE = 42\n", + "pd.set_option(\"display.max_columns\", 50)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "57cbb979", + "metadata": {}, + "source": [ + "### Hillstrom E-mail Test Dataset\n", + "\n", + "Kevin Hillstrom E-mail Test Dataset을 사용합니다. \n", + "이 데이터는 e-mail 마케팅 A/B/n 테스트 로그입니다.\n", + "\n", + "- **Treatment**: ${T}$\n", + " - `Mens E-Mail`, `Womens E-Mail` $\\Rightarrow$ ${T = 1}$ (이메일 발송)\n", + " - `No E-Mail` $\\Rightarrow$ ${T = 0}$ (대조군)\n", + "\n", + "- **Gain outcome**: ${Y^r}$\n", + " - 2주간 지출 금액 `spend`\n", + " - “이메일을 보내면 spend가 얼마나 증가하는가?” 가 관심\n", + "\n", + "- **Cost outcome**: ${Y^c}$\n", + " - 이메일 발송 1회당 비용을 1 단위로 단순화\n", + " - 따라서 $Y^c = T \\in \\{0,1\\}$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b2b3d7a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data shape: (64000, 8)\n", + "\n", + "spend (target) describe:\n", + "count 64000.000000\n", + "mean 1.050908\n", + "std 15.036448\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 499.000000\n", + "Name: spend, dtype: float64\n", + "\n", + "segment (treatment_raw) 분포:\n", + "segment\n", + "Womens E-Mail 0.334172\n", + "Mens E-Mail 0.332922\n", + "No E-Mail 0.332906\n", + "Name: proportion, dtype: float64\n" + ] + }, + { + "data": { + "text/html": [ + "
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recencyhistory_segmenthistorymenswomenszip_codenewbiechannel
0102) $100 - $200142.4410Surburban0Phone
163) $200 - $350329.0811Rural1Web
272) $100 - $200180.6501Surburban1Web
395) $500 - $750675.8310Rural1Web
421) $0 - $10045.3410Urban0Web
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" + ], + "text/plain": [ + " recency history_segment history mens womens zip_code newbie channel\n", + "0 10 2) $100 - $200 142.44 1 0 Surburban 0 Phone\n", + "1 6 3) $200 - $350 329.08 1 1 Rural 1 Web\n", + "2 7 2) $100 - $200 180.65 0 1 Surburban 1 Web\n", + "3 9 5) $500 - $750 675.83 1 0 Rural 1 Web\n", + "4 2 1) $0 - $100 45.34 1 0 Urban 0 Web" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = fetch_hillstrom(target_col=\"spend\", return_X_y_t=False)\n", + "\n", + "data = dataset.data.copy() # X (features, 아직 전처리 전)\n", + "y_gain = dataset.target.copy() # Y^r = spend\n", + "treatment_raw = dataset.treatment.copy() # 'Mens E-Mail', 'Womens E-Mail', 'No E-Mail'\n", + "\n", + "print(\"data shape:\", data.shape)\n", + "print(\"\\nspend (target) describe:\")\n", + "print(y_gain.describe())\n", + "\n", + "print(\"\\nsegment (treatment_raw) 분포:\")\n", + "print(treatment_raw.value_counts(normalize=True))\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2ff956f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Treatment 비율 (T=1): 0.66709375\n", + "\n", + "Y_gain (spend) 요약:\n", + "count 64000.000000\n", + "mean 1.050908\n", + "std 15.036448\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 499.000000\n", + "Name: spend, dtype: float64\n", + "\n", + "Y_cost 분포:\n", + "segment\n", + "1.0 0.667094\n", + "0.0 0.332906\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "T = (treatment_raw != \"No E-Mail\").astype(int) # 이메일 받았으면 1, 아니면 0\n", + "\n", + "Y_gain = y_gain.astype(float) # spend (float)\n", + "Y_cost = T.astype(float) # 이메일 발송 비용 (0/1)\n", + "\n", + "print(\"Treatment 비율 (T=1):\", T.mean())\n", + "print(\"\\nY_gain (spend) 요약:\")\n", + "print(pd.Series(Y_gain).describe())\n", + "\n", + "print(\"\\nY_cost 분포:\")\n", + "print(pd.Series(Y_cost).value_counts(normalize=True).rename(\"proportion\"))\n" + ] + }, + { + "cell_type": "markdown", + "id": "bfdbc4fd", + "metadata": {}, + "source": [ + "### Feature 전처리\n", + "\n", + "Hillstrom의 주요 feature 예시:\n", + "\n", + "- `recency`, `history`, `mens`, `womens`, `newbie` 등: 숫자/0-1 변수\n", + "- `history_segment`, `zip_code`, `channel`: 범주형\n", + "\n", + "R-learner / Propensity 모델에 넣기 위해\n", + "\n", + "- 숫자형 컬럼은 그대로 사용하고,\n", + "- 범주형 컬럼(`history_segment`, `zip_code`, `channel`)은 one-hot 인코딩으로 변환합니다.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e286adf5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "원본 feature columns:\n", + "['recency', 'history_segment', 'history', 'mens', 'womens', 'zip_code', 'newbie', 'channel']\n", + "\n", + "Numeric columns:\n", + "['recency', 'history', 'mens', 'womens', 'newbie']\n", + "\n", + "Categorical columns (one-hot 대상):\n", + "['history_segment', 'zip_code', 'channel']\n", + "\n", + "전처리 후 feature shape: (64000, 15)\n", + "전처리된 feature columns:\n", + "Index(['recency', 'history', 'mens', 'womens', 'newbie',\n", + " 'history_segment_2) $100 - $200', 'history_segment_3) $200 - $350',\n", + " 'history_segment_4) $350 - $500', 'history_segment_5) $500 - $750',\n", + " 'history_segment_6) $750 - $1,000', 'history_segment_7) $1,000 +',\n", + " 'zip_code_Surburban', 'zip_code_Urban', 'channel_Phone', 'channel_Web'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(\"원본 feature columns:\")\n", + "print(data.columns.tolist())\n", + "\n", + "# one-hot 대상 범주형 컬럼\n", + "categorical_cols = [\"history_segment\", \"zip_code\", \"channel\"]\n", + "\n", + "# 나머지는 숫자/0-1 컬럼으로 그대로 사용\n", + "numeric_cols = [c for c in data.columns if c not in categorical_cols]\n", + "\n", + "print(\"\\nNumeric columns:\")\n", + "print(numeric_cols)\n", + "print(\"\\nCategorical columns (one-hot 대상):\")\n", + "print(categorical_cols)\n", + "\n", + "# one-hot 인코딩\n", + "X_cat = pd.get_dummies(data[categorical_cols], drop_first=True)\n", + "X_num = data[numeric_cols].reset_index(drop=True)\n", + "\n", + "X_df = pd.concat([X_num, X_cat], axis=1)\n", + "\n", + "print(\"\\n전처리 후 feature shape:\", X_df.shape)\n", + "print(\"전처리된 feature columns:\")\n", + "print(X_df.columns)\n", + "\n", + "# numpy array로 변환\n", + "X = X_df.values.astype(np.float32)\n" + ] + }, + { + "cell_type": "markdown", + "id": "eeb08865", + "metadata": {}, + "source": [ + "데이터 세트는 각각 60%, 20%, 20%의 비율로 학습, 검증 및 테스트 세트의 3부분으로 나뉩니다." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3d964183", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train shape: (38400, 15)\n", + "Val shape: (12800, 15)\n", + "Test shape: (12800, 15)\n", + "\n", + "Treatment 비율 (Train/Val/Test):\n", + "Train: 0.6670833333333334\n", + "Val : 0.667109375\n", + "Test : 0.667109375\n" + ] + } + ], + "source": [ + "# Train / Validation / Test 분할\n", + "X_train_val, X_test, T_train_val, T_test, Yg_train_val, Yg_test, Yc_train_val, Yc_test = train_test_split(\n", + " X, T, Y_gain, Y_cost,\n", + " test_size=0.2,\n", + " random_state=RANDOM_STATE,\n", + " stratify=T,\n", + ")\n", + "\n", + "X_train, X_val, T_train, T_val, Yg_train, Yg_val, Yc_train, Yc_val = train_test_split(\n", + " X_train_val, T_train_val, Yg_train_val, Yc_train_val,\n", + " test_size=0.25, # 0.25 * 0.8 = 0.2\n", + " random_state=RANDOM_STATE,\n", + " stratify=T_train_val,\n", + ")\n", + "\n", + "print(\"Train shape:\", X_train.shape)\n", + "print(\"Val shape:\", X_val.shape)\n", + "print(\"Test shape:\", X_test.shape)\n", + "\n", + "print(\"\\nTreatment 비율 (Train/Val/Test):\")\n", + "print(\"Train:\", T_train.mean())\n", + "print(\"Val :\", T_val.mean())\n", + "print(\"Test :\", T_test.mean())\n" + ] + }, + { + "cell_type": "markdown", + "id": "576b8d00", + "metadata": {}, + "source": [ + "## Duality R-learner\n", + "\n", + "Duality R-learner는 다음 두 단계를 결합한 방식입니다.\n", + "\n", + "1. R-learner로 Gain/Cost CATE 추정\n", + " - $\\tau_r(x)$: gain uplift (예: spend uplift)\n", + " - $\\tau_c(x)$: cost uplift (예: 이메일 발송 비용 증가량)\n", + "\n", + "2. 예산 제약(budget constraint)을 듀얼 형태로 최적화\n", + " - 라그랑지 승수 $\\lambda$ 를 학습하여 최적 정책을 찾습니다.\n", + "\n", + "우리가 풀고 싶은 문제는 다음과 같습니다.\n", + "\n", + "- 예산 $B$ 하에서\n", + "\n", + " $$\n", + " \\max_{z_i \\in \\{0,1\\}} \\sum_i \\tau_r(x^{(i)}) z_i\n", + " \\quad \\text{s.t.} \\quad\n", + " \\sum_i \\tau_c(x^{(i)}) z_i \\le B\n", + " $$\n", + " \n", + "- $z_i = 1$ 이면 고객 $i$ 에게 이메일 발송, $z_i = 0$ 이면 미발송\n", + "\n", + "Duality R-learner 핵심 단계:\n", + "\n", + "1. Nuisance models: $m_r(x)$, $e(x)$ 학습 \n", + "2. Gain / Cost R-learner: $\\tau_r(x)$, $\\tau_c(x)$ 추정 \n", + "3. Duality: $\\lambda$ 를 gradient ascent 로 최적화 \n", + "4. 정책 생성: $s(x) = \\tau_r(x) - \\lambda^* \\tau_c(x)$\n", + "5. Cost Curve / AUCC 로 정책 성능 평가 " + ] + }, + { + "cell_type": "markdown", + "id": "5d9e213b", + "metadata": {}, + "source": [ + "### 1. Nuisance Models: $m_r(x)$ 와 $e(x)$\n", + "\n", + "R-learner는 아래 식을 기반으로 합니다.\n", + "\n", + "$$\n", + "Y - m^*(X)\n", + "= (T - e^*(X))\\,\\tau^*(X) + \\epsilon\n", + "$$\n", + "\n", + "여기서 \n", + "- $m^*(X) = \\mathbb{E}[Y \\mid X]$: outcome 평균 \n", + "- $e^*(X) = \\mathbb{P}(T=1 \\mid X)$: propensity score \n", + "\n", + "Gain outcome에 대한 nuisance 모델은 다음과 같이 구성합니다.\n", + "\n", + "- $m_r(x)$: Ridge 회귀 \n", + "- $e(x)$: Logistic 회귀 \n", + "\n", + "Cost outcome은 $Y^c = T$ 이므로\n", + "$m_c(x) = e(x)$" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3e718594", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== m_r(x) 성능 (R^2: spend 회귀) ==\n", + "Train R^2: 0.0011321804124860835\n", + "Val R^2: 0.0006200906912602333\n", + "\n", + "예측값 분포 (Val):\n", + "count 12800.000000\n", + "mean 0.998539\n", + "std 0.499257\n", + "min -4.757108\n", + "25% 0.690851\n", + "50% 0.961950\n", + "75% 1.234435\n", + "max 4.417754\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# Gain outcome 평균 모델 m_r(x): Ridge 회귀\n", + "m_r = Ridge(alpha=1.0, random_state=RANDOM_STATE)\n", + "m_r.fit(X_train, Yg_train)\n", + "\n", + "Yg_pred_train = m_r.predict(X_train)\n", + "Yg_pred_val = m_r.predict(X_val)\n", + "\n", + "r2_train = r2_score(Yg_train, Yg_pred_train)\n", + "r2_val = r2_score(Yg_val, Yg_pred_val)\n", + "\n", + "print(\"== m_r(x) 성능 (R^2: spend 회귀) ==\")\n", + "print(\"Train R^2:\", r2_train)\n", + "print(\"Val R^2:\", r2_val)\n", + "\n", + "print(\"\\n예측값 분포 (Val):\")\n", + "print(pd.Series(Yg_pred_val).describe())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1aa8d52b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== e(x) 성능 (AUC: treatment 모델) ==\n", + "Train AUC: 0.5117265124259399\n", + "Val AUC: 0.49799591470904553\n", + "\n", + "Propensity e(x) range:\n", + "Train: 0.633101626124968 → 0.8026040280233658\n", + "Val : 0.6331449838328645 → 0.8183494704982611\n" + ] + } + ], + "source": [ + "# Propensity model e(x) = P(T=1 | X): Logistic 회귀\n", + "propensity = LogisticRegression(\n", + " penalty=\"l2\",\n", + " C=1.0,\n", + " solver=\"lbfgs\",\n", + " max_iter=1000,\n", + " n_jobs=-1,\n", + ")\n", + "\n", + "propensity.fit(X_train, T_train)\n", + "\n", + "e_train = propensity.predict_proba(X_train)[:, 1]\n", + "e_val = propensity.predict_proba(X_val)[:, 1]\n", + "e_test = propensity.predict_proba(X_test)[:, 1]\n", + "\n", + "auc_train_e = roc_auc_score(T_train, e_train)\n", + "auc_val_e = roc_auc_score(T_val, e_val)\n", + "\n", + "print(\"== e(x) 성능 (AUC: treatment 모델) ==\")\n", + "print(\"Train AUC:\", auc_train_e)\n", + "print(\"Val AUC:\", auc_val_e)\n", + "\n", + "print(\"\\nPropensity e(x) range:\")\n", + "print(\"Train:\", e_train.min(), \"→\", e_train.max())\n", + "print(\"Val :\", e_val.min(), \"→\", e_val.max())\n" + ] + }, + { + "cell_type": "markdown", + "id": "a2b17bd8", + "metadata": {}, + "source": [ + "여기서 얻은 ${e(x)}$ 는 이후에\n", + "\n", + "- Gain R-learner에서 ${T - e(x)}$ 항을 만들 때,\n", + "- Cost R-learner에서 ${m_c(x)}$ 로도 재사용합니다. " + ] + }, + { + "cell_type": "markdown", + "id": "06b5558e", + "metadata": {}, + "source": [ + "### 2. Gain R-learner: $\\tau_r(x)$\n", + "\n", + "Gain outcome $Y^r$ 에 대해 R-learner 구조는 다음과 같습니다.\n", + "\n", + "$$\n", + "Y^r - m_r(X)\n", + "= (T - e(X))\\,\\tau_r(X) + \\epsilon\n", + "$$\n", + "\n", + "선형 모델 $\\tau_r(x) = w_r^\\top x$ 를 사용하면 학습 절차는 다음과 같습니다.\n", + "\n", + "1. 잔차 계산 \n", + "\n", + " $$\n", + " r^Y = Y^r - \\hat m_r(X), \\quad r^T = T - \\hat e(X)\n", + " $$\n", + "\n", + "2. 행별 스케일링 \n", + "\n", + " $$\n", + " Z = X \\odot r^T\n", + " $$\n", + "\n", + "3. 회귀 \n", + "\n", + " $$\n", + " r^Y \\approx Z w_r\n", + " $$\n", + "\n", + "4. 최종 CATE \n", + "\n", + " $$\n", + " \\hat\\tau_r(x) = w_r^\\top x\n", + " $$" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "28eb5e7c", + "metadata": {}, + "outputs": [], + "source": [ + "def fit_r_learner_linear(\n", + " X_tr, X_val,\n", + " T_tr, T_val,\n", + " Y_tr, Y_val,\n", + " m_tr, m_val,\n", + " e_tr, e_val,\n", + " alpha=1.0,\n", + " name=\"R-learner\",\n", + "):\n", + " \"\"\"\n", + " 선형 τ(x) = w^T x 를 R-learner 방식으로 학습.\n", + " - X_tr, X_val: feature 행렬\n", + " - T_tr, T_val: treatment (0/1)\n", + " - Y_tr, Y_val: outcome\n", + " - m_tr, m_val: m(x) = E[Y|X] 예측값\n", + " - e_tr, e_val: e(x) = P(T=1|X) 예측값\n", + " \"\"\"\n", + " X_tr = np.asarray(X_tr)\n", + " X_val = np.asarray(X_val)\n", + " T_tr = np.asarray(T_tr).astype(float)\n", + " T_val = np.asarray(T_val).astype(float)\n", + " Y_tr = np.asarray(Y_tr).astype(float)\n", + " Y_val = np.asarray(Y_val).astype(float)\n", + " m_tr = np.asarray(m_tr).astype(float)\n", + " m_val = np.asarray(m_val).astype(float)\n", + " e_tr = np.asarray(e_tr).astype(float)\n", + " e_val = np.asarray(e_val).astype(float)\n", + "\n", + " # residuals\n", + " rY_tr = Y_tr - m_tr\n", + " rT_tr = T_tr - e_tr\n", + "\n", + " # Z = X * rT (각 행을 rT로 스케일링)\n", + " Z_tr = X_tr * rT_tr.reshape(-1, 1)\n", + "\n", + " # 회귀: rY ~ Z\n", + " tau_model = Ridge(alpha=alpha, fit_intercept=False, random_state=RANDOM_STATE)\n", + " tau_model.fit(Z_tr, rY_tr)\n", + "\n", + " # τ_hat(x) = w^T x\n", + " tau_tr = tau_model.predict(X_tr)\n", + " tau_val = tau_model.predict(X_val)\n", + "\n", + " print(f\"== {name} 요약 ==\")\n", + " print(\"Train τ_hat summary:\")\n", + " print(pd.Series(tau_tr).describe())\n", + " print(\"\\nVal τ_hat summary:\")\n", + " print(pd.Series(tau_val).describe())\n", + "\n", + " return tau_model, tau_tr, tau_val\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "03fc2e68", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Gain R-learner τ_r(x) 요약 ==\n", + "Train τ_hat summary:\n", + "count 38400.000000\n", + "mean 0.562616\n", + "std 0.454530\n", + "min -2.669276\n", + "25% 0.265676\n", + "50% 0.528041\n", + "75% 0.837171\n", + "max 1.976344\n", + "dtype: float64\n", + "\n", + "Val τ_hat summary:\n", + "count 12800.000000\n", + "mean 0.567048\n", + "std 0.453703\n", + "min -3.313805\n", + "25% 0.272715\n", + "50% 0.530470\n", + "75% 0.836859\n", + "max 1.944813\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# m_r(x) 예측값\n", + "m_r_train = m_r.predict(X_train)\n", + "m_r_val = m_r.predict(X_val)\n", + "\n", + "tau_r_model, tau_r_train, tau_r_val = fit_r_learner_linear(\n", + " X_tr=X_train,\n", + " X_val=X_val,\n", + " T_tr=T_train,\n", + " T_val=T_val,\n", + " Y_tr=Yg_train,\n", + " Y_val=Yg_val,\n", + " m_tr=m_r_train,\n", + " m_val=m_r_val,\n", + " e_tr=e_train,\n", + " e_val=e_val,\n", + " alpha=1.0,\n", + " name=\"Gain R-learner τ_r(x)\",\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e8ae0ccd", + "metadata": {}, + "source": [ + "### 3. Cost R-learner: $\\tau_c(x)$\n", + "\n", + "Cost outcome은 $Y^c = T$ 이므로 \n", + "nuisance model은 이미\n", + "\n", + "$$m_c(x) = e(x)$$\n", + "\n", + "입니다.\n", + "\n", + "Cost R-learner 식은\n", + "\n", + "$$\n", + "Y^c - m_c(X)\n", + "= (T - e(X))\\,\\tau_c(X)\n", + "$$\n", + "\n", + "Gain과 동일한 R-learner 구조로 $\\tau_c(x)$ 를 학습합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f40867a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Cost R-learner τ_c(x) 요약 ==\n", + "Train τ_hat summary:\n", + "count 38400.000000\n", + "mean 0.974710\n", + "std 0.156975\n", + "min 0.429515\n", + "25% 0.894193\n", + "50% 0.978889\n", + "75% 1.046973\n", + "max 2.098633\n", + "dtype: float64\n", + "\n", + "Val τ_hat summary:\n", + "count 12800.000000\n", + "mean 0.974875\n", + "std 0.157403\n", + "min 0.424263\n", + "25% 0.894096\n", + "50% 0.979144\n", + "75% 1.046372\n", + "max 2.265868\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# Cost outcome 평균 m_c(x)는 e(x)를 그대로 사용\n", + "m_c_train = e_train\n", + "m_c_val = e_val\n", + "\n", + "tau_c_model, tau_c_train, tau_c_val = fit_r_learner_linear(\n", + " X_tr=X_train,\n", + " X_val=X_val,\n", + " T_tr=T_train,\n", + " T_val=T_val,\n", + " Y_tr=Yc_train,\n", + " Y_val=Yc_val,\n", + " m_tr=m_c_train,\n", + " m_val=m_c_val,\n", + " e_tr=e_train,\n", + " e_val=e_val,\n", + " alpha=1.0,\n", + " name=\"Cost R-learner τ_c(x)\",\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "48f3ae13", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== τ_r(x) 요약 ==\n", + "[Train]\n", + "count 38400.000000\n", + "mean 0.562616\n", + "std 0.454530\n", + "min -2.669276\n", + "25% 0.265676\n", + "50% 0.528041\n", + "75% 0.837171\n", + "max 1.976344\n", + "dtype: float64\n", + "\n", + "[Val]\n", + "count 12800.000000\n", + "mean 0.567048\n", + "std 0.453703\n", + "min -3.313805\n", + "25% 0.272715\n", + "50% 0.530470\n", + "75% 0.836859\n", + "max 1.944813\n", + "dtype: float64\n", + "\n", + "[Test]\n", + "count 12800.000000\n", + "mean 0.568442\n", + "std 0.450854\n", + "min -2.486865\n", + "25% 0.268170\n", + "50% 0.534959\n", + "75% 0.841292\n", + "max 1.970332\n", + "dtype: float64\n", + "\n", + "== τ_c(x) 요약 ==\n", + "[Train]\n", + "count 38400.000000\n", + "mean 0.974710\n", + "std 0.156975\n", + "min 0.429515\n", + "25% 0.894193\n", + "50% 0.978889\n", + "75% 1.046973\n", + "max 2.098633\n", + "dtype: float64\n", + "\n", + "[Val]\n", + "count 12800.000000\n", + "mean 0.974875\n", + "std 0.157403\n", + "min 0.424263\n", + "25% 0.894096\n", + "50% 0.979144\n", + "75% 1.046372\n", + "max 2.265868\n", + "dtype: float64\n", + "\n", + "[Test]\n", + "count 12800.000000\n", + "mean 0.975139\n", + "std 0.155813\n", + "min 0.436810\n", + "25% 0.895776\n", + "50% 0.978849\n", + "75% 1.045682\n", + "max 1.803177\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# Test set CATE 예측\n", + "tau_r_test = tau_r_model.predict(X_test)\n", + "tau_c_test = tau_c_model.predict(X_test)\n", + "\n", + "print(\"== τ_r(x) 요약 ==\")\n", + "print(\"[Train]\")\n", + "print(pd.Series(tau_r_train).describe())\n", + "print(\"\\n[Val]\")\n", + "print(pd.Series(tau_r_val).describe())\n", + "print(\"\\n[Test]\")\n", + "print(pd.Series(tau_r_test).describe())\n", + "\n", + "print(\"\\n== τ_c(x) 요약 ==\")\n", + "print(\"[Train]\")\n", + "print(pd.Series(tau_c_train).describe())\n", + "print(\"\\n[Val]\")\n", + "print(pd.Series(tau_c_val).describe())\n", + "print(\"\\n[Test]\")\n", + "print(pd.Series(tau_c_test).describe())\n" + ] + }, + { + "cell_type": "markdown", + "id": "e6e387a2", + "metadata": {}, + "source": [ + "### 4. Duality: 예산 제약 하에서 라그랑지안 기반 $\\lambda$ 최적화\n", + "\n", + "우리가 풀고자 하는 문제는 다음과 같습니다.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\max_{z_i \\in \\{0,1\\}} &\\quad \\sum_i \\tau_r(x^{(i)}) z_i \\\\\n", + "\\text{s.t.} &\\quad \\sum_i \\tau_c(x^{(i)}) z_i \\le B.\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "여기서 $z_i = 1$ 은 고객 $i$를 타겟팅하는 경우이며, $B$는 전체 예산입니다. \n", + "이 제약을 다루기 위해 라그랑지 승수 $\\lambda \\ge 0$ 를 도입하면 라그랑지안은\n", + "\n", + "$$\n", + "L(z,\\lambda)\n", + "= -\\sum_i \\tau_r(x^{(i)}) z_i\n", + "+ \\lambda\\left(\\sum_i \\tau_c(x^{(i)}) z_i - B\\right)\n", + "$$\n", + "\n", + "으로 표현됩니다.\n", + "\n", + "고정된 $\\lambda$ 아래에서 고객 $i$의 효율성 점수는 다음과 같습니다.\n", + "\n", + "$$\n", + "s_i(\\lambda) = \\tau_r(x^{(i)}) - \\lambda\\, \\tau_c(x^{(i)}).\n", + "$$\n", + "\n", + "점수가 양수이면 타겟팅하는 것이 유리하므로 \n", + "$s_i(\\lambda) \\ge 0$ 이면 $z_i = 1$, 음수이면 $z_i = 0$ 을 선택합니다. \n", + "즉, $\\lambda$가 주어지면 단순히 $s_i(\\lambda)$가 양수인 고객만 선택하면 됩니다.\n", + "\n", + "듀얼 목적함수의 기울기는\n", + "\n", + "$$\n", + "\\frac{\\partial g}{\\partial \\lambda}\n", + "\\approx \\sum_i z_i \\tau_c(x^{(i)}) - B\n", + "$$\n", + "\n", + "으로 근사할 수 있고, 이에 따른 gradient ascent 업데이트는\n", + "\n", + "$$\n", + "\\lambda \\leftarrow \\bigl[\\lambda + \\eta(\\text{cost\\_used} - B)\\bigr]_+\n", + "$$\n", + "\n", + "로 진행됩니다. 여기서 $[\\cdot]_+$ 는 $\\lambda$가 음수가 되지 않도록 하는 projection입니다.\n", + "\n", + "예산을 초과하면 $(\\text{cost\\_used} > B)$ $\\lambda$는 증가하여 비용 효과를 더 강하게 억제하고, \n", + "예산보다 적게 사용하면 $\\lambda$는 감소하여 더 많은 고객이 선택될 수 있도록 조정됩니다.\n", + "\n", + "Train 데이터에서 양의 Cost CATE 합을 기반으로 예산 $B$를 설정하고, \n", + "위 규칙을 반복 적용하여 최종 $\\lambda^*$와 정책을 학습합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5fe3e686", + "metadata": {}, + "outputs": [], + "source": [ + "def duality_learn_lambda(\n", + " tau_r,\n", + " tau_c,\n", + " budget_fraction=0.3,\n", + " lr=1e-5,\n", + " n_iter=200,\n", + " verbose_every=20,\n", + "):\n", + " \"\"\"\n", + " τ_r, τ_c 가 주어졌을 때 Duality gradient ascent로 λ 학습.\n", + " - budget_fraction: 전체 양의 cost effect 합 중 몇 %를 예산으로 둘지\n", + " \"\"\"\n", + " tau_r = np.asarray(tau_r).astype(float)\n", + " tau_c = np.asarray(tau_c).astype(float)\n", + "\n", + " # 양의 cost effect만 예산 계산에 사용\n", + " tau_c_pos = np.clip(tau_c, a_min=0.0, a_max=None)\n", + " total_pos_cost = tau_c_pos.sum()\n", + " B = budget_fraction * total_pos_cost\n", + "\n", + " lam = 0.0\n", + "\n", + " for it in range(n_iter + 1):\n", + " # effectiveness score\n", + " s = tau_r - lam * tau_c\n", + "\n", + " # z_i: 선택 여부 (s_i >= 0 이면 선택)\n", + " z = (s >= 0).astype(float)\n", + "\n", + " cost_used = (tau_c_pos * z).sum()\n", + " gain_used = (np.clip(tau_r, 0.0, None) * z).sum()\n", + "\n", + " # ∂g/∂λ ≈ cost_used - B\n", + " grad = cost_used - B\n", + "\n", + " # gradient ascent (λ >= 0 유지)\n", + " lam = max(0.0, lam + lr * grad)\n", + "\n", + " if it % verbose_every == 0:\n", + " sel_ratio = z.mean()\n", + " print(\n", + " f\"[iter {it:03d}] λ={lam:.6f}, \"\n", + " f\"cost_used={cost_used:.4f}, gain_used={gain_used:.4f}, \"\n", + " f\"grad={grad:.4f}, selected={sel_ratio:.3f}\"\n", + " )\n", + "\n", + " print(\"\\n최종 λ*:\", lam)\n", + " print(\"총 양의 cost effect 합:\", total_pos_cost)\n", + " print(f\"예산 B (fraction={budget_fraction}):\", B)\n", + " return lam, B\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "233a6a45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[iter 000] λ=0.228487, cost_used=34077.3618, gain_used=22363.6067, grad=22848.7019, selected=0.907\n", + "[iter 020] λ=0.784009, cost_used=11251.8179, gain_used=12501.7237, grad=23.1579, selected=0.298\n", + "[iter 040] λ=0.784586, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", + "[iter 060] λ=0.784587, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", + "[iter 080] λ=0.784587, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", + "[iter 100] λ=0.784587, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", + "[iter 120] λ=0.784588, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", + "[iter 140] λ=0.784588, cost_used=11229.1281, gain_used=12483.9314, grad=0.4682, selected=0.297\n", + "[iter 160] λ=0.784588, cost_used=11229.1281, gain_used=12483.9314, grad=0.4682, selected=0.297\n", + "[iter 180] λ=0.784589, cost_used=11229.1281, gain_used=12483.9314, grad=0.4682, selected=0.297\n", + "[iter 200] λ=0.784589, cost_used=11229.1281, gain_used=12483.9314, grad=0.4682, selected=0.297\n", + "\n", + "최종 λ*: 0.7845891317539325\n", + "총 양의 cost effect 합: 37428.86642372066\n", + "예산 B (fraction=0.3): 11228.659927116198\n" + ] + } + ], + "source": [ + "lambda_star, B = duality_learn_lambda(\n", + " tau_r=tau_r_train,\n", + " tau_c=tau_c_train,\n", + " budget_fraction=0.3,\n", + " lr=1e-5,\n", + " n_iter=200,\n", + " verbose_every=20,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a54ac8cf", + "metadata": {}, + "outputs": [], + "source": [ + "def selection_summary(tau_r, tau_c, lam, name=\"\"):\n", + " tau_r = np.asarray(tau_r).astype(float)\n", + " tau_c = np.asarray(tau_c).astype(float)\n", + "\n", + " s = tau_r - lam * tau_c\n", + " z = (s >= 0).astype(float)\n", + "\n", + " gain_pos = np.clip(tau_r, 0.0, None)\n", + " cost_pos = np.clip(tau_c, 0.0, None)\n", + "\n", + " gain_used = (gain_pos * z).sum()\n", + " cost_used = (cost_pos * z).sum()\n", + " sel_ratio = z.mean()\n", + "\n", + " ratio = gain_used / cost_used if cost_used > 0 else np.nan\n", + "\n", + " print(f\"\\n== Selection summary ({name}) ==\")\n", + " print(f\"λ = {lam:.6f}\")\n", + " print(f\"선택 비율: {sel_ratio:.3f} ({z.sum():.0f} / {len(z)})\")\n", + " print(f\"총 gain (∑ τ_r^+ z): {gain_used:.4f}\")\n", + " print(f\"총 cost (∑ τ_c^+ z): {cost_used:.4f}\")\n", + " print(f\"gain / cost 비율: {ratio:.4f}\")\n", + "\n", + " return {\n", + " \"lambda\": lam,\n", + " \"selected_ratio\": sel_ratio,\n", + " \"gain_used\": gain_used,\n", + " \"cost_used\": cost_used,\n", + " \"gain_per_cost\": ratio,\n", + " }\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6947da6a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "== Selection summary (Train) ==\n", + "λ = 0.784589\n", + "선택 비율: 0.297 (11420 / 38400)\n", + "총 gain (∑ τ_r^+ z): 12483.2661\n", + "총 cost (∑ τ_c^+ z): 11228.2803\n", + "gain / cost 비율: 1.1118\n", + "\n", + "== Selection summary (Val) ==\n", + "λ = 0.784589\n", + "선택 비율: 0.293 (3753 / 12800)\n", + "총 gain (∑ τ_r^+ z): 4124.4445\n", + "총 cost (∑ τ_c^+ z): 3685.4466\n", + "gain / cost 비율: 1.1191\n", + "\n", + "== Selection summary (Test) ==\n", + "λ = 0.784589\n", + "선택 비율: 0.302 (3862 / 12800)\n", + "총 gain (∑ τ_r^+ z): 4210.7541\n", + "총 cost (∑ τ_c^+ z): 3794.4139\n", + "gain / cost 비율: 1.1097\n" + ] + } + ], + "source": [ + "_ = selection_summary(tau_r_train, tau_c_train, lambda_star, name=\"Train\")\n", + "_ = selection_summary(tau_r_val, tau_c_val, lambda_star, name=\"Val\")\n", + "_ = selection_summary(tau_r_test, tau_c_test, lambda_star, name=\"Test\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ec8b309", + "metadata": {}, + "source": [ + "### 5. Cost Curve & AUCC\n", + "\n", + "Cost Curve 와 그 면적(AUCC, Area Under Cost Curve)로 비용 대비 uplift 모델을 평가합니다.\n", + "\n", + "Test 셋에서:\n", + "\n", + "1. Duality 점수 ${s(x) = \\tau_r(x) - \\lambda^* \\tau_c(x)}$ 기준으로 내림차순 정렬\n", + "2. 정렬된 순서대로\n", + " - ${\\tau_r^+(x) = \\max(\\tau_r(x), 0)}$\n", + " - ${\\tau_c^+(x) = \\max(\\tau_c(x), 0)}$\n", + " 의 누적합 계산\n", + "3. 누적 cost/gain 을 각각 최종값으로 나누어 ${[0,1]}$ 범위로 정규화\n", + "4. $(0,0)$ 에서 $(1,1)$ 까지 이어지는 곡선을 Cost Curve 로 사용\n", + "5. 수치 적분으로 AUCC 계산:\n", + " $$\n", + " \\text{AUCC} = \\int_0^1 \\text{gain}(x)\\,dx\n", + " $$\n", + "\n", + "비교를 위해 랜덤 ranking 의 Cost Curve 와 AUCC 도 함께 계산합니다.\n", + "\n", + "- AUCC ${\\approx 0.5}$: 랜덤에 가까운 정책\n", + "- AUCC ${>} 0.5$: 효율적인 고객부터 잘 고르는 정책\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "56c9cc3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Test set Cost Curve (τ 기반) ==\n", + "max_cost: 12481.778185597159\n", + "max_gain: 7511.766716461641\n", + "Normalized AUCC: 0.6946670819574676\n" + ] + } + ], + "source": [ + "# Duality R-learner 기반 effectiveness score (Test set)\n", + "s_test = tau_r_test - lambda_star * tau_c_test\n", + "\n", + "# score 기준 내림차순 정렬\n", + "order = np.argsort(-s_test)\n", + "tau_r_sorted = np.clip(tau_r_test[order], 0.0, None) # gain은 양수 부분만\n", + "tau_c_sorted = np.clip(tau_c_test[order], 0.0, None) # cost도 양수 부분만\n", + "\n", + "# 누적 cost / gain\n", + "cum_cost = np.cumsum(tau_c_sorted)\n", + "cum_gain = np.cumsum(tau_r_sorted)\n", + "\n", + "# 0 지점 포함\n", + "cum_cost = np.insert(cum_cost, 0, 0.0)\n", + "cum_gain = np.insert(cum_gain, 0, 0.0)\n", + "\n", + "# 정규화\n", + "max_cost = cum_cost[-1]\n", + "max_gain = cum_gain[-1]\n", + "\n", + "x = cum_cost / max_cost\n", + "y = cum_gain / max_gain\n", + "\n", + "# AUCC 계산\n", + "aucc = np.trapz(y, x)\n", + "\n", + "print(\"== Test set Cost Curve (τ 기반) ==\")\n", + "print(\"max_cost:\", max_cost)\n", + "print(\"max_gain:\", max_gain)\n", + "print(\"Normalized AUCC:\", aucc)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3b6badd9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random ranking AUCC: 0.5006872435398204\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 랜덤 베이스라인 Cost Curve\n", + "rng = np.random.default_rng(RANDOM_STATE)\n", + "perm = rng.permutation(len(tau_r_test))\n", + "\n", + "tau_r_rand = np.clip(tau_r_test[perm], 0.0, None)\n", + "tau_c_rand = np.clip(tau_c_test[perm], 0.0, None)\n", + "\n", + "cum_cost_rand = np.cumsum(tau_c_rand)\n", + "cum_gain_rand = np.cumsum(tau_r_rand)\n", + "\n", + "cum_cost_rand = np.insert(cum_cost_rand, 0, 0.0)\n", + "cum_gain_rand = np.insert(cum_gain_rand, 0, 0.0)\n", + "\n", + "x_rand = cum_cost_rand / cum_cost_rand[-1]\n", + "y_rand = cum_gain_rand / cum_gain_rand[-1]\n", + "\n", + "aucc_rand = np.trapz(y_rand, x_rand)\n", + "print(\"Random ranking AUCC:\", aucc_rand)\n", + "\n", + "# 플롯\n", + "plt.figure(figsize=(6, 5))\n", + "plt.plot(x, y, label=f\"Duality R-learner (AUCC={aucc:.3f})\")\n", + "plt.plot(x_rand, y_rand, linestyle=\"--\", label=f\"Random (AUCC={aucc_rand:.3f})\")\n", + "plt.plot([0, 1], [0, 1], alpha=0.4, linewidth=1, label=\"y=x reference\")\n", + "\n", + "plt.xlabel(\"Cumulative cost / max\")\n", + "plt.ylabel(\"Cumulative gain / max\")\n", + "plt.title(\"Cost curve on Test set (τ-based)\")\n", + "plt.legend()\n", + "plt.grid(alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "965eecec", + "metadata": {}, + "outputs": [], + "source": [ + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 8ee2699f7e179d5f0f83e42557e26ddbaabb0ce4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?= Date: Sun, 14 Dec 2025 17:49:30 +0900 Subject: [PATCH 3/5] fix: dataset and aucc logic --- ...rning_for_effectiveness_optimization.ipynb | 1225 +++++++++-------- book/prescriptive_analytics/overview.md | 4 +- 2 files changed, 652 insertions(+), 577 deletions(-) diff --git a/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb b/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb index c5b0644..0082571 100644 --- a/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb +++ b/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb @@ -47,24 +47,69 @@ } ], "source": [ - "%pip -q install scikit-uplift" + "%pip -q install fractional-uplift" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "9114f7da", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "

🌲 Try YDF, the successor of\n", + " TensorFlow\n", + " Decision Forests using the same algorithms but with more features and faster\n", + " training!\n", + "

\n", + "
\n", + "
\n", + " \n", + " Old code

\n", + " 
\n",
+       "import tensorflow_decision_forests as tfdf\n",
+       "\n",
+       "tf_ds = tfdf.keras.pd_dataframe_to_tf_dataset(ds, label=\"l\")\n",
+       "model = tfdf.keras.RandomForestModel(label=\"l\")\n",
+       "model.fit(tf_ds)\n",
+       "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " New code

\n", + " 
\n",
+       "import ydf\n",
+       "\n",
+       "model = ydf.RandomForestLearner(label=\"l\").train(ds)\n",
+       "
\n", + "
\n", + "
\n", + "

(Learn more in the migration\n", + " guide)

\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", - "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import Ridge, LogisticRegression\n", "from sklearn.metrics import r2_score, roc_auc_score\n", "\n", - "from sklift.datasets import fetch_hillstrom\n", + "import fractional_uplift as fr \n", "\n", "import matplotlib.pyplot as plt\n", "\n", @@ -80,27 +125,33 @@ "id": "57cbb979", "metadata": {}, "source": [ - "### Hillstrom E-mail Test Dataset\n", + "### CriteoWithSyntheticCostAndSpend Dataset\n", "\n", - "Kevin Hillstrom E-mail Test Dataset을 사용합니다. \n", - "이 데이터는 e-mail 마케팅 A/B/n 테스트 로그입니다.\n", "\n", - "- **Treatment**: ${T}$\n", - " - `Mens E-Mail`, `Womens E-Mail` $\\Rightarrow$ ${T = 1}$ (이메일 발송)\n", - " - `No E-Mail` $\\Rightarrow$ ${T = 0}$ (대조군)\n", + "- CriteoWithSyntheticCostAndSpend 데이터는\n", + " - treatment: 광고 노출 여부 (0/1)\n", + " - spend: 고객이 발생시킨 매출(이익)\n", + " - cost: 해당 고객에게 treatment를 줬을 때 발생한 고객별 비용\n", "\n", - "- **Gain outcome**: ${Y^r}$\n", - " - 2주간 지출 금액 `spend`\n", - " - “이메일을 보내면 spend가 얼마나 증가하는가?” 가 관심\n", + " 을 모두 포함하므로, 비용까지 고려한 처치 최적화 실험에 적합합니다. \n", "\n", - "- **Cost outcome**: ${Y^c}$\n", - " - 이메일 발송 1회당 비용을 1 단위로 단순화\n", - " - 따라서 $Y^c = T \\in \\{0,1\\}$\n" + "- 세 가지 DataFrame으로 구성\n", + " - `train_data`\n", + " - `distill_data` (여기서는 validation 역할로 사용)\n", + " - `test_data`\n", + "\n", + "- 주요 컬럼\n", + " - `treatment`: 광고/프로모션 노출 여부 (0/1)\n", + " - `spend`: 사용자가 발생시킨 매출(이익) → Gain outcome: $(Y^r)$\n", + " - `cost`: 해당 고객에게 treatment를 줄 때 들어간 비용 → Cost outcome: $(Y^c)$\n", + " - `treatment_propensity`: 실험에서 treatment에 할당될 확률\n", + " - `sample_weight`: 샘플 가중치\n", + " - `criteo.features`: feature 컬럼 이름 리스트 (문자열 리스트)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "b2b3d7a2", "metadata": {}, "outputs": [ @@ -108,25 +159,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "data shape: (64000, 8)\n", + "Train shape: (72053, 19)\n", + "Val shape: (17774, 19)\n", + "Test shape: (20333, 19)\n", "\n", - "spend (target) describe:\n", - "count 64000.000000\n", - "mean 1.050908\n", - "std 15.036448\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 499.000000\n", - "Name: spend, dtype: float64\n", + "Feature columns: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11']\n", "\n", - "segment (treatment_raw) 분포:\n", - "segment\n", - "Womens E-Mail 0.334172\n", - "Mens E-Mail 0.332922\n", - "No E-Mail 0.332906\n", - "Name: proportion, dtype: float64\n" + "Train head:\n" ] }, { @@ -150,150 +189,234 @@ " \n", " \n", " \n", - " recency\n", - " history_segment\n", - " history\n", - " mens\n", - " womens\n", - " zip_code\n", - " newbie\n", - " channel\n", + " f0\n", + " f1\n", + " f2\n", + " f3\n", + " f4\n", + " f5\n", + " f6\n", + " f7\n", + " f8\n", + " f9\n", + " f10\n", + " f11\n", + " treatment\n", + " conversion\n", + " treatment_propensity\n", + " cost_percentage\n", + " spend\n", + " cost\n", + " sample_weight\n", " \n", " \n", " \n", " \n", - " 0\n", - " 10\n", - " 2) $100 - $200\n", - " 142.44\n", + " 44\n", + " 12.616365\n", + " 10.059654\n", + " 8.964588\n", + " 4.679882\n", + " 10.280525\n", + " 4.115453\n", + " 0.294443\n", + " 4.833815\n", + " 3.955396\n", + " 13.190056\n", + " 5.300375\n", + " -0.168679\n", " 1\n", " 0\n", - " Surburban\n", - " 0\n", - " Phone\n", + " 0.85\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 100.0\n", " \n", " \n", - " 1\n", - " 6\n", - " 3) $200 - $350\n", - " 329.08\n", - " 1\n", + " 187\n", + " 12.616365\n", + " 10.059654\n", + " 8.904597\n", + " 4.679882\n", + " 10.280525\n", + " 4.115453\n", + " 0.294443\n", + " 4.833815\n", + " 3.955396\n", + " 13.190056\n", + " 5.300375\n", + " -0.168679\n", " 1\n", - " Rural\n", - " 1\n", - " Web\n", + " 0\n", + " 0.85\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 100.0\n", " \n", " \n", - " 2\n", - " 7\n", - " 2) $100 - $200\n", - " 180.65\n", - " 0\n", + " 484\n", + " 22.377238\n", + " 10.059654\n", + " 8.214383\n", + " 4.679882\n", + " 10.280525\n", + " 4.115453\n", + " -2.411115\n", + " 4.833815\n", + " 3.971858\n", + " 13.190056\n", + " 5.300375\n", + " -0.168679\n", " 1\n", - " Surburban\n", - " 1\n", - " Web\n", + " 0\n", + " 0.85\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 100.0\n", " \n", " \n", - " 3\n", - " 9\n", - " 5) $500 - $750\n", - " 675.83\n", + " 528\n", + " 12.616365\n", + " 10.059654\n", + " 8.350682\n", + " 4.679882\n", + " 10.280525\n", + " 4.115453\n", + " 0.294443\n", + " 4.833815\n", + " 3.955396\n", + " 16.226044\n", + " 5.300375\n", + " -0.168679\n", " 1\n", " 0\n", - " Rural\n", - " 1\n", - " Web\n", + " 0.85\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 100.0\n", " \n", " \n", - " 4\n", - " 2\n", - " 1) $0 - $100\n", - " 45.34\n", + " 1108\n", + " 14.617627\n", + " 10.059654\n", + " 8.489929\n", + " 3.907662\n", + " 13.253813\n", + " 4.115453\n", + " -2.411115\n", + " 4.833815\n", + " 3.809530\n", + " 42.176324\n", + " 5.737292\n", + " -0.560340\n", " 1\n", - " 0\n", - " Urban\n", - " 0\n", - " Web\n", + " 1\n", + " 0.85\n", + " 0.090777\n", + " 36.459294\n", + " 3.309655\n", + " 1.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " recency history_segment history mens womens zip_code newbie channel\n", - "0 10 2) $100 - $200 142.44 1 0 Surburban 0 Phone\n", - "1 6 3) $200 - $350 329.08 1 1 Rural 1 Web\n", - "2 7 2) $100 - $200 180.65 0 1 Surburban 1 Web\n", - "3 9 5) $500 - $750 675.83 1 0 Rural 1 Web\n", - "4 2 1) $0 - $100 45.34 1 0 Urban 0 Web" + " f0 f1 f2 f3 f4 f5 f6 \\\n", + "44 12.616365 10.059654 8.964588 4.679882 10.280525 4.115453 0.294443 \n", + "187 12.616365 10.059654 8.904597 4.679882 10.280525 4.115453 0.294443 \n", + "484 22.377238 10.059654 8.214383 4.679882 10.280525 4.115453 -2.411115 \n", + "528 12.616365 10.059654 8.350682 4.679882 10.280525 4.115453 0.294443 \n", + "1108 14.617627 10.059654 8.489929 3.907662 13.253813 4.115453 -2.411115 \n", + "\n", + " f7 f8 f9 f10 f11 treatment \\\n", + "44 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n", + "187 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n", + "484 4.833815 3.971858 13.190056 5.300375 -0.168679 1 \n", + "528 4.833815 3.955396 16.226044 5.300375 -0.168679 1 \n", + "1108 4.833815 3.809530 42.176324 5.737292 -0.560340 1 \n", + "\n", + " conversion treatment_propensity cost_percentage spend cost \\\n", + "44 0 0.85 0.000000 0.000000 0.000000 \n", + "187 0 0.85 0.000000 0.000000 0.000000 \n", + "484 0 0.85 0.000000 0.000000 0.000000 \n", + "528 0 0.85 0.000000 0.000000 0.000000 \n", + "1108 1 0.85 0.090777 36.459294 3.309655 \n", + "\n", + " sample_weight \n", + "44 100.0 \n", + "187 100.0 \n", + "484 100.0 \n", + "528 100.0 \n", + "1108 1.0 " ] }, - "execution_count": 11, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dataset = fetch_hillstrom(target_col=\"spend\", return_X_y_t=False)\n", + "criteo = fr.example_data.CriteoWithSyntheticCostAndSpend.load()\n", "\n", - "data = dataset.data.copy() # X (features, 아직 전처리 전)\n", - "y_gain = dataset.target.copy() # Y^r = spend\n", - "treatment_raw = dataset.treatment.copy() # 'Mens E-Mail', 'Womens E-Mail', 'No E-Mail'\n", + "train_df = criteo.train_data.copy()\n", + "val_df = criteo.distill_data.copy() # distill_data를 validation 데이터로 사용\n", + "test_df = criteo.test_data.copy()\n", + "features = criteo.features # feature column 리스트\n", "\n", - "print(\"data shape:\", data.shape)\n", - "print(\"\\nspend (target) describe:\")\n", - "print(y_gain.describe())\n", + "print(\"Train shape:\", train_df.shape)\n", + "print(\"Val shape:\", val_df.shape)\n", + "print(\"Test shape:\", test_df.shape)\n", + "print(\"\\nFeature columns:\", features)\n", "\n", - "print(\"\\nsegment (treatment_raw) 분포:\")\n", - "print(treatment_raw.value_counts(normalize=True))\n", - "\n", - "data.head()" + "print(\"\\nTrain head:\")\n", + "train_df.head()\n" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "2ff956f2", + "execution_count": 3, + "id": "5c9e7a68", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Treatment 비율 (T=1): 0.66709375\n", - "\n", - "Y_gain (spend) 요약:\n", - "count 64000.000000\n", - "mean 1.050908\n", - "std 15.036448\n", + "treatment 비율: 0.8611161228540103\n", + "spend describe:\n", + "count 72053.000000\n", + "mean 7.638117\n", + "std 15.380174\n", "min 0.000000\n", "25% 0.000000\n", "50% 0.000000\n", "75% 0.000000\n", - "max 499.000000\n", + "max 172.747528\n", "Name: spend, dtype: float64\n", - "\n", - "Y_cost 분포:\n", - "segment\n", - "1.0 0.667094\n", - "0.0 0.332906\n", - "Name: proportion, dtype: float64\n" + "cost describe:\n", + "count 72053.000000\n", + "mean 2.558094\n", + "std 7.596816\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 63.162000\n", + "Name: cost, dtype: float64\n" ] } ], "source": [ - "T = (treatment_raw != \"No E-Mail\").astype(int) # 이메일 받았으면 1, 아니면 0\n", - "\n", - "Y_gain = y_gain.astype(float) # spend (float)\n", - "Y_cost = T.astype(float) # 이메일 발송 비용 (0/1)\n", - "\n", - "print(\"Treatment 비율 (T=1):\", T.mean())\n", - "print(\"\\nY_gain (spend) 요약:\")\n", - "print(pd.Series(Y_gain).describe())\n", - "\n", - "print(\"\\nY_cost 분포:\")\n", - "print(pd.Series(Y_cost).value_counts(normalize=True).rename(\"proportion\"))\n" + "print(\"treatment 비율:\", train_df[\"treatment\"].mean())\n", + "print(\"spend describe:\")\n", + "print(train_df[\"spend\"].describe())\n", + "print(\"cost describe:\")\n", + "print(train_df[\"cost\"].describe())" ] }, { @@ -301,22 +424,25 @@ "id": "bfdbc4fd", "metadata": {}, "source": [ - "### Feature 전처리\n", - "\n", - "Hillstrom의 주요 feature 예시:\n", + "### Feature 행렬 & 타겟 정의\n", "\n", - "- `recency`, `history`, `mens`, `womens`, `newbie` 등: 숫자/0-1 변수\n", - "- `history_segment`, `zip_code`, `channel`: 범주형\n", + "- $X$: `features` 컬럼들\n", + "- $T$: `treatment` (0/1)\n", + "- $Y^r$: `spend` (gain)\n", + "- $Y^c$: `cost` (cost)\n", "\n", - "R-learner / Propensity 모델에 넣기 위해\n", + "여기서는:\n", "\n", - "- 숫자형 컬럼은 그대로 사용하고,\n", - "- 범주형 컬럼(`history_segment`, `zip_code`, `channel`)은 one-hot 인코딩으로 변환합니다.\n" + "- 데이터셋이 이미 `train / distill / test`로 나뉘어 있으므로,\n", + " - `train_df` → train\n", + " - `val_df` → validation\n", + " - `test_df` → test\n", + " 로 그대로 사용합니다.\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "id": "e286adf5", "metadata": {}, "outputs": [ @@ -324,108 +450,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "원본 feature columns:\n", - "['recency', 'history_segment', 'history', 'mens', 'womens', 'zip_code', 'newbie', 'channel']\n", - "\n", - "Numeric columns:\n", - "['recency', 'history', 'mens', 'womens', 'newbie']\n", - "\n", - "Categorical columns (one-hot 대상):\n", - "['history_segment', 'zip_code', 'channel']\n", - "\n", - "전처리 후 feature shape: (64000, 15)\n", - "전처리된 feature columns:\n", - "Index(['recency', 'history', 'mens', 'womens', 'newbie',\n", - " 'history_segment_2) $100 - $200', 'history_segment_3) $200 - $350',\n", - " 'history_segment_4) $350 - $500', 'history_segment_5) $500 - $750',\n", - " 'history_segment_6) $750 - $1,000', 'history_segment_7) $1,000 +',\n", - " 'zip_code_Surburban', 'zip_code_Urban', 'channel_Phone', 'channel_Web'],\n", - " dtype='object')\n" + "X_train shape: (72053, 12)\n", + "X_val shape: (17774, 12)\n", + "X_test shape: (20333, 12)\n" ] } ], "source": [ - "print(\"원본 feature columns:\")\n", - "print(data.columns.tolist())\n", - "\n", - "# one-hot 대상 범주형 컬럼\n", - "categorical_cols = [\"history_segment\", \"zip_code\", \"channel\"]\n", - "\n", - "# 나머지는 숫자/0-1 컬럼으로 그대로 사용\n", - "numeric_cols = [c for c in data.columns if c not in categorical_cols]\n", + "X_train = train_df[features].values.astype(np.float32)\n", + "X_val = val_df[features].values.astype(np.float32)\n", + "X_test = test_df[features].values.astype(np.float32)\n", "\n", - "print(\"\\nNumeric columns:\")\n", - "print(numeric_cols)\n", - "print(\"\\nCategorical columns (one-hot 대상):\")\n", - "print(categorical_cols)\n", + "T_train = train_df[\"treatment\"].values.astype(int)\n", + "T_val = val_df[\"treatment\"].values.astype(int)\n", + "T_test = test_df[\"treatment\"].values.astype(int)\n", "\n", - "# one-hot 인코딩\n", - "X_cat = pd.get_dummies(data[categorical_cols], drop_first=True)\n", - "X_num = data[numeric_cols].reset_index(drop=True)\n", + "Yg_train = train_df[\"spend\"].values.astype(float) # gain\n", + "Yg_val = val_df[\"spend\"].values.astype(float)\n", + "Yg_test = test_df[\"spend\"].values.astype(float)\n", "\n", - "X_df = pd.concat([X_num, X_cat], axis=1)\n", + "Yc_train = train_df[\"cost\"].values.astype(float) # cost\n", + "Yc_val = val_df[\"cost\"].values.astype(float)\n", + "Yc_test = test_df[\"cost\"].values.astype(float)\n", "\n", - "print(\"\\n전처리 후 feature shape:\", X_df.shape)\n", - "print(\"전처리된 feature columns:\")\n", - "print(X_df.columns)\n", - "\n", - "# numpy array로 변환\n", - "X = X_df.values.astype(np.float32)\n" - ] - }, - { - "cell_type": "markdown", - "id": "eeb08865", - "metadata": {}, - "source": [ - "데이터 세트는 각각 60%, 20%, 20%의 비율로 학습, 검증 및 테스트 세트의 3부분으로 나뉩니다." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3d964183", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train shape: (38400, 15)\n", - "Val shape: (12800, 15)\n", - "Test shape: (12800, 15)\n", - "\n", - "Treatment 비율 (Train/Val/Test):\n", - "Train: 0.6670833333333334\n", - "Val : 0.667109375\n", - "Test : 0.667109375\n" - ] - } - ], - "source": [ - "# Train / Validation / Test 분할\n", - "X_train_val, X_test, T_train_val, T_test, Yg_train_val, Yg_test, Yc_train_val, Yc_test = train_test_split(\n", - " X, T, Y_gain, Y_cost,\n", - " test_size=0.2,\n", - " random_state=RANDOM_STATE,\n", - " stratify=T,\n", - ")\n", - "\n", - "X_train, X_val, T_train, T_val, Yg_train, Yg_val, Yc_train, Yc_val = train_test_split(\n", - " X_train_val, T_train_val, Yg_train_val, Yc_train_val,\n", - " test_size=0.25, # 0.25 * 0.8 = 0.2\n", - " random_state=RANDOM_STATE,\n", - " stratify=T_train_val,\n", - ")\n", - "\n", - "print(\"Train shape:\", X_train.shape)\n", - "print(\"Val shape:\", X_val.shape)\n", - "print(\"Test shape:\", X_test.shape)\n", - "\n", - "print(\"\\nTreatment 비율 (Train/Val/Test):\")\n", - "print(\"Train:\", T_train.mean())\n", - "print(\"Val :\", T_val.mean())\n", - "print(\"Test :\", T_test.mean())\n" + "print(\"X_train shape:\", X_train.shape)\n", + "print(\"X_val shape:\", X_val.shape)\n", + "print(\"X_test shape:\", X_test.shape)" ] }, { @@ -438,8 +488,8 @@ "Duality R-learner는 다음 두 단계를 결합한 방식입니다.\n", "\n", "1. R-learner로 Gain/Cost CATE 추정\n", - " - $\\tau_r(x)$: gain uplift (예: spend uplift)\n", - " - $\\tau_c(x)$: cost uplift (예: 이메일 발송 비용 증가량)\n", + " - $\\tau_r(x)$: gain uplift\n", + " - $\\tau_c(x)$: cost uplift\n", "\n", "2. 예산 제약(budget constraint)을 듀얼 형태로 최적화\n", " - 라그랑지 승수 $\\lambda$ 를 학습하여 최적 정책을 찾습니다.\n", @@ -453,8 +503,8 @@ " \\quad \\text{s.t.} \\quad\n", " \\sum_i \\tau_c(x^{(i)}) z_i \\le B\n", " $$\n", - " \n", - "- $z_i = 1$ 이면 고객 $i$ 에게 이메일 발송, $z_i = 0$ 이면 미발송\n", + "\n", + "- $z_i = 1$ 이면 고객 $i$ 에게 프로모션/광고를 집행, $z_i = 0$ 이면 미집행\n", "\n", "Duality R-learner 핵심 단계:\n", "\n", @@ -462,7 +512,7 @@ "2. Gain / Cost R-learner: $\\tau_r(x)$, $\\tau_c(x)$ 추정 \n", "3. Duality: $\\lambda$ 를 gradient ascent 로 최적화 \n", "4. 정책 생성: $s(x) = \\tau_r(x) - \\lambda^* \\tau_c(x)$\n", - "5. Cost Curve / AUCC 로 정책 성능 평가 " + "5. Cost Curve / AUCC 로 정책 성능 평가 \n" ] }, { @@ -480,21 +530,26 @@ "$$\n", "\n", "여기서 \n", - "- $m^*(X) = \\mathbb{E}[Y \\mid X]$: outcome 평균 \n", + "\n", + "- $m^*(X) = \\mathbb{E}[Y \\mid X]$: outcome 평균 모델 \n", "- $e^*(X) = \\mathbb{P}(T=1 \\mid X)$: propensity score \n", "\n", - "Gain outcome에 대한 nuisance 모델은 다음과 같이 구성합니다.\n", + "Criteo 셋에서는 gain과 cost가 모두 연속값이므로 \n", + "각각 독립적인 회귀모델을 쓰는 것이 자연스럽습니다.\n", "\n", - "- $m_r(x)$: Ridge 회귀 \n", - "- $e(x)$: Logistic 회귀 \n", + "- Gain outcome $Y^r = \\texttt{spend}$\n", + " - $m_r(x) = \\mathbb{E}[Y^r\\mid X=x]$: Ridge 회귀\n", "\n", - "Cost outcome은 $Y^c = T$ 이므로\n", - "$m_c(x) = e(x)$" + "- Cost outcome $Y^c = \\texttt{cost}$\n", + " - $m_c(x) = \\mathbb{E}[Y^c\\mid X=x]$: Ridge 회귀\n", + "\n", + "- Treatment model\n", + " - $e(x) = \\mathbb{P}(T=1\\mid X=x)$: Logistic 회귀" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "3e718594", "metadata": {}, "outputs": [ @@ -503,18 +558,18 @@ "output_type": "stream", "text": [ "== m_r(x) 성능 (R^2: spend 회귀) ==\n", - "Train R^2: 0.0011321804124860835\n", - "Val R^2: 0.0006200906912602333\n", + "Train R^2: 0.5991714831471346\n", + "Val R^2: 0.6034863154274288\n", "\n", "예측값 분포 (Val):\n", - "count 12800.000000\n", - "mean 0.998539\n", - "std 0.499257\n", - "min -4.757108\n", - "25% 0.690851\n", - "50% 0.961950\n", - "75% 1.234435\n", - "max 4.417754\n", + "count 17774.000000\n", + "mean 7.529438\n", + "std 11.912387\n", + "min -5.214262\n", + "25% -0.278099\n", + "50% 1.335855\n", + "75% 12.749807\n", + "max 81.783020\n", "dtype: float64\n" ] } @@ -527,20 +582,66 @@ "Yg_pred_train = m_r.predict(X_train)\n", "Yg_pred_val = m_r.predict(X_val)\n", "\n", - "r2_train = r2_score(Yg_train, Yg_pred_train)\n", - "r2_val = r2_score(Yg_val, Yg_pred_val)\n", + "r2_train_mr = r2_score(Yg_train, Yg_pred_train)\n", + "r2_val_mr = r2_score(Yg_val, Yg_pred_val)\n", "\n", "print(\"== m_r(x) 성능 (R^2: spend 회귀) ==\")\n", - "print(\"Train R^2:\", r2_train)\n", - "print(\"Val R^2:\", r2_val)\n", + "print(\"Train R^2:\", r2_train_mr)\n", + "print(\"Val R^2:\", r2_val_mr)\n", "\n", "print(\"\\n예측값 분포 (Val):\")\n", - "print(pd.Series(Yg_pred_val).describe())\n" + "print(pd.Series(Yg_pred_val).describe())" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, + "id": "083c0aa5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== m_c(x) 성능 (R^2: cost 회귀) ==\n", + "Train R^2: 0.2841092845226817\n", + "Val R^2: 0.29156262859724946\n", + "\n", + "예측값 분포 (Val):\n", + "count 17774.000000\n", + "mean 2.524266\n", + "std 4.070278\n", + "min -24.999222\n", + "25% -0.114549\n", + "50% 0.894167\n", + "75% 4.298206\n", + "max 23.168346\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# Cost outcome 평균 모델 m_c(x): Ridge 회귀 (cost 전용 모델)\n", + "m_c = Ridge(alpha=1.0, random_state=RANDOM_STATE)\n", + "m_c.fit(X_train, Yc_train)\n", + "\n", + "Yc_pred_train = m_c.predict(X_train)\n", + "Yc_pred_val = m_c.predict(X_val)\n", + "\n", + "r2_train_mc = r2_score(Yc_train, Yc_pred_train)\n", + "r2_val_mc = r2_score(Yc_val, Yc_pred_val)\n", + "\n", + "print(\"== m_c(x) 성능 (R^2: cost 회귀) ==\")\n", + "print(\"Train R^2:\", r2_train_mc)\n", + "print(\"Val R^2:\", r2_val_mc)\n", + "\n", + "print(\"\\n예측값 분포 (Val):\")\n", + "print(pd.Series(Yc_pred_val).describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "1aa8d52b", "metadata": {}, "outputs": [ @@ -549,12 +650,12 @@ "output_type": "stream", "text": [ "== e(x) 성능 (AUC: treatment 모델) ==\n", - "Train AUC: 0.5117265124259399\n", - "Val AUC: 0.49799591470904553\n", + "Train AUC: 0.5432668234321524\n", + "Val AUC: 0.5470811168626702\n", "\n", "Propensity e(x) range:\n", - "Train: 0.633101626124968 → 0.8026040280233658\n", - "Val : 0.6331449838328645 → 0.8183494704982611\n" + "Train: 0.8259820462034405 → 0.9497856673846461\n", + "Val : 0.8258405409868536 → 0.9441271638409029\n" ] } ], @@ -583,18 +684,7 @@ "\n", "print(\"\\nPropensity e(x) range:\")\n", "print(\"Train:\", e_train.min(), \"→\", e_train.max())\n", - "print(\"Val :\", e_val.min(), \"→\", e_val.max())\n" - ] - }, - { - "cell_type": "markdown", - "id": "a2b17bd8", - "metadata": {}, - "source": [ - "여기서 얻은 ${e(x)}$ 는 이후에\n", - "\n", - "- Gain R-learner에서 ${T - e(x)}$ 항을 만들 때,\n", - "- Cost R-learner에서 ${m_c(x)}$ 로도 재사용합니다. " + "print(\"Val :\", e_val.min(), \"→\", e_val.max())" ] }, { @@ -602,45 +692,44 @@ "id": "06b5558e", "metadata": {}, "source": [ - "### 2. Gain R-learner: $\\tau_r(x)$\n", + "### 2. R-learner: Gain / Cost CATE 추정\n", "\n", "Gain outcome $Y^r$ 에 대해 R-learner 구조는 다음과 같습니다.\n", "\n", "$$\n", - "Y^r - m_r(X)\n", - "= (T - e(X))\\,\\tau_r(X) + \\epsilon\n", + "Y - m(X) = (T - e(X))\\,\\tau(X) + \\epsilon\n", "$$\n", "\n", - "선형 모델 $\\tau_r(x) = w_r^\\top x$ 를 사용하면 학습 절차는 다음과 같습니다.\n", - "\n", - "1. 잔차 계산 \n", + "선형 모델 $\\tau(x) = w^\\top x$ 를 쓰면:\n", "\n", + "1. **잔차 계산**\n", " $$\n", - " r^Y = Y^r - \\hat m_r(X), \\quad r^T = T - \\hat e(X)\n", + " r^Y = Y - \\hat m(X), \\quad r^T = T - \\hat e(X)\n", " $$\n", - "\n", - "2. 행별 스케일링 \n", - "\n", + "2. **행 단위 스케일링**\n", " $$\n", " Z = X \\odot r^T\n", " $$\n", - "\n", - "3. 회귀 \n", - "\n", + "3. **회귀**\n", + " $$\n", + " r^Y \\approx Z w\n", + " $$\n", + "4. **최종 CATE**\n", " $$\n", - " r^Y \\approx Z w_r\n", + " \\hat\\tau(x) = w^\\top x\n", " $$\n", "\n", - "4. 최종 CATE \n", + "이를 공통 함수로 구현하고,\n", "\n", - " $$\n", - " \\hat\\tau_r(x) = w_r^\\top x\n", - " $$" + "- Gain R-learner: $Y = Y^r$, $m = m_r$\n", + "- Cost R-learner: $Y = Y^c$, $m = m_c$\n", + "\n", + "로 각각 학습합니다." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "id": "28eb5e7c", "metadata": {}, "outputs": [], @@ -658,7 +747,7 @@ " 선형 τ(x) = w^T x 를 R-learner 방식으로 학습.\n", " - X_tr, X_val: feature 행렬\n", " - T_tr, T_val: treatment (0/1)\n", - " - Y_tr, Y_val: outcome\n", + " - Y_tr, Y_val: outcome (gain or cost)\n", " - m_tr, m_val: m(x) = E[Y|X] 예측값\n", " - e_tr, e_val: e(x) = P(T=1|X) 예측값\n", " \"\"\"\n", @@ -694,12 +783,12 @@ " print(\"\\nVal τ_hat summary:\")\n", " print(pd.Series(tau_val).describe())\n", "\n", - " return tau_model, tau_tr, tau_val\n" + " return tau_model, tau_tr, tau_val" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 11, "id": "03fc2e68", "metadata": {}, "outputs": [ @@ -709,31 +798,31 @@ "text": [ "== Gain R-learner τ_r(x) 요약 ==\n", "Train τ_hat summary:\n", - "count 38400.000000\n", - "mean 0.562616\n", - "std 0.454530\n", - "min -2.669276\n", - "25% 0.265676\n", - "50% 0.528041\n", - "75% 0.837171\n", - "max 1.976344\n", + "count 72053.000000\n", + "mean 0.869965\n", + "std 4.075175\n", + "min -16.366707\n", + "25% -0.214938\n", + "50% 0.119879\n", + "75% 1.240913\n", + "max 74.068790\n", "dtype: float64\n", "\n", "Val τ_hat summary:\n", - "count 12800.000000\n", - "mean 0.567048\n", - "std 0.453703\n", - "min -3.313805\n", - "25% 0.272715\n", - "50% 0.530470\n", - "75% 0.836859\n", - "max 1.944813\n", + "count 17774.000000\n", + "mean 0.847950\n", + "std 4.047337\n", + "min -13.205720\n", + "25% -0.215324\n", + "50% 0.116316\n", + "75% 1.209327\n", + "max 66.496148\n", "dtype: float64\n" ] } ], "source": [ - "# m_r(x) 예측값\n", + "# Gain R-learner: τ_r(x)\n", "m_r_train = m_r.predict(X_train)\n", "m_r_val = m_r.predict(X_val)\n", "\n", @@ -750,37 +839,13 @@ " e_val=e_val,\n", " alpha=1.0,\n", " name=\"Gain R-learner τ_r(x)\",\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "e8ae0ccd", - "metadata": {}, - "source": [ - "### 3. Cost R-learner: $\\tau_c(x)$\n", - "\n", - "Cost outcome은 $Y^c = T$ 이므로 \n", - "nuisance model은 이미\n", - "\n", - "$$m_c(x) = e(x)$$\n", - "\n", - "입니다.\n", - "\n", - "Cost R-learner 식은\n", - "\n", - "$$\n", - "Y^c - m_c(X)\n", - "= (T - e(X))\\,\\tau_c(X)\n", - "$$\n", - "\n", - "Gain과 동일한 R-learner 구조로 $\\tau_c(x)$ 를 학습합니다." + ")" ] }, { "cell_type": "code", - "execution_count": 21, - "id": "f40867a2", + "execution_count": 12, + "id": "554cb0c4", "metadata": {}, "outputs": [ { @@ -789,33 +854,33 @@ "text": [ "== Cost R-learner τ_c(x) 요약 ==\n", "Train τ_hat summary:\n", - "count 38400.000000\n", - "mean 0.974710\n", - "std 0.156975\n", - "min 0.429515\n", - "25% 0.894193\n", - "50% 0.978889\n", - "75% 1.046973\n", - "max 2.098633\n", + "count 72053.000000\n", + "mean 2.873521\n", + "std 4.356113\n", + "min -18.526381\n", + "25% -0.005015\n", + "50% 0.903932\n", + "75% 4.806126\n", + "max 29.188446\n", "dtype: float64\n", "\n", "Val τ_hat summary:\n", - "count 12800.000000\n", - "mean 0.974875\n", - "std 0.157403\n", - "min 0.424263\n", - "25% 0.894096\n", - "50% 0.979144\n", - "75% 1.046372\n", - "max 2.265868\n", + "count 17774.000000\n", + "mean 2.838168\n", + "std 4.375784\n", + "min -28.228684\n", + "25% -0.024353\n", + "50% 0.862495\n", + "75% 4.697582\n", + "max 26.297773\n", "dtype: float64\n" ] } ], "source": [ - "# Cost outcome 평균 m_c(x)는 e(x)를 그대로 사용\n", - "m_c_train = e_train\n", - "m_c_val = e_val\n", + "# Cost R-learner: τ_c(x)\n", + "m_c_train = m_c.predict(X_train)\n", + "m_c_val = m_c.predict(X_val)\n", "\n", "tau_c_model, tau_c_train, tau_c_val = fit_r_learner_linear(\n", " X_tr=X_train,\n", @@ -830,13 +895,13 @@ " e_val=e_val,\n", " alpha=1.0,\n", " name=\"Cost R-learner τ_c(x)\",\n", - ")\n" + ")" ] }, { "cell_type": "code", - "execution_count": 22, - "id": "48f3ae13", + "execution_count": 13, + "id": "01798a5e", "metadata": {}, "outputs": [ { @@ -845,70 +910,70 @@ "text": [ "== τ_r(x) 요약 ==\n", "[Train]\n", - "count 38400.000000\n", - "mean 0.562616\n", - "std 0.454530\n", - "min -2.669276\n", - "25% 0.265676\n", - "50% 0.528041\n", - "75% 0.837171\n", - "max 1.976344\n", + "count 72053.000000\n", + "mean 0.869965\n", + "std 4.075175\n", + "min -16.366707\n", + "25% -0.214938\n", + "50% 0.119879\n", + "75% 1.240913\n", + "max 74.068790\n", "dtype: float64\n", "\n", "[Val]\n", - "count 12800.000000\n", - "mean 0.567048\n", - "std 0.453703\n", - "min -3.313805\n", - "25% 0.272715\n", - "50% 0.530470\n", - "75% 0.836859\n", - "max 1.944813\n", + "count 17774.000000\n", + "mean 0.847950\n", + "std 4.047337\n", + "min -13.205720\n", + "25% -0.215324\n", + "50% 0.116316\n", + "75% 1.209327\n", + "max 66.496148\n", "dtype: float64\n", "\n", "[Test]\n", - "count 12800.000000\n", - "mean 0.568442\n", - "std 0.450854\n", - "min -2.486865\n", - "25% 0.268170\n", - "50% 0.534959\n", - "75% 0.841292\n", - "max 1.970332\n", + "count 20333.000000\n", + "mean 2.168109\n", + "std 7.207469\n", + "min -13.727579\n", + "25% -1.233063\n", + "50% 0.928363\n", + "75% 3.340883\n", + "max 76.512638\n", "dtype: float64\n", "\n", "== τ_c(x) 요약 ==\n", "[Train]\n", - "count 38400.000000\n", - "mean 0.974710\n", - "std 0.156975\n", - "min 0.429515\n", - "25% 0.894193\n", - "50% 0.978889\n", - "75% 1.046973\n", - "max 2.098633\n", + "count 72053.000000\n", + "mean 2.873521\n", + "std 4.356113\n", + "min -18.526381\n", + "25% -0.005015\n", + "50% 0.903932\n", + "75% 4.806126\n", + "max 29.188446\n", "dtype: float64\n", "\n", "[Val]\n", - "count 12800.000000\n", - "mean 0.974875\n", - "std 0.157403\n", - "min 0.424263\n", - "25% 0.894096\n", - "50% 0.979144\n", - "75% 1.046372\n", - "max 2.265868\n", + "count 17774.000000\n", + "mean 2.838168\n", + "std 4.375784\n", + "min -28.228684\n", + "25% -0.024353\n", + "50% 0.862495\n", + "75% 4.697582\n", + "max 26.297773\n", "dtype: float64\n", "\n", "[Test]\n", - "count 12800.000000\n", - "mean 0.975139\n", - "std 0.155813\n", - "min 0.436810\n", - "25% 0.895776\n", - "50% 0.978849\n", - "75% 1.045682\n", - "max 1.803177\n", + "count 20333.000000\n", + "mean 8.090442\n", + "std 4.941765\n", + "min -17.154167\n", + "25% 4.801457\n", + "50% 7.960261\n", + "75% 11.408073\n", + "max 26.761067\n", "dtype: float64\n" ] } @@ -940,63 +1005,57 @@ "id": "e6e387a2", "metadata": {}, "source": [ - "### 4. Duality: 예산 제약 하에서 라그랑지안 기반 $\\lambda$ 최적화\n", + "### 3. Duality: 예산 제약 하에서 $\\lambda$ 최적화\n", "\n", - "우리가 풀고자 하는 문제는 다음과 같습니다.\n", + "목표는 다음과 같습니다.\n", "\n", "$$\n", "\\begin{aligned}\n", - "\\max_{z_i \\in \\{0,1\\}} &\\quad \\sum_i \\tau_r(x^{(i)}) z_i \\\\\n", - "\\text{s.t.} &\\quad \\sum_i \\tau_c(x^{(i)}) z_i \\le B.\n", + "\\max_{z_i \\in \\{0,1\\}}\\quad & \\sum_i \\tau_r(x^{(i)}) z_i \\\\\n", + "\\text{s.t.}\\quad & \\sum_i \\tau_c(x^{(i)}) z_i \\le B\n", "\\end{aligned}\n", "$$\n", "\n", - "여기서 $z_i = 1$ 은 고객 $i$를 타겟팅하는 경우이며, $B$는 전체 예산입니다. \n", - "이 제약을 다루기 위해 라그랑지 승수 $\\lambda \\ge 0$ 를 도입하면 라그랑지안은\n", + "- $z_i = 1$: 고객 $i$ 타깃 (프로모션 발송)\n", + "- $B$: 사용할 수 있는 총 비용 예산\n", + "\n", + "이를 위해 라그랑지 승수 $\\lambda \\ge 0$ 를 도입합니다.\n", "\n", "$$\n", - "L(z,\\lambda)\n", + "L(z, \\lambda)\n", "= -\\sum_i \\tau_r(x^{(i)}) z_i\n", - "+ \\lambda\\left(\\sum_i \\tau_c(x^{(i)}) z_i - B\\right)\n", + " + \\lambda\\left(\\sum_i \\tau_c(x^{(i)}) z_i - B\\right)\n", "$$\n", "\n", - "으로 표현됩니다.\n", - "\n", - "고정된 $\\lambda$ 아래에서 고객 $i$의 효율성 점수는 다음과 같습니다.\n", + "고정된 $\\lambda$ 에 대해:\n", "\n", "$$\n", - "s_i(\\lambda) = \\tau_r(x^{(i)}) - \\lambda\\, \\tau_c(x^{(i)}).\n", + "s_i(\\lambda) = \\tau_r(x^{(i)}) - \\lambda\\, \\tau_c(x^{(i)})\n", "$$\n", "\n", - "점수가 양수이면 타겟팅하는 것이 유리하므로 \n", - "$s_i(\\lambda) \\ge 0$ 이면 $z_i = 1$, 음수이면 $z_i = 0$ 을 선택합니다. \n", - "즉, $\\lambda$가 주어지면 단순히 $s_i(\\lambda)$가 양수인 고객만 선택하면 됩니다.\n", + "- $s_i(\\lambda) \\ge 0$ 이면 $z_i = 1$ (타깃)\n", + "- $s_i(\\lambda) < 0$ 이면 $z_i = 0$ (비타깃)\n", "\n", - "듀얼 목적함수의 기울기는\n", + "듀얼 목적함수 기울기는\n", "\n", "$$\n", "\\frac{\\partial g}{\\partial \\lambda}\n", - "\\approx \\sum_i z_i \\tau_c(x^{(i)}) - B\n", + "\\approx \\underbrace{\\sum_i z_i\\,\\tau_c^+(x^{(i)})}_{\\text{cost\\_used}} - B\n", "$$\n", "\n", - "으로 근사할 수 있고, 이에 따른 gradient ascent 업데이트는\n", + "이며, gradient ascent 업데이트는\n", "\n", "$$\n", - "\\lambda \\leftarrow \\bigl[\\lambda + \\eta(\\text{cost\\_used} - B)\\bigr]_+\n", + "\\lambda \\leftarrow [\\lambda + \\eta(\\text{cost\\_used} - B)]_+\n", "$$\n", "\n", - "로 진행됩니다. 여기서 $[\\cdot]_+$ 는 $\\lambda$가 음수가 되지 않도록 하는 projection입니다.\n", - "\n", - "예산을 초과하면 $(\\text{cost\\_used} > B)$ $\\lambda$는 증가하여 비용 효과를 더 강하게 억제하고, \n", - "예산보다 적게 사용하면 $\\lambda$는 감소하여 더 많은 고객이 선택될 수 있도록 조정됩니다.\n", - "\n", - "Train 데이터에서 양의 Cost CATE 합을 기반으로 예산 $B$를 설정하고, \n", - "위 규칙을 반복 적용하여 최종 $\\lambda^*$와 정책을 학습합니다." + "- 예산 초과($\\text{cost\\_used} > B$) → $\\lambda$ 증가 → cost가 큰 고객 penalize\n", + "- 예산 미만($\\text{cost\\_used} < B$) → $\\lambda$ 감소 → 더 많은 고객 선택 허용" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "id": "5fe3e686", "metadata": {}, "outputs": [], @@ -1025,7 +1084,7 @@ "\n", " for it in range(n_iter + 1):\n", " # effectiveness score\n", - " s = tau_r - lam * tau_c\n", + " s = tau_r - lam * tau_c_pos\n", "\n", " # z_i: 선택 여부 (s_i >= 0 이면 선택)\n", " z = (s >= 0).astype(float)\n", @@ -1050,12 +1109,12 @@ " print(\"\\n최종 λ*:\", lam)\n", " print(\"총 양의 cost effect 합:\", total_pos_cost)\n", " print(f\"예산 B (fraction={budget_fraction}):\", B)\n", - " return lam, B\n" + " return lam, B" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 16, "id": "233a6a45", "metadata": {}, "outputs": [ @@ -1063,29 +1122,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "[iter 000] λ=0.228487, cost_used=34077.3618, gain_used=22363.6067, grad=22848.7019, selected=0.907\n", - "[iter 020] λ=0.784009, cost_used=11251.8179, gain_used=12501.7237, grad=23.1579, selected=0.298\n", - "[iter 040] λ=0.784586, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", - "[iter 060] λ=0.784587, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", - "[iter 080] λ=0.784587, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", - "[iter 100] λ=0.784587, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", - "[iter 120] λ=0.784588, cost_used=11228.2803, gain_used=12483.2661, grad=-0.3797, selected=0.297\n", - "[iter 140] λ=0.784588, cost_used=11229.1281, gain_used=12483.9314, grad=0.4682, selected=0.297\n", - "[iter 160] λ=0.784588, cost_used=11229.1281, gain_used=12483.9314, grad=0.4682, selected=0.297\n", - "[iter 180] λ=0.784589, cost_used=11229.1281, gain_used=12483.9314, grad=0.4682, selected=0.297\n", - "[iter 200] λ=0.784589, cost_used=11229.1281, gain_used=12483.9314, grad=0.4682, selected=0.297\n", + "[iter 000] λ=0.873750, cost_used=153674.7514, gain_used=95695.1631, grad=87375.0116, selected=0.571\n", + "[iter 020] λ=0.228848, cost_used=27172.5443, gain_used=61222.1379, grad=-39127.1955, selected=0.150\n", + "[iter 040] λ=0.229153, cost_used=27165.0076, gain_used=61217.4623, grad=-39134.7322, selected=0.150\n", + "[iter 060] λ=0.229200, cost_used=27164.1299, gain_used=61216.9177, grad=-39135.6099, selected=0.150\n", + "[iter 080] λ=0.229252, cost_used=27149.2112, gain_used=61207.6592, grad=-39150.5286, selected=0.150\n", + "[iter 100] λ=0.229233, cost_used=27149.2112, gain_used=61207.6592, grad=-39150.5286, selected=0.150\n", + "[iter 120] λ=0.229137, cost_used=27165.0076, gain_used=61217.4623, grad=-39134.7322, selected=0.150\n", + "[iter 140] λ=0.229207, cost_used=27164.1299, gain_used=61216.9177, grad=-39135.6099, selected=0.150\n", + "[iter 160] λ=0.229252, cost_used=27149.2112, gain_used=61207.6592, grad=-39150.5286, selected=0.150\n", + "[iter 180] λ=0.229143, cost_used=27165.0076, gain_used=61217.4623, grad=-39134.7322, selected=0.150\n", + "[iter 200] λ=0.229180, cost_used=27164.1299, gain_used=61216.9177, grad=-39135.6099, selected=0.150\n", "\n", - "최종 λ*: 0.7845891317539325\n", - "총 양의 cost effect 합: 37428.86642372066\n", - "예산 B (fraction=0.3): 11228.659927116198\n" + "최종 λ*: 0.22917953894026566\n", + "총 양의 cost effect 합: 220999.1326870586\n", + "예산 B (fraction=0.3): 66299.73980611758\n" ] } ], "source": [ - "lambda_star, B = duality_learn_lambda(\n", + "# Train 데이터에서 λ* 학습\n", + "lambda_star, B_train = duality_learn_lambda(\n", " tau_r=tau_r_train,\n", " tau_c=tau_c_train,\n", - " budget_fraction=0.3,\n", + " budget_fraction=0.3, # 전체 양의 cost uplift 중 30%를 예산으로\n", " lr=1e-5,\n", " n_iter=200,\n", " verbose_every=20,\n", @@ -1094,16 +1154,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "id": "a54ac8cf", "metadata": {}, "outputs": [], "source": [ "def selection_summary(tau_r, tau_c, lam, name=\"\"):\n", " tau_r = np.asarray(tau_r).astype(float)\n", - " tau_c = np.asarray(tau_c).astype(float)\n", + " tau_c_pos = np.clip(tau_c, a_min=0.0, a_max=None)\n", "\n", - " s = tau_r - lam * tau_c\n", + " s = tau_r - lam * tau_c_pos\n", " z = (s >= 0).astype(float)\n", "\n", " gain_pos = np.clip(tau_r, 0.0, None)\n", @@ -1128,12 +1188,12 @@ " \"gain_used\": gain_used,\n", " \"cost_used\": cost_used,\n", " \"gain_per_cost\": ratio,\n", - " }\n" + " }" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 19, "id": "6947da6a", "metadata": {}, "outputs": [ @@ -1143,32 +1203,32 @@ "text": [ "\n", "== Selection summary (Train) ==\n", - "λ = 0.784589\n", - "선택 비율: 0.297 (11420 / 38400)\n", - "총 gain (∑ τ_r^+ z): 12483.2661\n", - "총 cost (∑ τ_c^+ z): 11228.2803\n", - "gain / cost 비율: 1.1118\n", + "λ = 0.229180\n", + "선택 비율: 0.448 (32281 / 72053)\n", + "총 gain (∑ τ_r^+ z): 89986.7776\n", + "총 cost (∑ τ_c^+ z): 105798.3648\n", + "gain / cost 비율: 0.8505\n", "\n", "== Selection summary (Val) ==\n", - "λ = 0.784589\n", - "선택 비율: 0.293 (3753 / 12800)\n", - "총 gain (∑ τ_r^+ z): 4124.4445\n", - "총 cost (∑ τ_c^+ z): 3685.4466\n", - "gain / cost 비율: 1.1191\n", + "λ = 0.229180\n", + "선택 비율: 0.443 (7877 / 17774)\n", + "총 gain (∑ τ_r^+ z): 21768.6841\n", + "총 cost (∑ τ_c^+ z): 25771.2861\n", + "gain / cost 비율: 0.8447\n", "\n", "== Selection summary (Test) ==\n", - "λ = 0.784589\n", - "선택 비율: 0.302 (3862 / 12800)\n", - "총 gain (∑ τ_r^+ z): 4210.7541\n", - "총 cost (∑ τ_c^+ z): 3794.4139\n", - "gain / cost 비율: 1.1097\n" + "λ = 0.229180\n", + "선택 비율: 0.410 (8345 / 20333)\n", + "총 gain (∑ τ_r^+ z): 60981.3120\n", + "총 cost (∑ τ_c^+ z): 55160.2068\n", + "gain / cost 비율: 1.1055\n" ] } ], "source": [ "_ = selection_summary(tau_r_train, tau_c_train, lambda_star, name=\"Train\")\n", "_ = selection_summary(tau_r_val, tau_c_val, lambda_star, name=\"Val\")\n", - "_ = selection_summary(tau_r_test, tau_c_test, lambda_star, name=\"Test\")\n" + "_ = selection_summary(tau_r_test, tau_c_test, lambda_star, name=\"Test\")" ] }, { @@ -1176,98 +1236,142 @@ "id": "6ec8b309", "metadata": {}, "source": [ - "### 5. Cost Curve & AUCC\n", + "### 4. Cost Curve & AUCC (Test set 평가)\n", + "\n", + "Test 셋에서 정책의 성능을 Incremental Cost 대비 Incremental Gain 곡선으로 평가합니다.\n", + "\n", + "1. **Effectiveness score로 정렬** \n", "\n", - "Cost Curve 와 그 면적(AUCC, Area Under Cost Curve)로 비용 대비 uplift 모델을 평가합니다.\n", + " Duality R-learner의 점수는\n", + " $$\n", + " s(x)=\\tau_r(x)-\\lambda^*\\tau_c(x)\n", + " $$\n", + " 로 정의하며, 이를 기준으로 샘플을 내림차순으로 정렬합니다.\n", "\n", - "Test 셋에서:\n", + "2. **상위 \\(k\\)명(prefix)에서 ATE 추정** \n", "\n", - "1. Duality 점수 ${s(x) = \\tau_r(x) - \\lambda^* \\tau_c(x)}$ 기준으로 내림차순 정렬\n", - "2. 정렬된 순서대로\n", - " - ${\\tau_r^+(x) = \\max(\\tau_r(x), 0)}$\n", - " - ${\\tau_c^+(x) = \\max(\\tau_c(x), 0)}$\n", - " 의 누적합 계산\n", - "3. 누적 cost/gain 을 각각 최종값으로 나누어 ${[0,1]}$ 범위로 정규화\n", - "4. $(0,0)$ 에서 $(1,1)$ 까지 이어지는 곡선을 Cost Curve 로 사용\n", - "5. 수치 적분으로 AUCC 계산:\n", + " 정렬된 상위 \\(k\\)개 집단에서 관측 결과 \\(Y\\)를 사용해 gain과 cost에 대한 ATE를 계산합니다:\n", " $$\n", - " \\text{AUCC} = \\int_0^1 \\text{gain}(x)\\,dx\n", + " \\widehat{ATE}_g(k)=\\mathbb{E}[Y_g\\mid T=1]-\\mathbb{E}[Y_g\\mid T=0],\\quad\n", + " \\widehat{ATE}_c(k)=\\mathbb{E}[Y_c\\mid T=1]-\\mathbb{E}[Y_c\\mid T=0].\n", " $$\n", "\n", - "비교를 위해 랜덤 ranking 의 Cost Curve 와 AUCC 도 함께 계산합니다.\n", + "3. **총 증분 gain/cost 계산** \n", + " 상위 \\(k\\) 집단에서 실제 처치된 샘플 수 \\(n_t(k)\\)를 곱해,\n", + " $$\n", + " \\Delta G(k)=n_t(k)\\cdot \\widehat{ATE}_g(k),\\quad\n", + " \\Delta C(k)=n_t(k)\\cdot \\widehat{ATE}_c(k)\n", + " $$\n", + " 를 각 점으로 사용합니다.\n", "\n", - "- AUCC ${\\approx 0.5}$: 랜덤에 가까운 정책\n", - "- AUCC ${>} 0.5$: 효율적인 고객부터 잘 고르는 정책\n" + "4. **정규화 및 Cost Curve 구성** \n", + " \\((0,0)\\)을 포함한 $(\\Delta C(k), \\Delta G(k))$를 정규화하여\n", + " $$\n", + " x(k)=\\frac{\\Delta C(k)}{C_{\\text{norm}}},\\quad\n", + " y(k)=\\frac{\\Delta G(k)}{G_{\\text{norm}}}\n", + " $$\n", + " 로 변환하고, 이를 이은 곡선을 Cost Curve로 정의합니다. \n", + " 정규화 기준은 전체 집단을 기본으로 사용하되, 전원 처리 시 증분 gain이 0 이하인 경우에는\n", + " **양수 구간의 최대값(max-positive)**을 사용하여 비교 가능하게 합니다.\n", + "\n", + "5. **AUCC 계산** \n", + " Cost Curve 아래 면적을 수치 적분으로 계산합니다:\n", + " $$\n", + " \\text{AUCC}=\\int_0^1 y(x)\\,dx.\n", + " $$\n" ] }, { "cell_type": "code", - "execution_count": 27, - "id": "56c9cc3e", + "execution_count": 33, + "id": "6039a560", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "== Test set Cost Curve (τ 기반) ==\n", - "max_cost: 12481.778185597159\n", - "max_gain: 7511.766716461641\n", - "Normalized AUCC: 0.6946670819574676\n" - ] - } - ], + "outputs": [], "source": [ - "# Duality R-learner 기반 effectiveness score (Test set)\n", - "s_test = tau_r_test - lambda_star * tau_c_test\n", - "\n", - "# score 기준 내림차순 정렬\n", - "order = np.argsort(-s_test)\n", - "tau_r_sorted = np.clip(tau_r_test[order], 0.0, None) # gain은 양수 부분만\n", - "tau_c_sorted = np.clip(tau_c_test[order], 0.0, None) # cost도 양수 부분만\n", - "\n", - "# 누적 cost / gain\n", - "cum_cost = np.cumsum(tau_c_sorted)\n", - "cum_gain = np.cumsum(tau_r_sorted)\n", - "\n", - "# 0 지점 포함\n", - "cum_cost = np.insert(cum_cost, 0, 0.0)\n", - "cum_gain = np.insert(cum_gain, 0, 0.0)\n", - "\n", - "# 정규화\n", - "max_cost = cum_cost[-1]\n", - "max_gain = cum_gain[-1]\n", - "\n", - "x = cum_cost / max_cost\n", - "y = cum_gain / max_gain\n", - "\n", - "# AUCC 계산\n", - "aucc = np.trapz(y, x)\n", - "\n", - "print(\"== Test set Cost Curve (τ 기반) ==\")\n", - "print(\"max_cost:\", max_cost)\n", - "print(\"max_gain:\", max_gain)\n", - "print(\"Normalized AUCC:\", aucc)\n" + "def cost_curve_aucc(scores, Yg, Yc, T, n_points=80):\n", + " \"\"\"\n", + " Paper-style Y-based Cost Curve:\n", + " - sort by score desc\n", + " - for each prefix top-k:\n", + " ATE_gain = mean(Yg|T=1) - mean(Yg|T=0)\n", + " ATE_cost = mean(Yc|T=1) - mean(Yc|T=0)\n", + " ΔGain(k) = n_treat * ATE_gain\n", + " ΔCost(k) = n_treat * ATE_cost\n", + " - normalize (rightmost if possible else max-positive)\n", + " - AUCC = ∫ y dx\n", + " \"\"\"\n", + " scores = np.asarray(scores, float)\n", + " Yg = np.asarray(Yg, float)\n", + " Yc = np.asarray(Yc, float)\n", + " T = np.asarray(T, int)\n", + "\n", + " order = np.argsort(-scores)\n", + " Yg, Yc, T = Yg[order], Yc[order], T[order]\n", + "\n", + " N = len(T)\n", + " ks = np.linspace(1, N, n_points, dtype=int)\n", + "\n", + " inc_g, inc_c = [0.0], [0.0] # include (0,0)\n", + " for k in ks:\n", + " T_k, Yg_k, Yc_k = T[:k], Yg[:k], Yc[:k]\n", + " mt, mc = (T_k == 1), (T_k == 0)\n", + "\n", + " if mt.sum() == 0 or mc.sum() == 0:\n", + " inc_g.append(0.0); inc_c.append(0.0); continue\n", + "\n", + " ate_g = Yg_k[mt].mean() - Yg_k[mc].mean()\n", + " ate_c = Yc_k[mt].mean() - Yc_k[mc].mean()\n", + " n_t = mt.sum()\n", + "\n", + " inc_g.append(ate_g * n_t)\n", + " inc_c.append(ate_c * n_t)\n", + "\n", + " inc_g = np.maximum(np.asarray(inc_g, float), 0.0)\n", + " inc_c = np.asarray(inc_c, float)\n", + "\n", + " max_g, max_c = inc_g[-1], inc_c[-1]\n", + " if max_g <= 0 or max_c <= 0:\n", + " max_g = inc_g[inc_g > 0].max() if np.any(inc_g > 0) else 1.0\n", + " max_c = inc_c[inc_c > 0].max() if np.any(inc_c > 0) else 1.0\n", + "\n", + " x = inc_c / max_c\n", + " y = inc_g / max_g\n", + "\n", + " si = np.argsort(x)\n", + " aucc = np.trapz(y[si], x[si])\n", + " return x, y, aucc\n", + "\n", + "def plot_cost_curve(x, y, aucc, title=\"Cost Curve (Paper-style, Y-based)\", label=\"Model\"):\n", + " plt.figure(figsize=(7, 6))\n", + " plt.plot(x, y, label=f\"{label} (AUCC={aucc:.3f})\")\n", + " plt.plot([0, 1], [0, 1], alpha=0.35, linewidth=1, label=\"y=x benchmark\")\n", + " plt.xlabel(\"Incremental cost (normalized)\")\n", + " plt.ylabel(\"Incremental gain (normalized)\")\n", + " plt.title(title)\n", + " plt.grid(alpha=0.3)\n", + " plt.legend()\n", + " plt.tight_layout()\n", + " plt.show()" ] }, { "cell_type": "code", - "execution_count": 28, - "id": "3b6badd9", + "execution_count": 34, + "id": "2b41ea6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Random ranking AUCC: 0.5006872435398204\n" + "Duality AUCC: 0.6649279978825946\n" ] }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -1275,49 +1379,20 @@ } ], "source": [ - "# 랜덤 베이스라인 Cost Curve\n", - "rng = np.random.default_rng(RANDOM_STATE)\n", - "perm = rng.permutation(len(tau_r_test))\n", - "\n", - "tau_r_rand = np.clip(tau_r_test[perm], 0.0, None)\n", - "tau_c_rand = np.clip(tau_c_test[perm], 0.0, None)\n", - "\n", - "cum_cost_rand = np.cumsum(tau_c_rand)\n", - "cum_gain_rand = np.cumsum(tau_r_rand)\n", - "\n", - "cum_cost_rand = np.insert(cum_cost_rand, 0, 0.0)\n", - "cum_gain_rand = np.insert(cum_gain_rand, 0, 0.0)\n", - "\n", - "x_rand = cum_cost_rand / cum_cost_rand[-1]\n", - "y_rand = cum_gain_rand / cum_gain_rand[-1]\n", - "\n", - "aucc_rand = np.trapz(y_rand, x_rand)\n", - "print(\"Random ranking AUCC:\", aucc_rand)\n", - "\n", - "# 플롯\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(x, y, label=f\"Duality R-learner (AUCC={aucc:.3f})\")\n", - "plt.plot(x_rand, y_rand, linestyle=\"--\", label=f\"Random (AUCC={aucc_rand:.3f})\")\n", - "plt.plot([0, 1], [0, 1], alpha=0.4, linewidth=1, label=\"y=x reference\")\n", - "\n", - "plt.xlabel(\"Cumulative cost / max\")\n", - "plt.ylabel(\"Cumulative gain / max\")\n", - "plt.title(\"Cost curve on Test set (τ-based)\")\n", - "plt.legend()\n", - "plt.grid(alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()\n" + "scores_duality = tau_r_test - lambda_star * tau_c_test\n", + "x, y, aucc = cost_curve_aucc(scores_duality, Yg_test, Yc_test, T_test, n_points=80)\n", + "\n", + "print(\"Duality AUCC:\", aucc)\n", + "plot_cost_curve(x, y, aucc, title=\"Cost Curve on Test set\", label=\"Duality\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "965eecec", + "id": "d36a48d4", "metadata": {}, "outputs": [], - "source": [ - " " - ] + "source": [] } ], "metadata": { diff --git a/book/prescriptive_analytics/overview.md b/book/prescriptive_analytics/overview.md index 8bcc007..ad8685a 100644 --- a/book/prescriptive_analytics/overview.md +++ b/book/prescriptive_analytics/overview.md @@ -1,5 +1,5 @@ # Prescriptive Analytics - Prescriptive Analytics는 데이터를 활용해 최적의 의사결정을 도출하는 분석 방식입니다. -- 접근 방식은 크게 **Prediction + Optimization**, **Causal Inference + Optimization** 으로 나눌 수 있습니다. -- 이 섹션에서는 **Causal Inference + Optimization** 에 집중하여, 개입의 인과효과(CATE)를 기반으로 **가장 효율적인 정책·전략을 선택하는 방법**을 다룹니다. +- 접근 방식은 크게 Prediction + Optimization, Causal Inference + Optimization 으로 나눌 수 있습니다. +- 이 섹션에서는 Causal Inference + Optimization 에 집중하여, 개입의 인과효과(CATE)를 기반으로 가장 효율적인 정책·전략을 선택하는 방법을 다룹니다. \ No newline at end of file From 43c7906bd6bb2b18c172bddfc22e6fa340b5a93f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EC=A1=B0=ED=95=B4=EC=B0=BD?= Date: Sat, 20 Dec 2025 02:21:55 +0900 Subject: [PATCH 4/5] fix: train/val/test split and stabilize duality AUCC evaluation --- ...rning_for_effectiveness_optimization.ipynb | 897 ++++++------------ 1 file changed, 314 insertions(+), 583 deletions(-) diff --git a/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb b/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb index 0082571..c7f6f34 100644 --- a/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb +++ b/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb @@ -52,62 +52,17 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 503, "id": "9114f7da", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "

🌲 Try YDF, the successor of\n", - " TensorFlow\n", - " Decision Forests using the same algorithms but with more features and faster\n", - " training!\n", - "

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-       "import tensorflow_decision_forests as tfdf\n",
-       "\n",
-       "tf_ds = tfdf.keras.pd_dataframe_to_tf_dataset(ds, label=\"l\")\n",
-       "model = tfdf.keras.RandomForestModel(label=\"l\")\n",
-       "model.fit(tf_ds)\n",
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-       "import ydf\n",
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-       "model = ydf.RandomForestLearner(label=\"l\").train(ds)\n",
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(Learn more in the migration\n", - " guide)

\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", - "from sklearn.linear_model import Ridge, LogisticRegression\n", + "from sklearn.linear_model import Ridge\n", "from sklearn.metrics import r2_score, roc_auc_score\n", + "from sklearn.model_selection import train_test_split\n", "\n", "import fractional_uplift as fr \n", "\n", @@ -135,314 +90,52 @@ "\n", " 을 모두 포함하므로, 비용까지 고려한 처치 최적화 실험에 적합합니다. \n", "\n", - "- 세 가지 DataFrame으로 구성\n", - " - `train_data`\n", - " - `distill_data` (여기서는 validation 역할로 사용)\n", - " - `test_data`\n", - "\n", - "- 주요 컬럼\n", + "- 주요 컬럼:\n", " - `treatment`: 광고/프로모션 노출 여부 (0/1)\n", " - `spend`: 사용자가 발생시킨 매출(이익) → Gain outcome: $(Y^r)$\n", " - `cost`: 해당 고객에게 treatment를 줄 때 들어간 비용 → Cost outcome: $(Y^c)$\n", " - `treatment_propensity`: 실험에서 treatment에 할당될 확률\n", " - `sample_weight`: 샘플 가중치\n", - " - `criteo.features`: feature 컬럼 이름 리스트 (문자열 리스트)" + " - `criteo.features`: feature 컬럼 이름 리스트 (문자열 리스트)\n", + "\n", + "- Train/Val/Test split:\n", + " \n", + " `train_data`를 train / validation / test 로 분리하여 사용합니다.\n", + " " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 638, "id": "b2b3d7a2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train shape: (72053, 19)\n", - "Val shape: (17774, 19)\n", - "Test shape: (20333, 19)\n", - "\n", - "Feature columns: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11']\n", - "\n", - "Train head:\n" - ] - }, - { - "data": { - "text/html": [ - "
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f0f1f2f3f4f5f6f7f8f9f10f11treatmentconversiontreatment_propensitycost_percentagespendcostsample_weight
4412.61636510.0596548.9645884.67988210.2805254.1154530.2944434.8338153.95539613.1900565.300375-0.168679100.850.0000000.0000000.000000100.0
18712.61636510.0596548.9045974.67988210.2805254.1154530.2944434.8338153.95539613.1900565.300375-0.168679100.850.0000000.0000000.000000100.0
48422.37723810.0596548.2143834.67988210.2805254.115453-2.4111154.8338153.97185813.1900565.300375-0.168679100.850.0000000.0000000.000000100.0
52812.61636510.0596548.3506824.67988210.2805254.1154530.2944434.8338153.95539616.2260445.300375-0.168679100.850.0000000.0000000.000000100.0
110814.61762710.0596548.4899293.90766213.2538134.115453-2.4111154.8338153.80953042.1763245.737292-0.560340110.850.09077736.4592943.3096551.0
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" - ], - "text/plain": [ - " f0 f1 f2 f3 f4 f5 f6 \\\n", - "44 12.616365 10.059654 8.964588 4.679882 10.280525 4.115453 0.294443 \n", - "187 12.616365 10.059654 8.904597 4.679882 10.280525 4.115453 0.294443 \n", - "484 22.377238 10.059654 8.214383 4.679882 10.280525 4.115453 -2.411115 \n", - "528 12.616365 10.059654 8.350682 4.679882 10.280525 4.115453 0.294443 \n", - "1108 14.617627 10.059654 8.489929 3.907662 13.253813 4.115453 -2.411115 \n", - "\n", - " f7 f8 f9 f10 f11 treatment \\\n", - "44 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n", - "187 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n", - "484 4.833815 3.971858 13.190056 5.300375 -0.168679 1 \n", - "528 4.833815 3.955396 16.226044 5.300375 -0.168679 1 \n", - "1108 4.833815 3.809530 42.176324 5.737292 -0.560340 1 \n", - "\n", - " conversion treatment_propensity cost_percentage spend cost \\\n", - "44 0 0.85 0.000000 0.000000 0.000000 \n", - "187 0 0.85 0.000000 0.000000 0.000000 \n", - "484 0 0.85 0.000000 0.000000 0.000000 \n", - "528 0 0.85 0.000000 0.000000 0.000000 \n", - "1108 1 0.85 0.090777 36.459294 3.309655 \n", - "\n", - " sample_weight \n", - "44 100.0 \n", - "187 100.0 \n", - "484 100.0 \n", - "528 100.0 \n", - "1108 1.0 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "criteo = fr.example_data.CriteoWithSyntheticCostAndSpend.load()\n", "\n", - "train_df = criteo.train_data.copy()\n", - "val_df = criteo.distill_data.copy() # distill_data를 validation 데이터로 사용\n", - "test_df = criteo.test_data.copy()\n", - "features = criteo.features # feature column 리스트\n", - "\n", - "print(\"Train shape:\", train_df.shape)\n", - "print(\"Val shape:\", val_df.shape)\n", - "print(\"Test shape:\", test_df.shape)\n", - "print(\"\\nFeature columns:\", features)\n", + "df_all = criteo.train_data.copy()\n", + "features = criteo.features\n", "\n", - "print(\"\\nTrain head:\")\n", - "train_df.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "5c9e7a68", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "treatment 비율: 0.8611161228540103\n", - "spend describe:\n", - "count 72053.000000\n", - "mean 7.638117\n", - "std 15.380174\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 172.747528\n", - "Name: spend, dtype: float64\n", - "cost describe:\n", - "count 72053.000000\n", - "mean 2.558094\n", - "std 7.596816\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 63.162000\n", - "Name: cost, dtype: float64\n" - ] - } - ], - "source": [ - "print(\"treatment 비율:\", train_df[\"treatment\"].mean())\n", - "print(\"spend describe:\")\n", - "print(train_df[\"spend\"].describe())\n", - "print(\"cost describe:\")\n", - "print(train_df[\"cost\"].describe())" - ] - }, - { - "cell_type": "markdown", - "id": "bfdbc4fd", - "metadata": {}, - "source": [ - "### Feature 행렬 & 타겟 정의\n", - "\n", - "- $X$: `features` 컬럼들\n", - "- $T$: `treatment` (0/1)\n", - "- $Y^r$: `spend` (gain)\n", - "- $Y^c$: `cost` (cost)\n", - "\n", - "여기서는:\n", + "# 1) train vs temp(=val+test)\n", + "train_df, temp_df = train_test_split(\n", + " df_all,\n", + " test_size=0.4, # val 0.2 + test 0.2\n", + " random_state=RANDOM_STATE,\n", + " stratify=df_all[\"treatment\"],\n", + ")\n", "\n", - "- 데이터셋이 이미 `train / distill / test`로 나뉘어 있으므로,\n", - " - `train_df` → train\n", - " - `val_df` → validation\n", - " - `test_df` → test\n", - " 로 그대로 사용합니다.\n" + "# 2) temp를 val vs test\n", + "val_df, test_df = train_test_split(\n", + " temp_df,\n", + " test_size=0.5, # temp의 절반 = 0.2\n", + " random_state=RANDOM_STATE,\n", + " stratify=temp_df[\"treatment\"],\n", + ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 652, "id": "e286adf5", "metadata": {}, "outputs": [ @@ -450,9 +143,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "X_train shape: (72053, 12)\n", - "X_val shape: (17774, 12)\n", - "X_test shape: (20333, 12)\n" + "X_train shape: (43231, 12)\n", + "X_val shape: (14411, 12)\n", + "X_test shape: (14411, 12)\n" ] } ], @@ -473,6 +166,10 @@ "Yc_val = val_df[\"cost\"].values.astype(float)\n", "Yc_test = test_df[\"cost\"].values.astype(float)\n", "\n", + "W_train = train_df[\"sample_weight\"].values.astype(float)\n", + "W_val = val_df[\"sample_weight\"].values.astype(float)\n", + "W_test = test_df[\"sample_weight\"].values.astype(float)\n", + "\n", "print(\"X_train shape:\", X_train.shape)\n", "print(\"X_val shape:\", X_val.shape)\n", "print(\"X_test shape:\", X_test.shape)" @@ -533,23 +230,18 @@ "\n", "- $m^*(X) = \\mathbb{E}[Y \\mid X]$: outcome 평균 모델 \n", "- $e^*(X) = \\mathbb{P}(T=1 \\mid X)$: propensity score \n", - "\n", - "Criteo 셋에서는 gain과 cost가 모두 연속값이므로 \n", - "각각 독립적인 회귀모델을 쓰는 것이 자연스럽습니다.\n", + " > propensity score는 treatment_propensity 컬럼을 그대로 사용합니다.\n", "\n", "- Gain outcome $Y^r = \\texttt{spend}$\n", " - $m_r(x) = \\mathbb{E}[Y^r\\mid X=x]$: Ridge 회귀\n", "\n", "- Cost outcome $Y^c = \\texttt{cost}$\n", - " - $m_c(x) = \\mathbb{E}[Y^c\\mid X=x]$: Ridge 회귀\n", - "\n", - "- Treatment model\n", - " - $e(x) = \\mathbb{P}(T=1\\mid X=x)$: Logistic 회귀" + " - $m_c(x) = \\mathbb{E}[Y^c\\mid X=x]$: Ridge 회귀" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 620, "id": "3e718594", "metadata": {}, "outputs": [ @@ -558,18 +250,18 @@ "output_type": "stream", "text": [ "== m_r(x) 성능 (R^2: spend 회귀) ==\n", - "Train R^2: 0.5991714831471346\n", - "Val R^2: 0.6034863154274288\n", + "Train R^2: 0.5937760649833947\n", + "Val R^2: 0.6185973626734869\n", "\n", "예측값 분포 (Val):\n", - "count 17774.000000\n", - "mean 7.529438\n", - "std 11.912387\n", - "min -5.214262\n", - "25% -0.278099\n", - "50% 1.335855\n", - "75% 12.749807\n", - "max 81.783020\n", + "count 14411.000000\n", + "mean 7.721704\n", + "std 11.980173\n", + "min -5.330875\n", + "25% -0.231392\n", + "50% 1.539356\n", + "75% 12.941192\n", + "max 80.358582\n", "dtype: float64\n" ] } @@ -595,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 621, "id": "083c0aa5", "metadata": {}, "outputs": [ @@ -604,18 +296,18 @@ "output_type": "stream", "text": [ "== m_c(x) 성능 (R^2: cost 회귀) ==\n", - "Train R^2: 0.2841092845226817\n", - "Val R^2: 0.29156262859724946\n", + "Train R^2: 0.2921035090257208\n", + "Val R^2: 0.27535410112615455\n", "\n", "예측값 분포 (Val):\n", - "count 17774.000000\n", - "mean 2.524266\n", - "std 4.070278\n", - "min -24.999222\n", - "25% -0.114549\n", - "50% 0.894167\n", - "75% 4.298206\n", - "max 23.168346\n", + "count 14411.000000\n", + "mean 2.570728\n", + "std 4.126056\n", + "min -19.284588\n", + "25% -0.116940\n", + "50% 0.918125\n", + "75% 4.393160\n", + "max 24.175064\n", "dtype: float64\n" ] } @@ -641,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 622, "id": "1aa8d52b", "metadata": {}, "outputs": [ @@ -650,30 +342,19 @@ "output_type": "stream", "text": [ "== e(x) 성능 (AUC: treatment 모델) ==\n", - "Train AUC: 0.5432668234321524\n", - "Val AUC: 0.5470811168626702\n", + "Train AUC: 0.5\n", + "Val AUC: 0.5\n", "\n", "Propensity e(x) range:\n", - "Train: 0.8259820462034405 → 0.9497856673846461\n", - "Val : 0.8258405409868536 → 0.9441271638409029\n" + "Train: 0.8500001287591226 → 0.8500001287591226\n", + "Val : 0.8500001287591226 → 0.8500001287591226\n" ] } ], "source": [ - "# Propensity model e(x) = P(T=1 | X): Logistic 회귀\n", - "propensity = LogisticRegression(\n", - " penalty=\"l2\",\n", - " C=1.0,\n", - " solver=\"lbfgs\",\n", - " max_iter=1000,\n", - " n_jobs=-1,\n", - ")\n", - "\n", - "propensity.fit(X_train, T_train)\n", - "\n", - "e_train = propensity.predict_proba(X_train)[:, 1]\n", - "e_val = propensity.predict_proba(X_val)[:, 1]\n", - "e_test = propensity.predict_proba(X_test)[:, 1]\n", + "e_train = train_df[\"treatment_propensity\"].values.astype(float)\n", + "e_val = val_df[\"treatment_propensity\"].values.astype(float)\n", + "e_test = test_df[\"treatment_propensity\"].values.astype(float)\n", "\n", "auc_train_e = roc_auc_score(T_train, e_train)\n", "auc_val_e = roc_auc_score(T_val, e_val)\n", @@ -729,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 623, "id": "28eb5e7c", "metadata": {}, "outputs": [], @@ -742,53 +423,61 @@ " e_tr, e_val,\n", " alpha=1.0,\n", " name=\"R-learner\",\n", + " rt_clip=1e-6,\n", "):\n", " \"\"\"\n", " 선형 τ(x) = w^T x 를 R-learner 방식으로 학습.\n", - " - X_tr, X_val: feature 행렬\n", - " - T_tr, T_val: treatment (0/1)\n", - " - Y_tr, Y_val: outcome (gain or cost)\n", - " - m_tr, m_val: m(x) = E[Y|X] 예측값\n", - " - e_tr, e_val: e(x) = P(T=1|X) 예측값\n", + " rY = (T - e(X)) * τ(X) + ε 를 이용해 w를 추정한다.\n", " \"\"\"\n", - " X_tr = np.asarray(X_tr)\n", - " X_val = np.asarray(X_val)\n", - " T_tr = np.asarray(T_tr).astype(float)\n", - " T_val = np.asarray(T_val).astype(float)\n", - " Y_tr = np.asarray(Y_tr).astype(float)\n", - " Y_val = np.asarray(Y_val).astype(float)\n", - " m_tr = np.asarray(m_tr).astype(float)\n", - " m_val = np.asarray(m_val).astype(float)\n", - " e_tr = np.asarray(e_tr).astype(float)\n", - " e_val = np.asarray(e_val).astype(float)\n", + " X_tr = np.asarray(X_tr, dtype=float)\n", + " X_val = np.asarray(X_val, dtype=float)\n", + " T_tr = np.asarray(T_tr, dtype=float)\n", + " T_val = np.asarray(T_val, dtype=float)\n", + " Y_tr = np.asarray(Y_tr, dtype=float)\n", + " Y_val = np.asarray(Y_val, dtype=float)\n", + " m_tr = np.asarray(m_tr, dtype=float)\n", + " m_val = np.asarray(m_val, dtype=float)\n", + " e_tr = np.asarray(e_tr, dtype=float)\n", + " e_val = np.asarray(e_val, dtype=float)\n", "\n", " # residuals\n", " rY_tr = Y_tr - m_tr\n", " rT_tr = T_tr - e_tr\n", "\n", - " # Z = X * rT (각 행을 rT로 스케일링)\n", + " # rT가 너무 작은 경우 클리핑\n", + " rT_tr = np.where(np.abs(rT_tr) < rt_clip, np.sign(rT_tr) * rt_clip, rT_tr)\n", + "\n", + " # Z = X * rT\n", " Z_tr = X_tr * rT_tr.reshape(-1, 1)\n", "\n", - " # 회귀: rY ~ Z\n", + " # fit\n", " tau_model = Ridge(alpha=alpha, fit_intercept=False, random_state=RANDOM_STATE)\n", " tau_model.fit(Z_tr, rY_tr)\n", "\n", " # τ_hat(x) = w^T x\n", - " tau_tr = tau_model.predict(X_tr)\n", - " tau_val = tau_model.predict(X_val)\n", + " w = tau_model.coef_.reshape(-1)\n", + " tau_tr = X_tr @ w\n", + " tau_val = X_val @ w\n", + "\n", + " # val에서 rY를 얼마나 설명하는지 확인\n", + " rY_val = Y_val - m_val\n", + " rT_val = T_val - e_val\n", + " pred_rY_val = rT_val * tau_val\n", + " mse_val = np.mean((rY_val - pred_rY_val) ** 2)\n", "\n", " print(f\"== {name} 요약 ==\")\n", " print(\"Train τ_hat summary:\")\n", " print(pd.Series(tau_tr).describe())\n", " print(\"\\nVal τ_hat summary:\")\n", " print(pd.Series(tau_val).describe())\n", + " print(f\"\\nVal check: MSE(rY, rT*tau) = {mse_val:.6f}\")\n", "\n", - " return tau_model, tau_tr, tau_val" + " return tau_model, tau_tr, tau_val\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 624, "id": "03fc2e68", "metadata": {}, "outputs": [ @@ -798,26 +487,28 @@ "text": [ "== Gain R-learner τ_r(x) 요약 ==\n", "Train τ_hat summary:\n", - "count 72053.000000\n", - "mean 0.869965\n", - "std 4.075175\n", - "min -16.366707\n", - "25% -0.214938\n", - "50% 0.119879\n", - "75% 1.240913\n", - "max 74.068790\n", + "count 43231.000000\n", + "mean 0.744968\n", + "std 4.095001\n", + "min -14.967210\n", + "25% -0.467654\n", + "50% 0.084019\n", + "75% 1.151731\n", + "max 75.742621\n", "dtype: float64\n", "\n", "Val τ_hat summary:\n", - "count 17774.000000\n", - "mean 0.847950\n", - "std 4.047337\n", - "min -13.205720\n", - "25% -0.215324\n", - "50% 0.116316\n", - "75% 1.209327\n", - "max 66.496148\n", - "dtype: float64\n" + "count 14411.000000\n", + "mean 0.805699\n", + "std 4.339214\n", + "min -13.043869\n", + "25% -0.465580\n", + "50% 0.088950\n", + "75% 1.189868\n", + "max 70.995090\n", + "dtype: float64\n", + "\n", + "Val check: MSE(rY, rT*tau) = 91.237132\n" ] } ], @@ -844,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 625, "id": "554cb0c4", "metadata": {}, "outputs": [ @@ -854,26 +545,28 @@ "text": [ "== Cost R-learner τ_c(x) 요약 ==\n", "Train τ_hat summary:\n", - "count 72053.000000\n", - "mean 2.873521\n", - "std 4.356113\n", - "min -18.526381\n", - "25% -0.005015\n", - "50% 0.903932\n", - "75% 4.806126\n", - "max 29.188446\n", + "count 43231.000000\n", + "mean 2.810444\n", + "std 4.418757\n", + "min -19.332760\n", + "25% -0.070645\n", + "50% 0.982358\n", + "75% 4.749005\n", + "max 26.477165\n", "dtype: float64\n", "\n", "Val τ_hat summary:\n", - "count 17774.000000\n", - "mean 2.838168\n", - "std 4.375784\n", - "min -28.228684\n", - "25% -0.024353\n", - "50% 0.862495\n", - "75% 4.697582\n", - "max 26.297773\n", - "dtype: float64\n" + "count 14411.000000\n", + "mean 2.817899\n", + "std 4.431292\n", + "min -20.013473\n", + "25% -0.087577\n", + "50% 1.023093\n", + "75% 4.772627\n", + "max 25.653274\n", + "dtype: float64\n", + "\n", + "Val check: MSE(rY, rT*tau) = 40.727902\n" ] } ], @@ -900,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 626, "id": "01798a5e", "metadata": {}, "outputs": [ @@ -910,78 +603,78 @@ "text": [ "== τ_r(x) 요약 ==\n", "[Train]\n", - "count 72053.000000\n", - "mean 0.869965\n", - "std 4.075175\n", - "min -16.366707\n", - "25% -0.214938\n", - "50% 0.119879\n", - "75% 1.240913\n", - "max 74.068790\n", + "count 43231.000000\n", + "mean 0.744968\n", + "std 4.095001\n", + "min -14.967210\n", + "25% -0.467654\n", + "50% 0.084019\n", + "75% 1.151731\n", + "max 75.742621\n", "dtype: float64\n", "\n", "[Val]\n", - "count 17774.000000\n", - "mean 0.847950\n", - "std 4.047337\n", - "min -13.205720\n", - "25% -0.215324\n", - "50% 0.116316\n", - "75% 1.209327\n", - "max 66.496148\n", + "count 14411.000000\n", + "mean 0.805699\n", + "std 4.339214\n", + "min -13.043869\n", + "25% -0.465580\n", + "50% 0.088950\n", + "75% 1.189868\n", + "max 70.995090\n", "dtype: float64\n", "\n", "[Test]\n", - "count 20333.000000\n", - "mean 2.168109\n", - "std 7.207469\n", - "min -13.727579\n", - "25% -1.233063\n", - "50% 0.928363\n", - "75% 3.340883\n", - "max 76.512638\n", + "count 14411.000000\n", + "mean 0.713794\n", + "std 4.158598\n", + "min -14.772031\n", + "25% -0.479206\n", + "50% 0.054279\n", + "75% 1.114829\n", + "max 61.358405\n", "dtype: float64\n", "\n", "== τ_c(x) 요약 ==\n", "[Train]\n", - "count 72053.000000\n", - "mean 2.873521\n", - "std 4.356113\n", - "min -18.526381\n", - "25% -0.005015\n", - "50% 0.903932\n", - "75% 4.806126\n", - "max 29.188446\n", + "count 43231.000000\n", + "mean 2.810444\n", + "std 4.418757\n", + "min -19.332760\n", + "25% -0.070645\n", + "50% 0.982358\n", + "75% 4.749005\n", + "max 26.477165\n", "dtype: float64\n", "\n", "[Val]\n", - "count 17774.000000\n", - "mean 2.838168\n", - "std 4.375784\n", - "min -28.228684\n", - "25% -0.024353\n", - "50% 0.862495\n", - "75% 4.697582\n", - "max 26.297773\n", + "count 14411.000000\n", + "mean 2.817899\n", + "std 4.431292\n", + "min -20.013473\n", + "25% -0.087577\n", + "50% 1.023093\n", + "75% 4.772627\n", + "max 25.653274\n", "dtype: float64\n", "\n", "[Test]\n", - "count 20333.000000\n", - "mean 8.090442\n", - "std 4.941765\n", - "min -17.154167\n", - "25% 4.801457\n", - "50% 7.960261\n", - "75% 11.408073\n", - "max 26.761067\n", + "count 14411.000000\n", + "mean 2.739378\n", + "std 4.419076\n", + "min -20.425985\n", + "25% -0.102309\n", + "50% 0.915572\n", + "75% 4.637743\n", + "max 28.992231\n", "dtype: float64\n" ] } ], "source": [ "# Test set CATE 예측\n", - "tau_r_test = tau_r_model.predict(X_test)\n", - "tau_c_test = tau_c_model.predict(X_test)\n", + "tau_r_test = X_test @ tau_r_model.coef_.reshape(-1)\n", + "tau_c_test = X_test @ tau_c_model.coef_.reshape(-1)\n", "\n", "print(\"== τ_r(x) 요약 ==\")\n", "print(\"[Train]\")\n", @@ -1055,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "5fe3e686", "metadata": {}, "outputs": [], @@ -1067,6 +760,7 @@ " lr=1e-5,\n", " n_iter=200,\n", " verbose_every=20,\n", + " scale=1e4\n", "):\n", " \"\"\"\n", " τ_r, τ_c 가 주어졌을 때 Duality gradient ascent로 λ 학습.\n", @@ -1096,7 +790,8 @@ " grad = cost_used - B\n", "\n", " # gradient ascent (λ >= 0 유지)\n", - " lam = max(0.0, lam + lr * grad)\n", + " lr_eff = lr * scale / (total_pos_cost + 1e-12)\n", + " lam = max(0.0, lam + lr_eff * grad)\n", "\n", " if it % verbose_every == 0:\n", " sel_ratio = z.mean()\n", @@ -1114,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 628, "id": "233a6a45", "metadata": {}, "outputs": [ @@ -1122,21 +817,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "[iter 000] λ=0.873750, cost_used=153674.7514, gain_used=95695.1631, grad=87375.0116, selected=0.571\n", - "[iter 020] λ=0.228848, cost_used=27172.5443, gain_used=61222.1379, grad=-39127.1955, selected=0.150\n", - "[iter 040] λ=0.229153, cost_used=27165.0076, gain_used=61217.4623, grad=-39134.7322, selected=0.150\n", - "[iter 060] λ=0.229200, cost_used=27164.1299, gain_used=61216.9177, grad=-39135.6099, selected=0.150\n", - "[iter 080] λ=0.229252, cost_used=27149.2112, gain_used=61207.6592, grad=-39150.5286, selected=0.150\n", - "[iter 100] λ=0.229233, cost_used=27149.2112, gain_used=61207.6592, grad=-39150.5286, selected=0.150\n", - "[iter 120] λ=0.229137, cost_used=27165.0076, gain_used=61217.4623, grad=-39134.7322, selected=0.150\n", - "[iter 140] λ=0.229207, cost_used=27164.1299, gain_used=61216.9177, grad=-39135.6099, selected=0.150\n", - "[iter 160] λ=0.229252, cost_used=27149.2112, gain_used=61207.6592, grad=-39150.5286, selected=0.150\n", - "[iter 180] λ=0.229143, cost_used=27165.0076, gain_used=61217.4623, grad=-39134.7322, selected=0.150\n", - "[iter 200] λ=0.229180, cost_used=27164.1299, gain_used=61216.9177, grad=-39135.6099, selected=0.150\n", + "[iter 000] λ=0.036519, cost_used=88238.3493, gain_used=56003.9532, grad=48442.7285, selected=0.530\n", + "[iter 020] λ=0.324064, cost_used=46207.1315, gain_used=49466.9730, grad=6411.5107, selected=0.363\n", + "[iter 040] λ=0.366826, cost_used=40887.6177, gain_used=47644.3566, grad=1091.9968, selected=0.333\n", + "[iter 060] λ=0.374218, cost_used=39912.3018, gain_used=47282.8494, grad=116.6809, selected=0.327\n", + "[iter 080] λ=0.374977, cost_used=39803.9172, gain_used=47242.2461, grad=8.2964, selected=0.326\n", + "[iter 100] λ=0.375001, cost_used=39795.3574, gain_used=47239.0362, grad=-0.2634, selected=0.326\n", + "[iter 120] λ=0.375001, cost_used=39795.3574, gain_used=47239.0362, grad=-0.2634, selected=0.326\n", + "[iter 140] λ=0.375002, cost_used=39795.3574, gain_used=47239.0362, grad=-0.2634, selected=0.326\n", + "[iter 160] λ=0.375002, cost_used=39795.3574, gain_used=47239.0362, grad=-0.2634, selected=0.326\n", + "[iter 180] λ=0.375003, cost_used=39795.3574, gain_used=47239.0362, grad=-0.2634, selected=0.326\n", + "[iter 200] λ=0.375003, cost_used=39801.2137, gain_used=47241.2323, grad=5.5929, selected=0.326\n", "\n", - "최종 λ*: 0.22917953894026566\n", - "총 양의 cost effect 합: 220999.1326870586\n", - "예산 B (fraction=0.3): 66299.73980611758\n" + "최종 λ*: 0.3750031632094995\n", + "총 양의 cost effect 합: 132652.0694261761\n", + "예산 B (fraction=0.3): 39795.62082785283\n" ] } ], @@ -1149,51 +844,43 @@ " lr=1e-5,\n", " n_iter=200,\n", " verbose_every=20,\n", + " scale=1e4\n", ")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 642, "id": "a54ac8cf", "metadata": {}, "outputs": [], "source": [ "def selection_summary(tau_r, tau_c, lam, name=\"\"):\n", - " tau_r = np.asarray(tau_r).astype(float)\n", - " tau_c_pos = np.clip(tau_c, a_min=0.0, a_max=None)\n", + " tau_r = np.asarray(tau_r, float)\n", + " tau_c = np.asarray(tau_c, float)\n", + " tau_c_pos = np.clip(tau_c, 0.0, None)\n", "\n", " s = tau_r - lam * tau_c_pos\n", " z = (s >= 0).astype(float)\n", "\n", - " gain_pos = np.clip(tau_r, 0.0, None)\n", - " cost_pos = np.clip(tau_c, 0.0, None)\n", - "\n", - " gain_used = (gain_pos * z).sum()\n", - " cost_used = (cost_pos * z).sum()\n", + " gain_used = (tau_r * z).sum()\n", + " cost_used = (tau_c_pos * z).sum()\n", " sel_ratio = z.mean()\n", - "\n", " ratio = gain_used / cost_used if cost_used > 0 else np.nan\n", "\n", " print(f\"\\n== Selection summary ({name}) ==\")\n", " print(f\"λ = {lam:.6f}\")\n", " print(f\"선택 비율: {sel_ratio:.3f} ({z.sum():.0f} / {len(z)})\")\n", - " print(f\"총 gain (∑ τ_r^+ z): {gain_used:.4f}\")\n", + " print(f\"총 gain (∑ τ_r z): {gain_used:.4f}\")\n", " print(f\"총 cost (∑ τ_c^+ z): {cost_used:.4f}\")\n", " print(f\"gain / cost 비율: {ratio:.4f}\")\n", "\n", - " return {\n", - " \"lambda\": lam,\n", - " \"selected_ratio\": sel_ratio,\n", - " \"gain_used\": gain_used,\n", - " \"cost_used\": cost_used,\n", - " \"gain_per_cost\": ratio,\n", - " }" + " return {\"lambda\": lam, \"selected_ratio\": sel_ratio, \"gain_used\": gain_used, \"cost_used\": cost_used, \"gain_per_cost\": ratio}\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 643, "id": "6947da6a", "metadata": {}, "outputs": [ @@ -1203,25 +890,25 @@ "text": [ "\n", "== Selection summary (Train) ==\n", - "λ = 0.229180\n", - "선택 비율: 0.448 (32281 / 72053)\n", - "총 gain (∑ τ_r^+ z): 89986.7776\n", - "총 cost (∑ τ_c^+ z): 105798.3648\n", - "gain / cost 비율: 0.8505\n", + "λ = 0.375003\n", + "선택 비율: 0.326 (14100 / 43231)\n", + "총 gain (∑ τ_r z): 47239.0362\n", + "총 cost (∑ τ_c^+ z): 39795.3574\n", + "gain / cost 비율: 1.1870\n", "\n", "== Selection summary (Val) ==\n", - "λ = 0.229180\n", - "선택 비율: 0.443 (7877 / 17774)\n", - "총 gain (∑ τ_r^+ z): 21768.6841\n", - "총 cost (∑ τ_c^+ z): 25771.2861\n", - "gain / cost 비율: 0.8447\n", + "λ = 0.375003\n", + "선택 비율: 0.328 (4733 / 14411)\n", + "총 gain (∑ τ_r z): 16722.9333\n", + "총 cost (∑ τ_c^+ z): 13560.8063\n", + "gain / cost 비율: 1.2332\n", "\n", "== Selection summary (Test) ==\n", - "λ = 0.229180\n", - "선택 비율: 0.410 (8345 / 20333)\n", - "총 gain (∑ τ_r^+ z): 60981.3120\n", - "총 cost (∑ τ_c^+ z): 55160.2068\n", - "gain / cost 비율: 1.1055\n" + "λ = 0.375003\n", + "선택 비율: 0.323 (4656 / 14411)\n", + "총 gain (∑ τ_r z): 15984.1864\n", + "총 cost (∑ τ_c^+ z): 12859.2200\n", + "gain / cost 비율: 1.2430\n" ] } ], @@ -1283,65 +970,75 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 654, "id": "6039a560", "metadata": {}, "outputs": [], "source": [ - "def cost_curve_aucc(scores, Yg, Yc, T, n_points=80):\n", - " \"\"\"\n", - " Paper-style Y-based Cost Curve:\n", - " - sort by score desc\n", - " - for each prefix top-k:\n", - " ATE_gain = mean(Yg|T=1) - mean(Yg|T=0)\n", - " ATE_cost = mean(Yc|T=1) - mean(Yc|T=0)\n", - " ΔGain(k) = n_treat * ATE_gain\n", - " ΔCost(k) = n_treat * ATE_cost\n", - " - normalize (rightmost if possible else max-positive)\n", - " - AUCC = ∫ y dx\n", - " \"\"\"\n", + "def cost_curve_aucc(scores, Yg, Yc, T, W=None, n_points=80, clip_negative_gain=False):\n", " scores = np.asarray(scores, float)\n", " Yg = np.asarray(Yg, float)\n", " Yc = np.asarray(Yc, float)\n", " T = np.asarray(T, int)\n", + " if W is None:\n", + " W = np.ones_like(T, dtype=float)\n", + " else:\n", + " W = np.asarray(W, float)\n", "\n", " order = np.argsort(-scores)\n", - " Yg, Yc, T = Yg[order], Yc[order], T[order]\n", + " Yg, Yc, T, W = Yg[order], Yc[order], T[order], W[order]\n", "\n", " N = len(T)\n", " ks = np.linspace(1, N, n_points, dtype=int)\n", "\n", - " inc_g, inc_c = [0.0], [0.0] # include (0,0)\n", + " def wmean(y, w):\n", + " return (y * w).sum() / (w.sum() + 1e-12)\n", + "\n", + " inc_g, inc_c = [0.0], [0.0]\n", " for k in ks:\n", - " T_k, Yg_k, Yc_k = T[:k], Yg[:k], Yc[:k]\n", + " T_k, Yg_k, Yc_k, W_k = T[:k], Yg[:k], Yc[:k], W[:k]\n", " mt, mc = (T_k == 1), (T_k == 0)\n", "\n", " if mt.sum() == 0 or mc.sum() == 0:\n", " inc_g.append(0.0); inc_c.append(0.0); continue\n", "\n", - " ate_g = Yg_k[mt].mean() - Yg_k[mc].mean()\n", - " ate_c = Yc_k[mt].mean() - Yc_k[mc].mean()\n", - " n_t = mt.sum()\n", + " ate_g = wmean(Yg_k[mt], W_k[mt]) - wmean(Yg_k[mc], W_k[mc])\n", + " ate_c = wmean(Yc_k[mt], W_k[mt]) - wmean(Yc_k[mc], W_k[mc])\n", + "\n", + " \n", + " w_t = W_k[mt].sum()\n", + " inc_g.append(ate_g * w_t)\n", + " inc_c.append(ate_c * w_t)\n", "\n", - " inc_g.append(ate_g * n_t)\n", - " inc_c.append(ate_c * n_t)\n", + " inc_g = np.asarray(inc_g, float)\n", + " if clip_negative_gain:\n", + " inc_g = np.maximum(inc_g, 0.0)\n", "\n", - " inc_g = np.maximum(np.asarray(inc_g, float), 0.0)\n", - " inc_c = np.asarray(inc_c, float)\n", + " # cost는 음수면 0으로 (안전)\n", + " inc_c = np.maximum(np.asarray(inc_c, float), 0.0)\n", "\n", " max_g, max_c = inc_g[-1], inc_c[-1]\n", - " if max_g <= 0 or max_c <= 0:\n", - " max_g = inc_g[inc_g > 0].max() if np.any(inc_g > 0) else 1.0\n", - " max_c = inc_c[inc_c > 0].max() if np.any(inc_c > 0) else 1.0\n", + " if max_g == 0:\n", + " max_g = np.max(np.abs(inc_g)) if np.max(np.abs(inc_g)) > 0 else 1.0\n", + " if max_c == 0:\n", + " max_c = np.max(inc_c) if np.max(inc_c) > 0 else 1.0\n", "\n", " x = inc_c / max_c\n", " y = inc_g / max_g\n", "\n", - " si = np.argsort(x)\n", - " aucc = np.trapz(y[si], x[si])\n", - " return x, y, aucc\n", - "\n", - "def plot_cost_curve(x, y, aucc, title=\"Cost Curve (Paper-style, Y-based)\", label=\"Model\"):\n", + " x = np.maximum.accumulate(x)\n", + " aucc = np.trapz(y, x)\n", + " return x, y, aucc\n" + ] + }, + { + "cell_type": "code", + "execution_count": 655, + "id": "a60166d5", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_cost_curve(x, y, aucc, title=\"Cost Curve\", label=\"Model\"):\n", " plt.figure(figsize=(7, 6))\n", " plt.plot(x, y, label=f\"{label} (AUCC={aucc:.3f})\")\n", " plt.plot([0, 1], [0, 1], alpha=0.35, linewidth=1, label=\"y=x benchmark\")\n", @@ -1356,7 +1053,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 656, "id": "2b41ea6a", "metadata": {}, "outputs": [ @@ -1364,12 +1061,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Duality AUCC: 0.6649279978825946\n" + "Duality AUCC: 0.6109516208594291\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1379,17 +1076,51 @@ } ], "source": [ - "scores_duality = tau_r_test - lambda_star * tau_c_test\n", - "x, y, aucc = cost_curve_aucc(scores_duality, Yg_test, Yc_test, T_test, n_points=80)\n", + "tau_c_test_pos = np.clip(tau_c_test, 0.0, None)\n", + "scores_duality = tau_r_test - lambda_star * tau_c_test_pos\n", + "x, y, aucc = cost_curve_aucc(scores_duality, Yg_test, Yc_test, T_test, W=W_test, n_points=80)\n", + "\n", "\n", "print(\"Duality AUCC:\", aucc)\n", "plot_cost_curve(x, y, aucc, title=\"Cost Curve on Test set\", label=\"Duality\")" ] }, + { + "cell_type": "code", + "execution_count": 651, + "id": "1d1e4af1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train cost_used: 39795.357426534225 selected: 0.32615484258980826\n", + "val cost_used: 13560.806268071696 selected: 0.3284296717785025\n", + "test cost_used: 12859.22000170533 selected: 0.32308653112205954\n", + "B_train: 39795.62082785283\n" + ] + } + ], + "source": [ + "def cost_used_under_policy(tau_r, tau_c, lam):\n", + " tc = np.clip(tau_c, 0.0, None)\n", + " s = tau_r - lam * tc\n", + " z = (s >= 0).astype(float)\n", + " return (tc * z).sum(), z.mean()\n", + "\n", + "for name, tr, tc in [(\"train\", tau_r_train, tau_c_train),\n", + " (\"val\", tau_r_val, tau_c_val),\n", + " (\"test\", tau_r_test, tau_c_test)]:\n", + " c, sel = cost_used_under_policy(tr, tc, lambda_star)\n", + " print(name, \"cost_used:\", c, \"selected:\", sel)\n", + "print(\"B_train:\", B_train)\n" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "d36a48d4", + "id": "220d5e1f", "metadata": {}, "outputs": [], "source": [] From a0278b6fc0e9c0f8f108e6eb97857e8b29650999 Mon Sep 17 00:00:00 2001 From: Funbucket Date: Sun, 4 Jan 2026 18:42:26 +0900 Subject: [PATCH 5/5] add: fractional uplift --- book/_toc.yml | 3 +- ... => budget_constrained_optimization.ipynb} | 10 +- .../fractional_uplift.ipynb | 1994 +++++++++++++++++ 3 files changed, 2005 insertions(+), 2 deletions(-) rename book/prescriptive_analytics/{heterogeneous_causal_learning_for_effectiveness_optimization.ipynb => budget_constrained_optimization.ipynb} (99%) create mode 100644 book/prescriptive_analytics/fractional_uplift.ipynb diff --git a/book/_toc.yml b/book/_toc.yml index 4b59776..06ab718 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -26,4 +26,5 @@ parts: - file: scm/causal_discovery.ipynb - file: prescriptive_analytics/overview.md sections: - - file: prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb \ No newline at end of file + - file: prescriptive_analytics/fractional_uplift.ipynb + - file: prescriptive_analytics/budget_constrained_optimization.ipynb \ No newline at end of file diff --git a/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb b/book/prescriptive_analytics/budget_constrained_optimization.ipynb similarity index 99% rename from book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb rename to book/prescriptive_analytics/budget_constrained_optimization.ipynb index c7f6f34..7b69d3f 100644 --- a/book/prescriptive_analytics/heterogeneous_causal_learning_for_effectiveness_optimization.ipynb +++ b/book/prescriptive_analytics/budget_constrained_optimization.ipynb @@ -5,7 +5,15 @@ "id": "d6b70f64", "metadata": {}, "source": [ - "# Heterogeneous Causal Learning for Effectiveness Optimization" + "# Budget Constrained Optimization" + ] + }, + { + "cell_type": "markdown", + "id": "826633ac", + "metadata": {}, + "source": [ + "> [Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing](https://arxiv.org/abs/2004.09702) 논문에 제안된 방법을 Python 코드로 재현했습니다." ] }, { diff --git a/book/prescriptive_analytics/fractional_uplift.ipynb b/book/prescriptive_analytics/fractional_uplift.ipynb new file mode 100644 index 0000000..412a666 --- /dev/null +++ b/book/prescriptive_analytics/fractional_uplift.ipynb @@ -0,0 +1,1994 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8530047b", + "metadata": {}, + "source": [ + "# Fractional uplift modelling" + ] + }, + { + "cell_type": "markdown", + "id": "91104063", + "metadata": {}, + "source": [ + "> Google LLC의 [Fractional Uplift – End to End Example](https://github.com/google-marketing-solutions/fractional_uplift)를 한국어로 번역 및 정리했습니다.\n" + ] + }, + { + "cell_type": "markdown", + "id": "601fb081", + "metadata": {}, + "source": [ + "fractional uplift 모델은 프로모션 비용이 사전에 확정되지 않는 상황에 적합합니다. \n", + "\n", + "예를 들어, 특정 조건이 있는 쿠폰을 제공하는 경우 쿠폰을 제공하는 시점에는 실제 비용을 알 수 없으며, 비용은 사용자가 어떤 상품을 구매하느냐에 따라 달라집니다.\n", + "\n", + "이러한 상황에서 일반적인 uplift 모델링은 한계가 있습니다. 일반 uplift 모델은 처치로 인한 증분 효과(incrementality)만을 다루고, 비용은 고려하지 않기 때문입니다. \n", + "\n", + "반면 fractional uplift 모델은 여러 지표를 함께 고려하여 최적화하도록 설계되어 있습니다.\n", + "\n", + "일반적인 uplift 모델은 하나의 지표(예: 전환율 또는 지출 금액)에 대해 조건부 평균 처치 효과(CATE)를 추정합니다.\n", + "\n", + "$$\n", + "f_\\text{uplift}(X) = \\text{CATE}_y(X) = E[y \\mid T=1, X] - E[y \\mid T=0, X]\n", + "$$\n", + "\n", + "fractional uplift 모델은 여러 지표에 대한 CATE 추정치를 결합하여, 다음과 같은 단일 점수를 계산합니다.\n", + "\n", + "$$\n", + "f_\\text{fractional uplift}(X) =\n", + "\\begin{cases}\n", + " \\dfrac{\\text{CATE}_\\alpha (X)}\n", + " {\\text{CATE}_\\beta(X) - \\dfrac{\\text{CATE}_\\gamma (X)}{\\delta}},\n", + " & \\text{CATE}_\\beta(X) > \\dfrac{\\text{CATE}_\\gamma (X)}{\\delta} \\\\\n", + " \\infty, & \\text{그 외의 경우}\n", + "\\end{cases}\n", + "$$\n", + "\n", + "각 항의 의미는 다음과 같습니다.\n", + "\n", + "- $\\alpha$ (Maximize KPI) \n", + " 모델이 최대화하고자 하는 지표입니다.\n", + "\n", + "- $\\beta$ (Constraint KPI) \n", + " 제약으로 작용하는 지표로, 가능한 한 낮게 유지하고자 합니다.\n", + "\n", + "- $\\gamma$ (Constraint Offset KPI) \n", + " 제약을 상쇄하는 데 사용할 수 있는 지표이며, 선택적으로 사용됩니다.\n", + "\n", + "- $\\delta$ (Constraint Offset Scale) \n", + " constraint offset KPI의 스케일을 조정하는 상수입니다. \n", + " constraint offset KPI를 사용하지 않는 경우에는 필요하지 않습니다.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7f3eb8c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -q fractional-uplift pandas numpy statsmodels tensorflow tensorflow_decision_forests matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "e4d6d469", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OMP_NUM_THREADS\"] = \"8\"\n", + "os.environ[\"TF_NUM_INTRAOP_THREADS\"] = \"8\"\n", + "os.environ[\"TF_NUM_INTEROP_THREADS\"] = \"2\"" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "97a2ff86", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import statsmodels.api as sm\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as mtick\n", + "from matplotlib.lines import Line2D\n", + "\n", + "import tensorflow as tf # tensorflow decision forests가 eager mode로 실행되도록 하기 위해 필요\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "77fb1695", + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow_decision_forests as tfdf\n", + "import fractional_uplift as fr" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "27a1ae4b", + "metadata": {}, + "outputs": [], + "source": [ + "# tfdf 사용 시 출력되는 Keras 학습 로그 숨김\n", + "tfdf.keras.set_training_logs_redirection(False)" + ] + }, + { + "cell_type": "markdown", + "id": "acd5db70", + "metadata": {}, + "source": [ + "## Load the Criteo data\n", + "\n", + "[Criteo dataset](https://ailab.criteo.com/criteo-uplift-prediction-dataset/)은 uplift 모델링을 벤치마킹하기위한 공개된 데이터셋입니다. 여러 incrementality 테스트 결과를 모아 구성되었으며, 각 행은 사용자 한 명을 나타냅니다.\n", + "\n", + "데이터셋에는 다음 정보가 포함되어 있습니다.\n", + "\n", + "- 사용자 특성(feature) 11개\n", + "- 처치 여부(treatment)\n", + "- 결과 라벨 2개: 방문(visits), 전환(conversions)\n", + "\n", + "이 데이터셋은 전환이나 방문과 같은 단일 KPI를 대상으로 하는 표준 uplift 모델링 문제를 위해 설계되었습니다. \n", + "\n", + "그러나 이 노트북에서는 사용자에게 프로모션을 제공하는 상황을 가정하여, 보다 현실적인 uplift 모델링 문제를 다룹니다.\n", + "\n", + "이를 위해 다음과 같은 추가 지표를 사용합니다.\n", + "\n", + "- **Spend**: 사용자가 전환했을 때 지출한 금액을 의미하며, 특성(feature)을 기반으로 생성됩니다.\n", + "\n", + "- **Cost:** 또는 쿠폰 비용을 의미합니다. 사용자가 전환한 경우에만 발생하며, 처치된 사용자(T=1)에게서 전환이 발생했을 때에만 비용이 발생하도록 설정합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "7ee1e798", + "metadata": {}, + "outputs": [], + "source": [ + "criteo = fr.example_data.CriteoWithSyntheticCostAndSpend.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "92c3c0ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " f0 f1 f2 f3 f4 f5 f6 \\\n", + "44 12.616365 10.059654 8.964588 4.679882 10.280525 4.115453 0.294443 \n", + "187 12.616365 10.059654 8.904597 4.679882 10.280525 4.115453 0.294443 \n", + "484 22.377238 10.059654 8.214383 4.679882 10.280525 4.115453 -2.411115 \n", + "528 12.616365 10.059654 8.350682 4.679882 10.280525 4.115453 0.294443 \n", + "1108 14.617627 10.059654 8.489929 3.907662 13.253813 4.115453 -2.411115 \n", + "\n", + " f7 f8 f9 f10 f11 treatment \\\n", + "44 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n", + "187 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n", + "484 4.833815 3.971858 13.190056 5.300375 -0.168679 1 \n", + "528 4.833815 3.955396 16.226044 5.300375 -0.168679 1 \n", + "1108 4.833815 3.809530 42.176324 5.737292 -0.560340 1 \n", + "\n", + " conversion treatment_propensity cost_percentage spend cost \\\n", + "44 0 0.85 0.000000 0.000000 0.000000 \n", + "187 0 0.85 0.000000 0.000000 0.000000 \n", + "484 0 0.85 0.000000 0.000000 0.000000 \n", + "528 0 0.85 0.000000 0.000000 0.000000 \n", + "1108 1 0.85 0.090777 36.459294 3.309655 \n", + "\n", + " sample_weight \n", + "44 100.0 \n", + "187 100.0 \n", + "484 100.0 \n", + "528 100.0 \n", + "1108 1.0 " + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "criteo.train_data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "bdef041d", + "metadata": {}, + "source": [ + "## Experiment Analysis\n", + "\n", + "먼저 쿠폰 제공의 전체적인 효과를 일반적인 A/B 테스트로 분석합니다. \n", + "\n", + "사용자에게 구매 금액에 대한 할인을 제공하는 쿠폰을 제공하므로, 캠페인의 증분 RoI(incremental RoI, iRoI)를 평가합니다.\n", + "\n", + "iRoI는 다음과 같이 정의합니다.\n", + "\n", + "$$\n", + "\\text{iRoI} = \\frac{\\text{Spend}_{T=1} - \\text{Spend}_{T=0}}{\\text{Cost}_{T=1}}\n", + "$$\n", + "\n", + "최소제곱법(ordinary least squares)과 델타 방법(delta method)을 사용하여 iRoI와 신뢰구간을 추정합니다.\n", + "\n", + "**주의**: 델타 방법을 이용한 iRoI 신뢰구간 추정은 처치군에서의 spend와 cost 간 상관관계를 고려하지 않습니다. 이로 인해 신뢰구간이 실제보다 넓게 추정될 수 있습니다. 다만 이는 핵심 주제가 아니므로, 단순화를 위해 해당 방식을 그대로 사용합니다.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "7c472fde", + "metadata": {}, + "outputs": [], + "source": [ + "def perform_ols(input_df: pd.DataFrame, target: str) -> tuple[float, float]:\n", + " \"\"\"statsmodels를 사용하여 최소제곱법(OLS) 회귀를 수행합니다.\n", + "\n", + " target 변수를 처치 여부(treatment)에 회귀시키며,\n", + " 이는 두 집단 간 평균 차이를 검정하는 t-test와 동일한 역할을 합니다.\n", + "\n", + " Args:\n", + " input_df: 회귀 분석에 사용할 데이터프레임.\n", + " target: 효과를 추정할 대상 변수명.\n", + "\n", + " Returns:\n", + " (effect_size, standard_error) 튜플을 반환합니다.\n", + " - effect_size: 처치 효과(평균 차이)\n", + " - standard_error: 처치 효과의 표준오차\n", + " \"\"\"\n", + " Y = input_df[target].values\n", + " X = input_df[[\"treatment\"]]\n", + " X = sm.add_constant(X)\n", + " model = sm.OLS(Y, X)\n", + " results = model.fit()\n", + "\n", + " effect_size = results.params[\"treatment\"]\n", + " se = results.HC0_se[\"treatment\"]\n", + " return effect_size, se\n", + "\n", + "\n", + "def estimate_incremental_roi(data: pd.DataFrame) -> tuple[float, float, float]:\n", + " \"\"\"델타 방법(delta method)을 사용하여 증분 RoI와 그 불확실성을 추정합니다.\n", + "\n", + " 이 방법은 처치군 사용자에서 spend와 cost 간의 상관관계를\n", + " 고려하지 않기 때문에 정확한 추정은 아닙니다.\n", + " 실제로는 신뢰구간의 폭이 더 좁아질 수 있으며,\n", + " 여기서 계산된 값은 보수적인 추정치입니다.\n", + "\n", + " Args:\n", + " data: 분석에 사용할 데이터프레임.\n", + "\n", + " Returns:\n", + " (incremental_roi, lower_bound, upper_bound)를 반환합니다.\n", + " - incremental_roi: 증분 RoI 추정값\n", + " - lower_bound: 신뢰구간 하한\n", + " - upper_bound: 신뢰구간 상한\n", + " \"\"\"\n", + " effect_size_spend, spend_se = perform_ols(data, \"spend\")\n", + " avg_cost = data.loc[data[\"treatment\"] == 1, \"cost\"].mean()\n", + " cost_se = (\n", + " data.loc[data[\"treatment\"] == 1, \"cost\"].std()\n", + " / np.sqrt(np.sum(data[\"treatment\"]))\n", + " )\n", + "\n", + " inc_roi = effect_size_spend / avg_cost\n", + " inc_roi_se = np.abs(inc_roi) * np.sqrt(\n", + " spend_se**2 / effect_size_spend**2\n", + " + cost_se**2 / avg_cost**2\n", + " )\n", + "\n", + " inc_roi_lb = inc_roi - 2.0 * inc_roi_se\n", + " inc_roi_ub = inc_roi + 2.0 * inc_roi_se\n", + "\n", + " return inc_roi, inc_roi_lb, inc_roi_ub" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "b78d9b3b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Incremental RoI = 0.97 [Lower Bound=0.90, Upper Bound=1.03]\n" + ] + } + ], + "source": [ + "inc_roi, inc_roi_lb, inc_roi_ub = estimate_incremental_roi(criteo.data)\n", + "print(\n", + " f\"Incremental RoI = {inc_roi:.2f} \"\n", + " f\"[Lower Bound={inc_roi_lb:.2f}, Upper Bound={inc_roi_ub:.2f}]\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "664e5bd8", + "metadata": {}, + "source": [ + "처치가 분명하고 측정 가능한 효과를 보이는 것으로 보입니다(iRoI의 하한이 0보다 충분히 큼). \n", + "\n", + "그러나 동시에 비용도 높아, RoI가 확실하게 1.0을 넘는다고 말하기는 어렵습니다. 즉, 이 프로모션으로 손실이 발생하고 있을 가능성도 있습니다.\n", + "\n", + "이러한 상황이 바로 uplift 모델링에 적합한 경우입니다. 프로모션을 적절한 사용자에게만 타겟팅한다면, 더 높은 iRoI를 달성할 수 있을 것입니다." + ] + }, + { + "cell_type": "markdown", + "id": "3fd3f766", + "metadata": {}, + "source": [ + "## Uplift Modelling\n", + "\n", + "이제 이 프로모션 캠페인을 최적화하기 위해 다양한 uplift 모델을 학습합니다.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8ea86385", + "metadata": {}, + "source": [ + "### Distillation\n", + "\n", + "이 노트북에서 사용하는 uplift 모델들은 모두 meta learner입니다. 즉, 여러 개의 머신러닝 모델을 조합해 하나의 uplift 모델을 구성합니다.\n", + "\n", + "예를 들어, 아래에서 설명할 T-Learner는 처치군의 반응을 예측하는 모델과 대조군의 반응을 예측하는 모델, 총 두 개의 모델로 이루어져 있습니다.\n", + "\n", + "이처럼 여러 모델을 함께 사용하면, 실제 서비스 환경에서 추론 지연(latency)이 발생할 수 있습니다. \n", + "\n", + "이를 해결하기 위해 fractional uplift 패키지의 모든 메타 러너는 distill 메서드를 제공합니다. 이 메서드는 전체 uplift 모델을 근사하는 단일 모델을 생성합니다.\n", + "\n", + "아래 예제에서는 distillation을 적용한 모델과 적용하지 않은 모델의 성능을 함께 비교합니다." + ] + }, + { + "cell_type": "markdown", + "id": "868108a3", + "metadata": {}, + "source": [ + "### The T-Learner (baseline)\n", + "\n", + "fractional uplift 모델과 비교하기 위한 기준선(baseline)으로 기존 uplift 모델을 먼저 사용합니다.\n", + "\n", + "여기서는 비교적 단순하면서도 성능이 안정적인 T-Learner를 사용합니다. T-Learner는 하나의 KPI에 대한 uplift를 추정하기 위해, 대조군과 처치군 데이터를 각각 사용해 두 개의 모델을 학습하고, 두 예측값의 차이를 uplift로 계산합니다.\n", + "\n", + "전환(conversion)과 매출(spend)에 대한 uplift를 각각 추정하는 두 개의 T-Learner를 학습합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "id": "d2f0d38c", + "metadata": {}, + "outputs": [], + "source": [ + "test_dataset = fr.datasets.PandasDataset(\n", + " features_data=criteo.test_data[criteo.features]\n", + ")\n", + "distill_dataset = fr.datasets.PandasDataset(\n", + " features_data=criteo.distill_data[criteo.features]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "6ba9653f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_base_regressor():\n", + " # 예시로 사용하는 기본 회귀 모델입니다.\n", + " # 실제 프로젝트에서는 tuning 인자를 사용해 하이퍼파라미터 튜닝을 수행하는 것이 좋습니다.\n", + " # 자세한 내용은 tensorflow decision forests 문서를 참고하십시오.\n", + " return fr.base_models.TensorflowDecisionForestRegressor(\n", + " tfdf.keras.GradientBoostedTreesModel,\n", + " init_args=dict(verbose=0, max_depth=6, num_trees=300, shrinkage=0.1),\n", + " fit_args=dict(verbose=0)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "414804f0", + "metadata": {}, + "outputs": [], + "source": [ + "for target in [\"conversion\", \"spend\"]:\n", + " print(f\"\\nTraining the {target} T-learner\\n\")\n", + "\n", + " train_data = fr.datasets.PandasTrainData(\n", + " features_data=criteo.train_data[criteo.features],\n", + " maximize_kpi=criteo.train_data[target].values,\n", + " is_treated=criteo.train_data[\"treatment\"].values,\n", + " treatment_propensity=criteo.train_data[\"treatment_propensity\"].values,\n", + " sample_weight=criteo.train_data[\"sample_weight\"].values,\n", + " shuffle_seed=1234\n", + " )\n", + "\n", + " t_learner = fr.meta_learners.TLearner(get_base_regressor())\n", + " t_learner.fit(train_data)\n", + "\n", + " distill_t_learner = get_base_regressor()\n", + " t_learner.distill(distill_dataset, distill_t_learner)\n", + "\n", + " criteo.test_data[f\"{target}_t_learner_score\"] = t_learner.predict(test_dataset)\n", + " criteo.test_data[f\"{target}_t_learner_score_distill\"] = distill_t_learner.predict(test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "32668b60", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " f0 f1 f2 f3 f4 f5 \\\n", + "1007 13.221509 10.059654 8.471764 -1.666396 13.059169 2.230907 \n", + "1080 15.643772 10.059654 8.232822 3.907662 11.029584 4.115453 \n", + "1295 13.236812 11.119309 8.329031 -2.570015 11.561050 1.128518 \n", + "1670 19.622574 10.059654 8.305211 3.907662 13.253813 4.115453 \n", + "1940 12.616365 10.059654 8.681976 4.679882 11.029584 4.115453 \n", + "\n", + " f6 f7 f8 f9 ... conversion \\\n", + "1007 -14.476362 9.170324 3.793946 39.917532 ... 1 \n", + "1080 -1.288207 4.833815 3.858041 34.180688 ... 1 \n", + "1295 -18.659676 5.814641 3.855652 34.621266 ... 1 \n", + "1670 -1.288207 4.833815 3.803324 41.176485 ... 1 \n", + "1940 0.294443 4.833815 3.864711 13.190056 ... 1 \n", + "\n", + " treatment_propensity cost_percentage spend cost \\\n", + "1007 0.85 0.051091 37.386848 1.910123 \n", + "1080 0.85 0.469323 37.690649 17.689078 \n", + "1295 0.85 0.007478 45.282760 0.338639 \n", + "1670 0.85 0.010958 44.872755 0.491714 \n", + "1940 0.85 0.993622 19.274877 19.151940 \n", + "\n", + " sample_weight conversion_t_learner_score \\\n", + "1007 1.0 0.035170 \n", + "1080 1.0 0.015709 \n", + "1295 1.0 -0.233079 \n", + "1670 1.0 -0.137972 \n", + "1940 1.0 0.002356 \n", + "\n", + " conversion_t_learner_score_distill spend_t_learner_score \\\n", + "1007 0.053963 0.217223 \n", + "1080 0.015331 0.661332 \n", + "1295 -0.195807 -12.409683 \n", + "1670 -0.126640 -10.253737 \n", + "1940 0.001106 0.003596 \n", + "\n", + " spend_t_learner_score_distill \n", + "1007 0.484556 \n", + "1080 0.196871 \n", + "1295 -8.438211 \n", + "1670 -9.813921 \n", + "1940 -0.081532 \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "criteo.test_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "6c84863c", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_cumulative_incrementality(\n", + " ax: plt.Axes,\n", + " results_data: pd.DataFrame,\n", + " model_names: dict[str, str],\n", + " x_col: str,\n", + " y_col: str,\n", + " title: str = \"\",\n", + " x_label: str | None = None,\n", + " y_label: str | None = None,\n", + " x_format: str = \"{0}\",\n", + " y_format: str = \"{0}\",\n", + " order_col: str = \"share_targeted\",\n", + " random_baseline_name: str = \"random\",\n", + " x_lim: list[float] | None = None,\n", + " y_lim: list[float] | None = None,\n", + " show_legend: bool = True,\n", + " ) -> None:\n", + " \"\"\"Plots the cumulative incrementality of any x and y metrics.\"\"\"\n", + "\n", + " baselines_data = results_data.loc[results_data.name == random_baseline_name].copy().sort_values(order_col)\n", + " models_data = results_data.loc[results_data.name.isin(model_names)].copy().sort_values(order_col)\n", + "\n", + " raw_model_names = list(model_names.keys())\n", + " clean_model_names = list(model_names.values())\n", + "\n", + " ax.plot(baselines_data[x_col], baselines_data[y_col], color=\"k\", lw=1, label=\"\")\n", + "\n", + " for raw_model_name, model_results in models_data.groupby(\"name\"):\n", + "\n", + " if raw_model_name.endswith(\"_distill\"):\n", + " raw_model_name = raw_model_name.removesuffix(\"_distill\")\n", + " label = \"\"\n", + " line_style = \"--\"\n", + " else:\n", + " label = model_names[raw_model_name]\n", + " line_style = \"-\"\n", + "\n", + " color = f\"C{raw_model_names.index(raw_model_name)}\"\n", + " ax.plot(model_results[x_col], model_results[y_col], color=color, lw=1.5, label=label, ls=line_style)\n", + "\n", + " if x_lim is not None:\n", + " ax.set_xlim(x_lim)\n", + " if y_lim is not None:\n", + " ax.set_ylim(y_lim)\n", + "\n", + " ax.set_xlabel(x_label or x_col)\n", + " ax.set_ylabel(y_label or y_col)\n", + " ax.set_title(title)\n", + "\n", + " ax.xaxis.set_major_formatter(mtick.FuncFormatter(lambda x, pos: x_format.format(x)))\n", + " ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda y, pos: y_format.format(y)))\n", + "\n", + " # Add legend\n", + " if show_legend:\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " handles.extend([\n", + " Line2D([0], [0], alpha=0.0),\n", + " Line2D([0], [0], color=\"0.7\", lw=1.5, ls=\"-\"),\n", + " Line2D([0], [0], color=\"0.7\", lw=1.5, ls=\"--\")\n", + " ])\n", + " labels.extend([\n", + " \"\",\n", + " \"Full model\",\n", + " \"Distilled model\"\n", + " ])\n", + " model_legend = ax.legend(\n", + " handles=handles,\n", + " labels=labels,\n", + " loc='upper left',\n", + " bbox_to_anchor=(1, 1)\n", + " )\n", + "\n", + " del(baselines_data)\n", + " del(models_data)" + ] + }, + { + "cell_type": "markdown", + "id": "3418e936", + "metadata": {}, + "source": [ + "이제 두 개의 T-Learner를 평가합니다. 평가는 모델이 타깃으로 선택한 사용자 비율에 따라 incremental spend과 incremental conversions이 어떻게 증가하는지를 시각화하는 방식으로 수행합니다.\n", + "\n", + "만약 모델이 사용자를 무작위로 선택한다면, 전체 사용자 중 50%를 타깃팅했을 때 incremental conversions과 incremental spend 역시 전체의 약 50% 수준에 그칠 것입니다.\n", + "\n", + "반면 uplift 모델이 제대로 학습되었다면, 사용자 50%를 타깃팅했을 때 50%를 초과하는 incremental conversions과 incremental spend을 기대할 수 있습니다.\n", + "\n", + "아래에 제시된 uplift 곡선은 타깃 사용자 비율 전 구간에 걸쳐 이러한 성능 차이를 직관적으로 보여줍니다." + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "dcba6c3d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluator = fr.evaluate.UpliftEvaluator(\n", + " metric_cols=[\"spend\", \"conversion\"],\n", + " is_treated_col=\"treatment\",\n", + " treatment_propensity_col=\"treatment_propensity\",\n", + " effect_type=fr.EffectType.ATE\n", + ")\n", + "\n", + "models = {\n", + " \"spend_t_learner_score\": \"Spend T-Learner\",\n", + " \"conversion_t_learner_score\": \"Conversion T-Learner\",\n", + " \"spend_t_learner_score_distill\": \"Spend T-Learner\",\n", + " \"conversion_t_learner_score_distill\": \"Conversion T-Learner\",\n", + "}\n", + "\n", + "results = evaluator.evaluate(criteo.test_data, score_cols=list(models.keys()))\n", + "\n", + "fig, axs = plt.subplots(ncols=2, figsize=(12, 4), constrained_layout=True)\n", + "\n", + "plot_cumulative_incrementality(\n", + " axs[0],\n", + " results,\n", + " title=\"Incremental Conversions vs Share Targeted\",\n", + " model_names=models,\n", + " x_col=\"share_targeted\",\n", + " y_col=\"conversion__inc_cum\",\n", + " x_label=\"Share of Users Targeted\",\n", + " y_label=\"Incremental Conversions\",\n", + " x_format=\"{:.0%}\",\n", + " y_format=\"{:,.0f}\",\n", + " show_legend=False,\n", + " x_lim=[0, 1],\n", + " y_lim=[0, None]\n", + ")\n", + "\n", + "plot_cumulative_incrementality(\n", + " axs[1],\n", + " results,\n", + " title=\"Incremental Revenue vs Share Targeted\",\n", + " model_names=models,\n", + " x_col=\"share_targeted\",\n", + " y_col=\"spend__inc_cum\",\n", + " x_label=\"Share of Users Targeted\",\n", + " y_label=\"Incremental Revenue\",\n", + " x_format=\"{:.0%}\",\n", + " y_format=\"${:,.0f}\",\n", + " x_lim=[0, 1],\n", + " y_lim=[0, None]\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ed581503", + "metadata": {}, + "source": [ + "uplift model들이 제대로 작동하고 있는 것으로 보입니다. uplift curve가 대부분 random baseline(검은 선) 위에 위치해 있는데, 이는 사용자를 random으로 선택하는 것보다 더 나은 성과를 내고 있음을 의미합니다.\n", + "\n", + "두 모델 모두 incremental conversions를 최적화하는 데에는 무난한 성능을 보이지만, incremental revenue를 찾아내는 데에서는 spend T-Learner가 확실히 더 우수한 성능을 보입니다.\n", + "\n", + "이는 conversion T-Learner가 spend에 대한 정보를 전혀 알지 못하기 때문에 충분히 예상 가능한 결과입니다.\n", + "\n", + "하지만 과연 이 방법이 uplift model을 평가하는 올바른 방법일까요? 일반적으로는 그렇지 않습니다. 사용자 비율을 기준으로 타겟팅하는 것이 동일한 비용 비율을 의미하지는 않기 때문입니다. 예컨대, 모델이 선택한 사용자들이 오히려 가장 cost가 많이 드는 사용자들일 수도 있습니다.\n", + "\n", + "다음 섹션에서는 보다 현실적인 마케팅 목표를 기준으로 uplift model을 평가해 보고, 이러한 환경에서 T-Learner들이 적절한 fractional uplift model과 비교했을 때 얼마나 잘 동작하는지도 함께 살펴보겠습니다." + ] + }, + { + "cell_type": "markdown", + "id": "353e5314", + "metadata": {}, + "source": [ + "## Fractional uplift modelling" + ] + }, + { + "cell_type": "markdown", + "id": "3000ba3e", + "metadata": {}, + "source": [ + "### Objective 1: Minimum Cost per Incremental Acquisition (CPiA)\n", + "\n", + "첫 번째로 살펴볼 마케팅 목표는 incremental acquisition당 비용(cost per incremental acquisition, CPiA)을 최소화하는 것입니다. 여기서 acquisition은 conversion을 의미합니다.\n", + "\n", + "즉, 우리가 얻는 incremental conversion 하나당 가능한 한 가장 적은 비용을 지출하는 것이 목표입니다. \n", + "\n", + "CPiA는 다음과 같이 정의됩니다.\n", + "\n", + "$$ \\text{CPiA} = \\frac{\\text{Cost}_{T=1}}{N_{\\text{convert}, \\, T=1} - N_{\\text{convert}, \\, T=0}} $$\n" + ] + }, + { + "cell_type": "markdown", + "id": "6377e331", + "metadata": {}, + "source": [ + "#### Fractional uplift model\n", + "\n", + "CPiA를 최적화하기 위해 fractional uplift model에서 다음과 같은 metric 설정을 사용합니다.\n", + "\n", + "- Maximize KPI ($\\alpha$) = Conversion \n", + "- Constraint KPI ($\\beta$) = Cost \n", + "- Constraint Offset KPI ($\\gamma$) = *사용하지 않음*\n", + "\n", + "아래에서는 이러한 설정을 바탕으로 fractional uplift model을 학습합니다." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f516ac56", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = fr.datasets.PandasTrainData(\n", + " features_data=criteo.train_data[criteo.features],\n", + " maximize_kpi=criteo.train_data[\"conversion\"].values,\n", + " constraint_kpi=criteo.train_data[\"cost\"].values,\n", + " is_treated=criteo.train_data[\"treatment\"].values,\n", + " treatment_propensity=criteo.train_data[\"treatment_propensity\"].values,\n", + " sample_weight=criteo.train_data[\"sample_weight\"].values,\n", + " shuffle_seed=1234\n", + ")\n", + "fractional_t_learner = fr.meta_learners.FractionalLearner(get_base_regressor())\n", + "fractional_t_learner.fit(train_data)\n", + "\n", + "distill_fractional_t_learner = get_base_regressor()\n", + "fractional_t_learner.distill(distill_dataset, distill_fractional_t_learner)\n", + "\n", + "criteo.test_data[f\"cpia_score\"] = fractional_t_learner.predict(test_dataset)\n", + "criteo.test_data[f\"cpia_score_distill\"] = distill_fractional_t_learner.predict(test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "f707b4c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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f0f1f2f3f4f5f6f7f8f9...cost_percentagespendcostsample_weightconversion_t_learner_scoreconversion_t_learner_score_distillspend_t_learner_scorespend_t_learner_score_distillcpia_scorecpia_score_distill
100713.22150910.0596548.471764-1.66639613.0591692.230907-14.4763629.1703243.79394639.917532...0.05109137.3868481.9101231.00.0351700.0539630.2172230.4845560.0259710.011292
108015.64377210.0596548.2328223.90766211.0295844.115453-1.2882074.8338153.85804134.180688...0.46932337.69064917.6890781.00.0157090.0153310.6613320.1968710.0484940.030863
129513.23681211.1193098.329031-2.57001511.5610501.128518-18.6596765.8146413.85565234.621266...0.00747845.2827600.3386391.0-0.233079-0.195807-12.409683-8.438211-0.363601-0.103153
167019.62257410.0596548.3052113.90766213.2538134.115453-1.2882074.8338153.80332441.176485...0.01095844.8727550.4917141.0-0.137972-0.126640-10.253737-9.813921-0.158326-0.219133
194012.61636510.0596548.6819764.67988211.0295844.1154530.2944434.8338153.86471113.190056...0.99362219.27487719.1519401.00.0023560.0011060.003596-0.0815320.1137060.011170
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" + ], + "text/plain": [ + " f0 f1 f2 f3 f4 f5 \\\n", + "1007 13.221509 10.059654 8.471764 -1.666396 13.059169 2.230907 \n", + "1080 15.643772 10.059654 8.232822 3.907662 11.029584 4.115453 \n", + "1295 13.236812 11.119309 8.329031 -2.570015 11.561050 1.128518 \n", + "1670 19.622574 10.059654 8.305211 3.907662 13.253813 4.115453 \n", + "1940 12.616365 10.059654 8.681976 4.679882 11.029584 4.115453 \n", + "\n", + " f6 f7 f8 f9 ... cost_percentage \\\n", + "1007 -14.476362 9.170324 3.793946 39.917532 ... 0.051091 \n", + "1080 -1.288207 4.833815 3.858041 34.180688 ... 0.469323 \n", + "1295 -18.659676 5.814641 3.855652 34.621266 ... 0.007478 \n", + "1670 -1.288207 4.833815 3.803324 41.176485 ... 0.010958 \n", + "1940 0.294443 4.833815 3.864711 13.190056 ... 0.993622 \n", + "\n", + " spend cost sample_weight conversion_t_learner_score \\\n", + "1007 37.386848 1.910123 1.0 0.035170 \n", + "1080 37.690649 17.689078 1.0 0.015709 \n", + "1295 45.282760 0.338639 1.0 -0.233079 \n", + "1670 44.872755 0.491714 1.0 -0.137972 \n", + "1940 19.274877 19.151940 1.0 0.002356 \n", + "\n", + " conversion_t_learner_score_distill spend_t_learner_score \\\n", + "1007 0.053963 0.217223 \n", + "1080 0.015331 0.661332 \n", + "1295 -0.195807 -12.409683 \n", + "1670 -0.126640 -10.253737 \n", + "1940 0.001106 0.003596 \n", + "\n", + " spend_t_learner_score_distill cpia_score cpia_score_distill \n", + "1007 0.484556 0.025971 0.011292 \n", + "1080 0.196871 0.048494 0.030863 \n", + "1295 -8.438211 -0.363601 -0.103153 \n", + "1670 -9.813921 -0.158326 -0.219133 \n", + "1940 -0.081532 0.113706 0.011170 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "criteo.test_data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b51f3134", + "metadata": {}, + "source": [ + "#### Evaluation\n", + "\n", + "가장 낮은 CPiA는 가장 낮은 비용으로 가장 많은 incremental conversions를 달성하는 경우에 해당합니다. 이를 다음 두 가지 방식으로 평가합니다.\n", + "\n", + "1. 총 비용(total cost)에 따른 incremental conversions를 시각화합니다. 동일한 비용에서 incremental conversions가 가장 높은 모델이 가장 우수한 모델입니다.\n", + "2. incremental conversions에 따른 CPiA를 시각화합니다. 동일한 incremental conversions에서 CPiA가 가장 낮은 모델이 가장 우수한 모델입니다.\n", + "\n", + "이러한 지표들은 아래의 UpliftEvaluator를 사용하여 계산합니다.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "46fddcae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class CPIAUpliftEvaluator(fr.evaluate.UpliftEvaluator):\n", + " def __init__(self, **kwargs):\n", + " kwargs[\"metric_cols\"] = [\"spend\", \"conversion\", \"cost\"]\n", + " super().__init__(**kwargs)\n", + "\n", + " def _calculate_composite_metrics(self, data: pd.DataFrame) -> pd.DataFrame:\n", + " data[\"cpia__inc_cum\"] = data[\"cost__inc_cum\"] / data[\"conversion__inc_cum\"]\n", + " data[\"cpia__inc\"] = data[\"cost__inc\"] / data[\"conversion__inc\"]\n", + " return data\n", + "\n", + "evaluator = CPIAUpliftEvaluator(\n", + " is_treated_col=\"treatment\",\n", + " treatment_propensity_col=\"treatment_propensity\",\n", + " effect_type=fr.EffectType.ATE\n", + ")\n", + "\n", + "models = {\n", + " \"spend_t_learner_score\": \"Spend T-Learner\",\n", + " \"conversion_t_learner_score\": \"Conversion T-Learner\",\n", + " \"cpia_score\": \"CPiA Fractional T-Learner\",\n", + " \"cpia_score_distill\": \"CPiA Fractional T-Learner\",\n", + "}\n", + "\n", + "results = evaluator.evaluate(criteo.test_data, score_cols=list(models.keys()))\n", + "\n", + "fig, axs = plt.subplots(ncols=2, figsize=(12, 4), constrained_layout=True)\n", + "\n", + "plot_cumulative_incrementality(\n", + " axs[0],\n", + " results,\n", + " title=\"Incremental Conversions vs Cost\",\n", + " model_names=models,\n", + " x_col=\"cost__inc_cum\",\n", + " y_col=\"conversion__inc_cum\",\n", + " x_label=\"Cost\",\n", + " y_label=\"Incremental Conversions\",\n", + " x_format=\"${:,.0f}\",\n", + " y_format=\"{:,.0f}\",\n", + " x_lim=[0, None],\n", + " y_lim=[0, None],\n", + " show_legend=False\n", + ")\n", + "\n", + "plot_cumulative_incrementality(\n", + " axs[1],\n", + " results,\n", + " title=\"Cost per Incremental Acquisition (CPiA)\",\n", + " model_names=models,\n", + " x_col=\"conversion__inc_cum\",\n", + " y_col=\"cpia__inc_cum\",\n", + " x_label=\"Incremental Conversions\",\n", + " y_label=\"CPiA\",\n", + " x_format=\"{:,.0f}\",\n", + " y_format=\"${:,.0f}\",\n", + " x_lim=[0, None]\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0ac57c64", + "metadata": {}, + "source": [ + "fractional uplift 모델은 기존 uplift 모델보다 명확하게 더 우수한 성과를 보입니다. 동일한 비용 대비 훨씬 낮은 CPiA를 달성하고, 더 많은 추가 전환(incremental conversions)을 만들어냅니다. \n", + "\n", + "반면 T-Learner는 일부 경우 오히려 CPiA를 증가시키는 모습을 보입니다. 이는 T-Learner가 비용을 고려하지 않기 때문에, 증분 효과는 크지만 비용이 높은 사용자들을 타겟팅하게 되고, 그 결과 전체적으로는 더 나쁜 CPiA를 초래하기 때문입니다.\n" + ] + }, + { + "cell_type": "markdown", + "id": "75dda433", + "metadata": {}, + "source": [ + "### Objective 2: Maximum iRoI\n", + "\n", + "이번에는 CPiA가 아니라 수익(revenue) 관점에서 살펴보겠습니다. 모든 전환이 동일한 가치를 갖는 것은 아니며, 어떤 전환은 다른 전환보다 훨씬 더 큰 매출을 만들어냅니다.\n", + "\n", + "따라서 가능한 한 낮은 비용으로 최대의 추가 매출(incremental revenue, spend) 을 창출하고자 한다면, 우리의 목표는 iRoI를 최대화하는 것이 됩니다.\n", + "\n", + "$$\n", + "\\text{iRoI} = \\frac{\\text{Spend}_\\text{T=1} - \\text{Spend}_\\text{T=0}}{\\text{Cost}_\\text{T=1}}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "10405922", + "metadata": {}, + "source": [ + "#### Fractional uplift model\n", + "\n", + "iRoI를 최적화하기 위해 fractional uplift 모델에서 다음과 같이 지표를 설정합니다.\n", + "\n", + "- Maximize KPI ($\\alpha$) = Spend \n", + "- Constraint KPI ($\\beta$) = Cost \n", + "- Constraint Offset KPI ($\\gamma$) = *사용하지 않음*\n", + "\n", + "아래에서는 이러한 설정을 바탕으로 fractional uplift 모델을 학습합니다." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c23b3cd", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = fr.datasets.PandasTrainData(\n", + " features_data=criteo.train_data[criteo.features],\n", + " maximize_kpi=criteo.train_data[\"spend\"].values,\n", + " constraint_kpi=criteo.train_data[\"cost\"].values,\n", + " is_treated=criteo.train_data[\"treatment\"].values,\n", + " treatment_propensity=criteo.train_data[\"treatment_propensity\"].values,\n", + " sample_weight=criteo.train_data[\"sample_weight\"].values,\n", + " shuffle_seed=1234\n", + ")\n", + "fractional_t_learner = fr.meta_learners.FractionalLearner(get_base_regressor())\n", + "fractional_t_learner.fit(train_data)\n", + "\n", + "distill_fractional_t_learner = get_base_regressor()\n", + "fractional_t_learner.distill(distill_dataset, distill_fractional_t_learner)\n", + "\n", + "criteo.test_data[f\"roi_score\"] = fractional_t_learner.predict(test_dataset)\n", + "criteo.test_data[f\"roi_score_distill\"] = distill_fractional_t_learner.predict(test_dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "8d235815", + "metadata": {}, + "source": [ + "#### Evaluation\n", + "\n", + "가장 높은 iRoI는 가장 낮은 비용으로 가장 많은 추가 매출(incremental revenue) 을 달성한 경우에 해당합니다. 이를 다음 두 가지 방식으로 평가합니다.\n", + "\n", + "1. 총 비용 대비 추가 매출을 시각화합니다. 동일한 비용에서 추가 매출이 가장 높은 모델이 가장 우수한 모델입니다.\n", + "2. 추가 매출 대비 iRoI를 시각화합니다. 동일한 추가 매출 수준에서 iRoI가 가장 높은 모델이 가장 우수한 모델입니다.\n", + "\n", + "이러한 지표들은 아래의 UpliftEvaluator를 사용해 계산합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "0ef46b07", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class RoIUpliftEvaluator(fr.evaluate.UpliftEvaluator):\n", + " def __init__(self, **kwargs):\n", + " kwargs[\"metric_cols\"] = [\"spend\", \"conversion\", \"cost\"]\n", + " super().__init__(**kwargs)\n", + "\n", + " def _calculate_composite_metrics(self, data: pd.DataFrame) -> pd.DataFrame:\n", + " data[\"roi__inc_cum\"] = data[\"spend__inc_cum\"] / data[\"cost__inc_cum\"]\n", + " data[\"roi__inc\"] = data[\"spend__inc\"] / data[\"cost__inc\"]\n", + " return data\n", + "\n", + "evaluator = RoIUpliftEvaluator(\n", + " is_treated_col=\"treatment\",\n", + " treatment_propensity_col=\"treatment_propensity\",\n", + " effect_type=fr.EffectType.ATE\n", + ")\n", + "\n", + "models = {\n", + " \"spend_t_learner_score\": \"Spend T-Learner\",\n", + " \"conversion_t_learner_score\": \"Conversion T-Learner\",\n", + " \"roi_score\": \"RoI Fractional T-Learner\",\n", + " \"roi_score_distill\": \"RoI Fractional T-Learner\",\n", + "}\n", + "\n", + "results = evaluator.evaluate(criteo.test_data, score_cols=list(models.keys()))\n", + "\n", + "fig, axs = plt.subplots(ncols=2, figsize=(12, 4), constrained_layout=True)\n", + "\n", + "plot_cumulative_incrementality(\n", + " axs[0],\n", + " results,\n", + " title=\"Incremental Revenue vs Cost\",\n", + " model_names=models,\n", + " x_col=\"cost__inc_cum\",\n", + " y_col=\"spend__inc_cum\",\n", + " x_label=\"Cost\",\n", + " y_label=\"Incremental Revenue\",\n", + " x_format=\"${:,.0f}\",\n", + " y_format=\"${:,.0f}\",\n", + " show_legend=False,\n", + " x_lim=[0, None],\n", + " y_lim=[0, None],\n", + ")\n", + "\n", + "plot_cumulative_incrementality(\n", + " axs[1],\n", + " results,\n", + " title=\"iRoI vs Incremental Revenue\",\n", + " model_names=models,\n", + " x_col=\"spend__inc_cum\",\n", + " y_col=\"roi__inc_cum\",\n", + " x_label=\"Incremental Revenue\",\n", + " y_label=\"iRoI\",\n", + " x_format=\"${:,.0f}\",\n", + " y_format=\"{:.1f}\",\n", + " x_lim=[0, None],\n", + " y_lim=[0, 5]\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "eca57559", + "metadata": {}, + "source": [ + "이번 경우에서도 fractional uplift 모델이 기존 uplift 모델보다 더 우수한 성과를 보입니다. 일반적인 spend T-Learner 꽤 괜찮은 성능을 내지만, RoI를 최적화한 fractional learner에는 미치지 못합니다. \n", + "\n", + "또한 이 경우에는 distilled 버전이 전체(full) 모델의 성능을 충분히 잘 따라가지 못하는 것으로 보이며, 추가적인 튜닝이 필요함을 시사합니다." + ] + }, + { + "cell_type": "markdown", + "id": "a7289d07", + "metadata": {}, + "source": [ + "### Objective 3: Maximum Incremental Conversions with an RoI Constraint\n", + "\n", + "마지막으로 살펴볼 경우는 iRoI가 특정 목표 값 이하로 떨어지지 않도록 유지하면서, 가능한 한 많은 추가 전환(incremental conversions) 을 만들어내는 상황입니다.\n", + "\n", + "이 예제에서는 iRoI 목표값을 2.0으로 설정합니다." + ] + }, + { + "cell_type": "markdown", + "id": "8c6e744f", + "metadata": {}, + "source": [ + "#### Fractional uplift model\n", + "\n", + "이 문제는 constraint가 포함되어 있어 조금 더 복잡하지만, fractional uplift 설정을 통해 해결할 수 있습니다. 이를 위해 다음과 같이 설정합니다.\n", + "\n", + "* Maximize KPI ($\\alpha$) = Conversion \n", + "* Constraint KPI ($\\beta$) = Cost \n", + "* Constraint Offset KPI ($\\gamma$) = Spend \n", + "* Constraint Offset Scale ($\\delta$) = iRoI target \n", + "\n", + "이 설정은 uplift 모델이 다음과 같은 값을 추정하도록 만듭니다.\n", + "\n", + "$$\n", + "f_\\delta(X)=\n", + "\\begin{cases}\n", + " \\frac{N_{\\text{convert}, \\, T=1} - N_{\\text{convert}, \\, T=0}}{\\text{Cost}_{T=1} - \\frac{\\text{Spend}_\\text{T=1} - \\text{Spend}_\\text{T=0}}{\\text{iRoI}_\\text{target}}},& \\text{Cost}_{T=1} > \\frac{\\text{Spend}_\\text{T=1} - \\text{Spend}_\\text{T=0}}{\\text{iRoI}_\\text{target}}\\\\\n", + " \\infty, & \\text{otherwise}\n", + "\\end{cases}\n", + "$$\n", + "\n", + "이를 직관적으로 보면 다음과 같이 해석할 수 있습니다.\n", + "\n", + "1. 사용자의 iRoI가 iRoI target보다 높다면, 항상 해당 사용자를 타겟팅합니다.\n", + "2. iRoI가 target보다 낮은 경우에는, \n", + " incremental conversions을 얼마나 만들어내는지를 \n", + " net cost으로 나눈 값으로 사용자를 정렬합니다. \n", + " 여기서 순비용은 iRoI target을 초과해서 발생하는 비용을 의미합니다. \n", + " 즉, iRoI target을 가장 적게 훼손하면서 가장 많은 incremental conversions를 만드는 사용자부터 타겟팅합니다.\n", + "\n", + "아래에서는 이를 실제로 구현합니다." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "263b9561", + "metadata": {}, + "outputs": [], + "source": [ + "target_roi = 2.0\n", + "\n", + "train_data = fr.datasets.PandasTrainData(\n", + " features_data=criteo.train_data[criteo.features],\n", + " maximize_kpi=criteo.train_data[\"conversion\"].values,\n", + " constraint_kpi=criteo.train_data[\"cost\"].values,\n", + " constraint_offset_kpi=criteo.train_data[\"spend\"].values,\n", + " is_treated=criteo.train_data[\"treatment\"].values,\n", + " treatment_propensity=criteo.train_data[\"treatment_propensity\"].values,\n", + " sample_weight=criteo.train_data[\"sample_weight\"].values,\n", + " shuffle_seed=1234\n", + ")\n", + "fractional_t_learner = fr.meta_learners.FractionalLearner(get_base_regressor())\n", + "fractional_t_learner.fit(train_data)\n", + "\n", + "distill_fractional_t_learner = get_base_regressor()\n", + "fractional_t_learner.distill(distill_dataset, distill_fractional_t_learner, constraint_offset_scale=target_roi)\n", + "\n", + "criteo.test_data[f\"roi_constrained_conversion_score\"] = fractional_t_learner.predict(\n", + " test_dataset, constraint_offset_scale=target_roi\n", + ")\n", + "criteo.test_data[f\"roi_constrained_conversion_score_distill\"] = distill_fractional_t_learner.predict(test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "2a9a778f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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spend_t_learner_score_distill cpia_score \\\n", + "1007 0.217223 0.484556 0.025971 \n", + "1080 0.661332 0.196871 0.048494 \n", + "1295 -12.409683 -8.438211 -0.363601 \n", + "1670 -10.253737 -9.813921 -0.158326 \n", + "1940 0.003596 -0.081532 0.113706 \n", + "\n", + " cpia_score_distill roi_score roi_score_distill \\\n", + "1007 0.011292 0.160407 -1.164981 \n", + "1080 0.030863 2.041546 1.063659 \n", + "1295 -0.103153 -19.358949 -1.164981 \n", + "1670 -0.219133 -11.766439 -1.164981 \n", + "1940 0.011170 0.173577 0.932336 \n", + "\n", + " roi_constrained_conversion_score \\\n", + "1007 0.028235 \n", + "1080 inf \n", + "1295 -0.034047 \n", + "1670 -0.023002 \n", + "1940 0.124512 \n", + "\n", + " roi_constrained_conversion_score_distill \n", + "1007 30.174107 \n", + "1080 30.259451 \n", + "1295 10.612205 \n", + "1670 0.024123 \n", + "1940 3.947203 \n", + "\n", + "[5 rows x 29 columns]" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "criteo.test_data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c72e932f", + "metadata": {}, + "source": [ + "#### Evaluation\n", + "\n", + "이를 평가하기 위해, iRoI가 목표 iRoI 값인 2.0과 같아지는 지점에서 달성할 수 있는 최대 전환 수를 확인합니다. \n", + "\n", + "아래에서는 다시 한 번 uplift evaluator를 사용해, incremental conversions에 따른 iRoI의 변화를 그래프로 시각화합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "a40a2611", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluator = RoIUpliftEvaluator(\n", + " is_treated_col=\"treatment\",\n", + " treatment_propensity_col=\"treatment_propensity\",\n", + " effect_type=fr.EffectType.ATE\n", + ")\n", + "\n", + "models = {\n", + " \"spend_t_learner_score\": \"Spend T-Learner\",\n", + " \"conversion_t_learner_score\": \"Conversion T-Learner\",\n", + " \"roi_constrained_conversion_score\": \"RoI Constrained Fractional T-Learner\",\n", + " \"roi_constrained_conversion_score_distill\": \"RoI Constrained Fractional T-Learner\",\n", + "}\n", + "\n", + "results = evaluator.evaluate(criteo.test_data, score_cols=list(models.keys()))\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 4), constrained_layout=True)\n", + "\n", + "roi_plot = plot_cumulative_incrementality(\n", + " ax,\n", + " results,\n", + " title=\"RoI vs Incremental Conversion\",\n", + " model_names=models,\n", + " x_col=\"conversion__inc_cum\",\n", + " y_col=\"roi__inc_cum\",\n", + " x_label=\"Incremental Conversions\",\n", + " y_label=\"iRoI\",\n", + " x_format=\"{:,.0f}\",\n", + " y_format=\"{:.1f}\",\n", + " x_lim=[0, None],\n", + " y_lim=[0, 5]\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d9e251b3", + "metadata": {}, + "source": [ + "fractional learner가 단순한 T-learner들보다 우수한 성과를 보인다는 점이 분명하게 드러납니다.\n", + "fractional learner는 iRoI가 2.0인 조건에서 약 3,000건의 incremental conversions을 달성합니다.\n", + "\n", + "반면, conversion T-learner는 iRoI 2.0에 도달하지 못하며,\n", + "Spend T-learner 역시 iRoI가 2.0일 때 약 1,000건의 incremental conversions만을 만들어낼 수 있습니다." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}