diff --git a/econml/tests/test_notebooks.py b/econml/tests/test_notebooks.py index 9bc13d2d8..8a38cad75 100644 --- a/econml/tests/test_notebooks.py +++ b/econml/tests/test_notebooks.py @@ -9,9 +9,10 @@ import traitlets _nbdir = os.path.join(os.path.dirname(__file__), '..', '..', 'notebooks') -_notebooks = [path - for path in os.listdir(_nbdir) - if path.endswith('.ipynb')] +_nbsubdirs = ['.', 'CustomerScenarios'] # TODO: add AutoML notebooks +_notebooks = [ + os.path.join(subdir, path) for subdir + in _nbsubdirs for path in os.listdir(os.path.join(_nbdir, subdir)) if path.endswith('.ipynb')] @pytest.mark.parametrize("file", _notebooks) diff --git a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb new file mode 100644 index 000000000..2d1032261 --- /dev/null +++ b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb @@ -0,0 +1,865 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customer Segmentation -- Estimate Individualized Responses to Incentives\n", + "\n", + "Nowadays, business decision makers rely on estimating the causal effect of interventions to answer what-if questions about shifts in strategy, such as promoting specific product with discount, adding new features to a website or increasing investment from a sales team. However, rather than learning whether to take action for a specific intervention for all users, people are increasingly interested in understanding the different responses from different users to the two alternatives. Identifying the characteristics of users having the strongest response for the intervention could help make rules to segment the future users into different groups. This can help optimize the policy to use the least resources and get the most profit.\n", + "\n", + "In this case study, we will use a personalized pricing example to explain how the [EconML](https://aka.ms/econml) library could fit into this problem and provide robust and reliable causal solutions.\n", + "\n", + "### Summary\n", + "\n", + "1. [Background](#background)\n", + "2. [Data](#data)\n", + "3. [Get Causal Effects with EconML](#estimate)\n", + "4. [Understand Treatment Effects with EconML](#interpret)\n", + "5. [Make Policy Decisions with EconML](#policy)\n", + "6. [Conclusions](#conclusion)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Background \n", + "\n", + "\n", + "\n", + "The global online media market is growing fast over the years. Media companies are always interested in attracting more users into the market and encouraging them to buy more songs or become members. In this example, we'll consider a scenario where one experiment a media company is running is to give small discount (10%, 20% or 0) to their current users based on their income level in order to boost the likelihood of their purchase. The goal is to understand the **heterogeneous price elasticity of demand** for people with different income level, learning which users would respond most strongly to a small discount. Furthermore, their end goal is to make sure that despite decreasing the price for some consumers, the demand is raised enough to boost the overall revenue.\n", + "\n", + "EconML’s `DMLCateEstimator` based estimators can be used to take the discount variation in existing data, along with a rich set of user features, to estimate heterogeneous price sensitivities that vary with multiple customer features. Then, the `SingleTreeCateInterpreter` provides a presentation-ready summary of the key features that explain the biggest differences in responsiveness to a discount, and the `SingleTreePolicyInterpreter` recommends a policy on who should receive a discount in order to increase revenue (not only demand), which could help the company to set an optimal price for those users in the future. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Some imports to get us started\n", + "# Utilities\n", + "import os\n", + "import urllib.request\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Generic ML imports\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "# EconML imports\n", + "from econml.dml import LinearDMLCateEstimator, ForestDMLCateEstimator\n", + "from econml.cate_interpreter import SingleTreeCateInterpreter, SingleTreePolicyInterpreter\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data \n", + "\n", + "\n", + "The dataset* has ~10,000 observations and includes 9 continuous and categorical variables that represent user's characteristics and online behaviour history such as age, log income, previous purchase, previous online time per week, etc. \n", + "\n", + "We define the following variables:\n", + "\n", + "Feature Name|Type|Details \n", + ":--- |:---|:--- \n", + "**account_age** |W| user's account age\n", + "**age** |W|user's age\n", + "**avg_hours** |W| the average hours user was online per week in the past\n", + "**days_visited** |W| the average number of days user visited the website per week in the past\n", + "**friend_count** |W| number of friends user connected in the account \n", + "**has_membership** |W| whether the user had membership\n", + "**is_US** |W| whether the user accesses the website from the US \n", + "**songs_purchased** |W| the average songs user purchased per week in the past\n", + "**income** |X| user's income\n", + "**price** |T| the price user was exposed during the discount season (baseline price * samll discount)\n", + "**demand** |Y| songs user purchased during the discount season\n", + "\n", + "**To protect the privacy of the company, we use the simulated data as an example here. The data is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*\n", + "\n", + "\n", + "The treatment and outcome are generated using the following functions:\n", + "$$\n", + "T = \n", + "\\begin{cases}\n", + " 1 & \\text{with } p=0.2, \\\\\n", + " 0.9 & \\text{with }p=0.3, & \\text{if income}<1 \\\\\n", + " 0.8 & \\text{with }p=0.5, \\\\\n", + " \\\\\n", + " 1 & \\text{with }p=0.7, \\\\\n", + " 0.9 & \\text{with }p=0.2, & \\text{if income}\\ge1 \\\\\n", + " 0.8 & \\text{with }p=0.1, \\\\\n", + "\\end{cases}\n", + "$$\n", + "\n", + "\n", + "\\begin{align}\n", + "\\gamma(X) & = -3 - 14 \\cdot \\{\\text{income}<1\\} \\\\\n", + "\\beta(X,W) & = 20 + 0.5 \\cdot \\text{avg_hours} + 5 \\cdot \\{\\text{days_visited}>4\\} \\\\\n", + "Y &= \\gamma(X) \\cdot T + \\beta(X,W)\n", + "\\end{align}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the sample pricing data\n", + "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/Pricing/pricing_sample.csv\"\n", + "train_data = pd.read_csv(file_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " account_age age avg_hours days_visited friends_count has_membership \\\n", + "0 3 53 1.834234 2 8 1 \n", + "1 5 54 7.171411 7 9 0 \n", + "2 3 33 5.351920 6 9 0 \n", + "3 2 34 6.723551 0 8 0 \n", + "4 4 30 2.448247 5 8 1 \n", + "\n", + " is_US songs_purchased income price demand \n", + "0 1 4.903237 0.960863 1.0 3.917117 \n", + "1 1 3.330161 0.732487 1.0 11.585706 \n", + "2 1 3.036203 1.130937 1.0 24.675960 \n", + "3 1 7.911926 0.929197 1.0 6.361776 \n", + "4 0 7.148967 0.533527 0.8 12.624123 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Data sample\n", + "train_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Define estimator inputs\n", + "Y = train_data[\"demand\"] # outcome of interest\n", + "T = train_data[\"price\"] # intervention, or treatment\n", + "X = train_data[[\"income\"]] # features\n", + "W = train_data.drop(columns=[\"demand\", \"price\", \"income\"]) # confounders" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Get test data\n", + "X_test = np.linspace(0, 5, 100).reshape(-1, 1)\n", + "X_test_data = pd.DataFrame(X_test, columns=[\"income\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get Causal Effects with EconML \n", + "To learn the price elasticity on demand as a function of income, we fit the model as follows:\n", + "\n", + "\n", + "\\begin{align}\n", + "log(Y) & = \\theta(X) \\cdot log(T) + f(X,W) + \\epsilon \\\\\n", + "log(T) & = g(X,W) + \\eta\n", + "\\end{align}\n", + "\n", + "\n", + "where $\\epsilon, \\eta$ are uncorrelated error terms. \n", + "\n", + "The models we fit here aren't an exact match for the data generation function above, but if they are a good approximation, they will allow us to create a good discount policy. Although the model is misspecified, we hope to see that our `DMLCateEstimator` based estimators can still capture the right trend of $\\theta(X)$ and that the recommended policy beats other baseline policies (such as always giving a discount) on revenue. Because of the mismatch between the data generating process and the model we're fitting, there isn't a single true $\\theta(X)$ (the true elasticity varies with not only X but also T and W), but given how we generate the data above, we can still calculate the range of true $\\theta(X)$ to compare against." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Define underlying treatment effect function given DGP\n", + "def gamma_fn(X):\n", + " return -3 - 14 * (X[\"income\"] < 1)\n", + "\n", + "def beta_fn(X):\n", + " return 20 + 0.5 * (X[\"avg_hours\"]) + 5 * (X[\"days_visited\"] > 4)\n", + "\n", + "def demand_fn(data, T):\n", + " Y = gamma_fn(data) * T + beta_fn(data)\n", + " return Y\n", + "\n", + "def true_te(x, n, stats):\n", + " if x < 1:\n", + " subdata = train_data[train_data[\"income\"] < 1].sample(n=n, replace=True)\n", + " else:\n", + " subdata = train_data[train_data[\"income\"] >= 1].sample(n=n, replace=True)\n", + " te_array = subdata[\"price\"] * gamma_fn(subdata) / (subdata[\"demand\"])\n", + " if stats == \"mean\":\n", + " return np.mean(te_array)\n", + " elif stats == \"median\":\n", + " return np.median(te_array)\n", + " elif isinstance(stats, int):\n", + " return np.percentile(te_array, stats)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the estimate and range of true treatment effect\n", + "truth_te_estimate = np.apply_along_axis(true_te, 1, X_test, 1000, \"mean\") # estimate\n", + "truth_te_upper = np.apply_along_axis(true_te, 1, X_test, 1000, 95) # upper level\n", + "truth_te_lower = np.apply_along_axis(true_te, 1, X_test, 1000, 5) # lower level" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parametric heterogeneity\n", + "First of all, we can try to learn a **linear projection of the treatment effect** assuming a polynomial form of $\\theta(X)$. We use the `LinearDMLCateEstimator` estimator. Since we don't have any priors on these models, we use a generic gradient boosting tree estimators to learn the expected price and demand from the data." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Get log_T and log_Y\n", + "log_T = np.log(T)\n", + "log_Y = np.log(Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Train EconML model\n", + "est = LinearDMLCateEstimator(\n", + " model_y=GradientBoostingRegressor(),\n", + " model_t=GradientBoostingRegressor(),\n", + " featurizer=PolynomialFeatures(degree=2, include_bias=False),\n", + ")\n", + "est.fit(log_Y, log_T, X, W, inference=\"statsmodels\")\n", + "# Get treatment effect and its confidence interval\n", + "te_pred = est.effect(X_test)\n", + "te_pred_interval = est.effect_interval(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compare the estimate and the truth\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\n", + "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\n", + "plt.fill_between(\n", + " X_test.flatten(),\n", + " te_pred_interval[0],\n", + " te_pred_interval[1],\n", + " alpha=0.2,\n", + " label=\"90% Confidence Interval\",\n", + ")\n", + "plt.fill_between(\n", + " X_test.flatten(),\n", + " truth_te_lower,\n", + " truth_te_upper,\n", + " alpha=0.2,\n", + " label=\"True Elasticity Range\",\n", + ")\n", + "plt.xlabel(\"Income\")\n", + "plt.ylabel(\"Songs Sales Elasticity\")\n", + "plt.title(\"Songs Sales Elasticity vs Income\")\n", + "plt.legend(loc=\"lower right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot above, it's clear to see that the true treatment effect is a **nonlinear** function of income, with elasticity around -1.75 when income is smaller than 1 and a small negative value when income is larger than 1. The model fits a quadratic treatment effect, which is not a great fit. But it still captures the overall trend: the elasticity is negative and people are less sensitive to the price change if they have higher income." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
income 2.451 0.065 37.659 0.0 2.344 2.558
income^2 -0.443 0.022 -20.517 0.0 -0.479 -0.408
\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
intercept -3.04 0.042 -72.165 0.0 -3.109 -2.97
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Coefficient Results \n", + "===============================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "---------------------------------------------------------------\n", + "income 2.451 0.065 37.659 0.0 2.344 2.558\n", + "income^2 -0.443 0.022 -20.517 0.0 -0.479 -0.408\n", + " Intercept Results \n", + "================================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "----------------------------------------------------------------\n", + "intercept -3.04 0.042 -72.165 0.0 -3.109 -2.97\n", + "----------------------------------------------------------------\n", + "\"\"\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the final coefficient and intercept summary\n", + "est.summary(feat_name=X.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`LinearDMLCateEstimator` estimator can also return the summary of the coefficients and intercept for the final model, including point estimates, p-values and confidence intervals. From the table above, we notice that $income$ has positive effect and ${income}^2$ has negative effect, and both of them are statistically significant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nonparametric Heterogeneity\n", + "Since we already know the true treatment effect function is nonlinear, let us fit another model using `ForestDMLCateEstimator`, which assumes a fully **nonparametric estimation of the treatment effect**." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Train EconML model\n", + "est = ForestDMLCateEstimator(\n", + " model_y=GradientBoostingRegressor(), model_t=GradientBoostingRegressor()\n", + ")\n", + "est.fit(log_Y, log_T, X, W, inference=\"blb\")\n", + "# Get treatment effect and its confidence interval\n", + "te_pred = est.effect(X_test)\n", + "te_pred_interval = est.effect_interval(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compare the estimate and the truth\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\n", + "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\n", + "plt.fill_between(\n", + " X_test.flatten(),\n", + " te_pred_interval[0],\n", + " te_pred_interval[1],\n", + " alpha=0.2,\n", + " label=\"90% Confidence Interval\",\n", + ")\n", + "plt.fill_between(\n", + " X_test.flatten(),\n", + " truth_te_lower,\n", + " truth_te_upper,\n", + " alpha=0.2,\n", + " label=\"True Elasticity Range\",\n", + ")\n", + "plt.xlabel(\"Income\")\n", + "plt.ylabel(\"Songs Sales Elasticity\")\n", + "plt.title(\"Songs Sales Elasticity vs Income\")\n", + "plt.legend(loc=\"lower right\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We notice that this model fits much better than the `LinearDMLCateEstimator`, the 90% confidence interval correctly covers the true treatment effect estimate and captures the variation when income is around 1. Overall, the model shows that people with low income are much more sensitive to the price changes than higher income people." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understand Treatment Effects with EconML \n", + "EconML includes interpretability tools to better understand treatment effects. Treatment effects can be complex, but oftentimes we are interested in simple rules that can differentiate between users who respond positively, users who remain neutral and users who respond negatively to the proposed changes.\n", + "\n", + "The EconML `SingleTreeCateInterpreter` provides interperetability by training a single decision tree on the treatment effects outputted by the any of the EconML estimators. In the figure below we can see in dark red users respond strongly to the discount and the in white users respond lightly to the discount." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\n", + "intrp.interpret(est, X_test)\n", + "plt.figure(figsize=(25, 5))\n", + "intrp.plot(feature_names=X.columns, fontsize=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Make Policy Decision with EconML \n", + "We want to make policy decisions to maximum the **revenue** instead of the demand. In this scenario,\n", + "\n", + "\n", + "\\begin{align}\n", + "Rev & = Y \\cdot T \\\\\n", + " & = \\exp^{log(Y)} \\cdot T\\\\\n", + " & = \\exp^{(\\theta(X) \\cdot log(T) + f(X,W) + \\epsilon)} \\cdot T \\\\\n", + " & = \\exp^{(f(X,W) + \\epsilon)} \\cdot T^{(\\theta(X)+1)}\n", + "\\end{align}\n", + "\n", + "\n", + "With the decrease of price, revenue will increase only if $\\theta(X)+1<0$. Thus, we set `sample_treatment_cast=-1` here to learn **what kinds of customers we should give a small discount to maximum the revenue**.\n", + "\n", + "The EconML library includes policy interpretability tools such as `SingleTreePolicyInterpreter` that take in a treatment cost and the treatment effects to learn simple rules about which customers to target profitably. In the figure below we can see the model recommends to give discount for people with income less than $0.985$ and give original price for the others." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=1, min_impurity_decrease=0.001)\n", + "intrp.interpret(est, X_test, sample_treatment_costs=-1, treatment_names=[\"Discount\", \"No-Discount\"])\n", + "plt.figure(figsize=(25, 5))\n", + "intrp.plot(feature_names=X.columns, fontsize=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us compare our policy with other baseline policies! Our model says which customers to give a small discount to, and for this experiment, we will set a discount level of 10% for those users. Because the model is misspecified we would not expect good results with large discounts. Here, because we know the ground truth, we can evaluate the value of this policy." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# define function to compute revenue\n", + "def revenue_fn(data, discount_level1, discount_level2, baseline_T, policy):\n", + " policy_price = baseline_T * (1 - discount_level1) * policy + baseline_T * (1 - discount_level2) * (1 - policy)\n", + " demand = demand_fn(data, policy_price)\n", + " rev = demand * policy_price\n", + " return rev" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "policy_dic = {}\n", + "# our policy above\n", + "policy = intrp.treat(X)\n", + "policy_dic[\"Our Policy\"] = np.mean(revenue_fn(train_data, 0, 0.1, 1, policy))\n", + "\n", + "## previous strategy\n", + "policy_dic[\"Previous Strategy\"] = np.mean(train_data[\"price\"] * train_data[\"demand\"])\n", + "\n", + "## give everyone discount\n", + "policy_dic[\"Give Everyone Discount\"] = np.mean(revenue_fn(train_data, 0.1, 0, 1, np.ones(len(X))))\n", + "\n", + "## don't give discount\n", + "policy_dic[\"Give No One Discount\"] = np.mean(revenue_fn(train_data, 0, 0.1, 1, np.ones(len(X))))\n", + "\n", + "## follow our policy, but give -10% discount for the group doesn't recommend to give discount\n", + "policy_dic[\"Our Policy + Give Negative Discount for No-Discount Group\"] = np.mean(revenue_fn(train_data, -0.1, 0.1, 1, policy))\n", + "\n", + "## give everyone -10% discount\n", + "policy_dic[\"Give Everyone Negative Discount\"] = np.mean(revenue_fn(train_data, -0.1, 0, 1, np.ones(len(X))))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RevenueRank
Our Policy14.6862412.0
Previous Strategy14.3493424.0
Give Everyone Discount13.7744696.0
Give No One Discount14.2946065.0
Our Policy + Give Negative Discount for No-Discount Group15.5644111.0
Give Everyone Negative Discount14.6126703.0
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" + ], + "text/plain": [ + " Revenue Rank\n", + "Our Policy 14.686241 2.0\n", + "Previous Strategy 14.349342 4.0\n", + "Give Everyone Discount 13.774469 6.0\n", + "Give No One Discount 14.294606 5.0\n", + "Our Policy + Give Negative Discount for No-Disc... 15.564411 1.0\n", + "Give Everyone Negative Discount 14.612670 3.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get policy summary table\n", + "res = pd.DataFrame.from_dict(policy_dic, orient=\"index\", columns=[\"Revenue\"])\n", + "res[\"Rank\"] = res[\"Revenue\"].rank(ascending=False)\n", + "res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**We beat the baseline policies!** Our policy gets the highest revenue except for the one raising the price for the No-Discount group. That means our currently baseline price is low, but the way we segment the user does help increase the revenue!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusions \n", + "\n", + "In this notebook, we have demonstrated the power of using EconML to:\n", + "\n", + "* Estimate the treatment effect correctly even the model is misspecified\n", + "* Interpret the resulting individual-level treatment effects\n", + "* Make the policy decision beats the previous and baseline policies\n", + "\n", + "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [docummentation](https://econml.azurewebsites.net/). " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb new file mode 100644 index 000000000..716a72576 --- /dev/null +++ b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb @@ -0,0 +1,770 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "\n", + "# Recommendation A/B Testing: Experimentation with Imperfect Compliance\n", + "\n", + "An online business would like to test a new feature or offering of their website and learn its effect on downstream revenue. Furthermore, they would like to know which kind of users respond best to the new version. We call the user-specfic effect a **heterogeneous treatment effect**. \n", + "\n", + "Ideally, the business would run an A/B tests between the old and new versions of the website. However, a direct A/B test might not work because the business cannot force the customers to take the new offering. Measuring the effect in this way will be misleading since not every customer exposed to the new offering will take it.\n", + "\n", + "The business also cannot look directly at existing data as it will be biased: the users who use the latest website features are most likely the ones who are very engaged on the website and hence spend more on the company's products to begin with. Estimating the effect this way would be overly optimistic." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "In this customer scenario walkthough, we show how tools from the [EconML](https://aka.ms/econml) library can still use a direct A/B test and mitigate these shortcomings.\n", + "\n", + "### Summary\n", + "\n", + "1. [Background](#Background)\n", + "2. [Data](#Data)\n", + "3. [Get Causal Effects with EconML](#Get-Causal-Effects-with-EconML)\n", + "4. [Understand Treatment Effects with EconML](#Understand-Treatment-Effects-with-EconML)\n", + "5. [Make Policy Decisions with EconML](#Make-Policy-Decisions-with-EconML)\n", + "6. [Conclusions](#Conclusions)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Background\n", + "\n", + "\n", + "\n", + "In this scenario, a travel website would like to know whether joining a membership program compels users to spend more time engaging with the website and purchasing more products. \n", + "\n", + "A direct A/B test is infeasible because the website cannot force users to become members. Likewise, the travel company can’t look directly at existing data, comparing members and non-members, because the customers who chose to become members are likely already more engaged than other users. \n", + "\n", + "**Solution:** The company had run an earlier experiment to test the value of a new, faster sign-up process. EconML's IV estimators can exploit this experimental nudge towards membership as an instrument that generates random variation in the likelihood of membership. This is known as an **intent-to-treat** setting: the intention is to give a random group of user the \"treatment\" (access to the easier sign-up process), but not not all users will actually take it. \n", + "\n", + "EconML's `IntentToTreatDRIV` estimator model takes advantage of the fact that not every customer who was offered the easier sign-up became a member to learn the effect of membership rather than the effect of receiving the quick sign-up." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "# Some imports to get us started\n", + "# Utilities\n", + "import os\n", + "import urllib.request\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Generic ML imports\n", + "import lightgbm as lgb\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "\n", + "# EconML imports\n", + "from econml.ortho_iv import LinearIntentToTreatDRIV\n", + "from econml.cate_interpreter import SingleTreeCateInterpreter, \\\n", + " SingleTreePolicyInterpreter\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Data\n", + "\n", + "The data* is comprised of:\n", + " * Features collected in the 28 days prior to the experiment (denoted by the suffix `_pre`)\n", + " * Experiment variables (whether the use was exposed to the easier signup -> the instrument, and whether the user became a member -> the treatment)\n", + " * Variables collected in the 28 days after the experiment (denoted by the suffix `_post`).\n", + "\n", + "Feature Name | Details \n", + ":--- |: --- \n", + "**days_visited_exp_pre** |#days a user visits the attractions pages \n", + "**days_visited_free_pre** | #days a user visits the website through free channels (e.g. domain direct) \n", + "**days_visited_fs_pre** | #days a user visits the flights pages \n", + "**days_visited_hs_pre** | #days a user visits the hotels pages \n", + "**days_visited_rs_pre** | #days a user visits the restaurants pages \n", + "**days_visited_vrs_pre** | #days a user visits the vacation rental pages \n", + "**locale_en_US** | whether the user access the website from the US \n", + "**os_type** | user's operating system (windows, osx, other) \n", + "**revenue_pre** | how much the user spent on the website in the pre-period \n", + "**easier_signup** | whether the user was exposed to the easier signup process \n", + "**became_member** | whether the user became a member \n", + "**days_visited_post** | #days a user visits the website in the 28 days after the experiment \n", + "\n", + "\n", + "**To protect the privacy of the travel company's users, the data used in this scenario is synthetically generated and the feature distributions don't correspond to real distributions. However, the feature names have preserved their names and meaning.*" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "# Import the sample AB data\n", + "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/RecommendationAB/ab_sample.csv\" \n", + "ab_data = pd.read_csv(file_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " days_visited_exp_pre days_visited_free_pre days_visited_fs_pre \\\n", + "0 1 9 7 \n", + "1 10 25 27 \n", + "2 18 14 8 \n", + "3 17 0 23 \n", + "4 24 9 22 \n", + "\n", + " days_visited_hs_pre days_visited_rs_pre days_visited_vrs_pre \\\n", + "0 25 6 3 \n", + "1 10 27 27 \n", + "2 4 5 2 \n", + "3 2 3 1 \n", + "4 2 3 18 \n", + "\n", + " locale_en_US revenue_pre os_type_osx os_type_windows easier_signup \\\n", + "0 1 0.01 0 1 0 \n", + "1 0 2.26 0 0 0 \n", + "2 1 0.03 0 1 0 \n", + "3 1 418.77 0 1 0 \n", + "4 1 1.54 0 0 0 \n", + "\n", + " became_member days_visited_post \n", + "0 0 1 \n", + "1 0 15 \n", + "2 0 17 \n", + "3 0 6 \n", + "4 0 12 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Data sample\n", + "ab_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# Define estimator inputs\n", + "Z = ab_data['easier_signup'] # nudge, or instrument\n", + "T = ab_data['became_member'] # intervention, or treatment\n", + "Y = ab_data['days_visited_post'] # outcome of interest\n", + "X_data = ab_data.drop(columns=['easier_signup', 'became_member', 'days_visited_post']) # features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "The data was generated using the following undelying treatment effect function:\n", + "\n", + "$$\n", + "\\text{treatment_effect} = 0.2 + 0.3 \\cdot \\text{days_visited_free_pre} - 0.2 \\cdot \\text{days_visited_hs_pre} + \\text{os_type_osx}\n", + "$$\n", + "\n", + "The interpretation of this is that users who visited the website before the experiment and/or who use an iPhone tend to benefit from the membership program, whereas users who visited the hotels pages tend to be harmed by membership. **This is the relationship we seek to learn from the data.**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "# Define underlying treatment effect function \n", + "TE_fn = lambda X: (0.2 + 0.3 * X['days_visited_free_pre'] - 0.2 * X['days_visited_hs_pre'] + X['os_type_osx']).values\n", + "true_TE = TE_fn(X_data)\n", + "\n", + "# Define the true coefficients to compare with\n", + "true_coefs = np.zeros(X_data.shape[1])\n", + "true_coefs[[1, 3, -2]] = [0.3, -0.2, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Get Causal Effects with EconML\n", + "\n", + "To learn a linear projection of the treatment effect, we use the `LinearIntentToTreatDRIV` EconML estimator. For a more flexible treatment effect function, use the `IntentToTreatDRIV` estimator instead. \n", + "\n", + "The model requires to define some nuissance models (i.e. models we don't really care about but that matter for the analysis): the model for how the outcome $Y$ depends on the features $X$ (`model_Y_X`) and the model for how the treatment $T$ depends on the instrument $Z$ and features $X$ (`model_T_XZ`). Since we don't have any priors on these models, we use generic boosted tree estimators to learn them. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# Define nuissance estimators\n", + "lgb_T_XZ_params = {\n", + " 'objective' : 'binary',\n", + " 'metric' : 'auc',\n", + " 'learning_rate': 0.1,\n", + " 'num_leaves' : 30,\n", + " 'max_depth' : 5\n", + "}\n", + "\n", + "lgb_Y_X_params = {\n", + " 'metric' : 'rmse',\n", + " 'learning_rate': 0.1,\n", + " 'num_leaves' : 30,\n", + " 'max_depth' : 5\n", + "}\n", + "model_T_XZ = lgb.LGBMClassifier(**lgb_T_XZ_params)\n", + "model_Y_X = lgb.LGBMRegressor(**lgb_Y_X_params)\n", + "flexible_model_effect = lgb.LGBMRegressor(**lgb_Y_X_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train EconML model\n", + "model = LinearIntentToTreatDRIV(\n", + " model_Y_X = model_Y_X,\n", + " model_T_XZ = model_T_XZ,\n", + " flexible_model_effect = flexible_model_effect,\n", + " featurizer = PolynomialFeatures(degree=1, include_bias=False)\n", + ")\n", + "model.fit(Y.values, T, Z, X_data.values, inference=\"statsmodels\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "# Compare learned coefficients with true model coefficients\n", + "coef_indices = np.arange(model.coef_.shape[0])\n", + "# Calculate error bars\n", + "coef_error = np.asarray(model.coef__interval()) # 90% confidence interval for coefficients\n", + "coef_error[0, :] = model.coef_ - coef_error[0, :]\n", + "coef_error[1, :] = coef_error[1, :] - model.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.errorbar(coef_indices, model.coef_, coef_error, fmt=\"o\", label=\"Learned coefficients\\nand 90% confidence interval\")\n", + "plt.scatter(coef_indices, true_coefs, color='C1', label=\"True coefficients\")\n", + "plt.xticks(coef_indices, X_data.columns, rotation='vertical')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "We notice that the coefficients estimates are pretty close to the true coefficients for the linear treatment effect function. \n", + "\n", + "We can also use the `model.summary` function to get point estimates, p-values and confidence intervals. From the table below, we notice that only the **days_visited_free_pre**, **days_visited_hs_pre** and **os_type_osx** features are statistically significant (the confidence interval doesn't contain $0$, p-value < 0.05) for the treatment effect. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
days_visited_exp_pre 0.001 0.007 0.157 0.875 -0.01 0.012
days_visited_free_pre 0.285 0.007 38.361 0.0 0.273 0.297
days_visited_fs_pre -0.009 0.007 -1.257 0.209 -0.02 0.003
days_visited_hs_pre -0.191 0.007 -28.274 0.0 -0.202 -0.18
days_visited_rs_pre -0.0 0.007 -0.051 0.959 -0.011 0.011
days_visited_vrs_pre -0.001 0.007 -0.143 0.887 -0.012 0.01
locale_en_US -0.043 0.113 -0.381 0.703 -0.229 0.143
revenue_pre -0.0 0.0 -1.551 0.121 -0.0 0.0
os_type_osx 0.964 0.139 6.949 0.0 0.736 1.192
os_type_windows 0.034 0.138 0.244 0.807 -0.194 0.262
\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
intercept 0.539 0.27 1.996 0.046 0.095 0.983
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Coefficient Results \n", + "============================================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "----------------------------------------------------------------------------\n", + "days_visited_exp_pre 0.001 0.007 0.157 0.875 -0.01 0.012 \n", + "days_visited_free_pre 0.285 0.007 38.361 0.0 0.273 0.297 \n", + "days_visited_fs_pre -0.009 0.007 -1.257 0.209 -0.02 0.003 \n", + "days_visited_hs_pre -0.191 0.007 -28.274 0.0 -0.202 -0.18 \n", + "days_visited_rs_pre -0.0 0.007 -0.051 0.959 -0.011 0.011 \n", + "days_visited_vrs_pre -0.001 0.007 -0.143 0.887 -0.012 0.01 \n", + "locale_en_US -0.043 0.113 -0.381 0.703 -0.229 0.143 \n", + "revenue_pre -0.0 0.0 -1.551 0.121 -0.0 0.0 \n", + "os_type_osx 0.964 0.139 6.949 0.0 0.736 1.192 \n", + "os_type_windows 0.034 0.138 0.244 0.807 -0.194 0.262 \n", + " Intercept Results \n", + "==============================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "--------------------------------------------------------------\n", + "intercept 0.539 0.27 1.996 0.046 0.095 0.983 \n", + "--------------------------------------------------------------\n", + "\"\"\"" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.summary(feat_name=X_data.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "test_customers = X_data.iloc[:1000]\n", + "true_customer_TE = TE_fn(test_customers)\n", + "model_customer_TE = model.effect(test_customers)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# How close are the predicted treatment effect to the true treatment effects for 1000 users?\n", + "plt.scatter(true_customer_TE, model.effect(test_customers), label=\"Predicted vs True treatment effect\")\n", + "plt.xlabel(\"True treatment effect\")\n", + "plt.ylabel(\"Predicted treatment effect\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Understand Treatment Effects with EconML\n", + "\n", + "EconML includes interpretability tools to better understand treatment effects. Treatment effects can be complex, but oftentimes we are interested in simple rules that can differentiate between users who respond positively, users who remain neutral and users who respond negatively to the proposed changes.\n", + "\n", + "The EconML `SingleTreeCateInterpreter` provides interperetability by training a single decision tree on the treatment effects outputted by the any of the EconML estimators. In the figure below we can see in dark red users who respond negatively to the membership program and in dark green users who respond positively." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\n", + "intrp.interpret(model, test_customers)\n", + "plt.figure(figsize=(25, 5))\n", + "intrp.plot(feature_names=X_data.columns, fontsize=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Make Policy Decisions with EconML\n", + "\n", + "Interventions usually have a cost: incetivizing a user to become a member can be costly (e.g by offering a discount). Thus, we would like to know what customers to target to maximize the profit from their increased engagement. This is the **treatment policy**. \n", + "\n", + "The EconML library includes policy interpretability tools such as `SingleTreePolicyInterpreter` that take in a treatment cost and the treatment effects to learn simple rules about which customers to target profitably. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=10)\n", + "intrp.interpret(model, test_customers, sample_treatment_costs=0.2)\n", + "plt.figure(figsize=(25, 5))\n", + "intrp.plot(feature_names=X_data.columns, fontsize=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Conclusions\n", + "\n", + "In this notebook, we have demonstrated the power of using EconML to:\n", + "\n", + "* Get valid causal insights in seemingly impossible scenarios\n", + "* Intepret the resulting individual-level treatment effects\n", + "* Build policies around the learned effects\n", + "\n", + "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/microsoft/EconML) or our [docummentation](https://econml.azurewebsites.net/). " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/setup.cfg b/setup.cfg index 92644d323..d380dcdd4 100644 --- a/setup.cfg +++ b/setup.cfg @@ -57,6 +57,8 @@ tests_require = pytest-cov jupyter seaborn + lightgbm + dowhy [options.extras_require] automl =