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{
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"name": "python",
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"colab": {
"name": "ML_Accuracy_Blog.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/amysteier/3dcbc1bf15cab6bfdce252588ca36250/ml_accuracy_blog.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "failing-henry"
},
"source": [
"# Machine Learning Accuracy Using Synthetic Data\n",
"This notebook contains the code used in the Gretel blog \"Machine Learning Accuracy Using Synthetic Data\"\n"
],
"id": "failing-henry"
},
{
"cell_type": "markdown",
"metadata": {
"id": "J1crjFsXrqWP"
},
"source": [
"## Introduction\n",
"\n",
"One of the questions we hear often from developers working with synthetic data is “how well does synthetic data work for ML use cases versus the original dataset?” The question refers to whether the synthetic data can really be used to produce a machine learning model in par with what could have been created if the original data had been used. Let’s dive in and find out.\n",
"\n",
"Below is a table listing the datasets we've used, as well each dataset's row and column count. When training the synthetic model, all rows and columns were used with no modifications. All training parameters used default settings. We generate 5000 synthetic records for each dataset.\n",
"\n"
],
"id": "J1crjFsXrqWP"
},
{
"cell_type": "markdown",
"metadata": {
"id": "XXhq4ABaCof1"
},
"source": [
"## Datasets Used\n",
"<table><th>Kaggle Dataset</th><th>Rows</th><th>Columns</th><th>Classification Task</th>\n",
"<tr><td><a href=\"https://www.kaggle.com/blastchar/telco-customer-churn\">Telco Customer Churn</a></td><td>7043</td><td>20</td><td>Predict which customers will leave</td></tr>\n",
"<tr><td><a href=\"https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset\">IBM HR Attrition Dataset</a></td><td>4408</td><td>35</td><td>Predict which employees will leave</td></tr>\n",
"<tr><td><a href=\"https://www.kaggle.com/uciml/adult-census-income\">UCI Adult Census Income</a></td><td>32561</td><td>15</td><td>Predict who earns more than $50,000</td></tr>\n",
"<tr><td><a href=\"https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset\">UCI Credit Card Default</a></td><td>30000</td><<td>25</td><td>Predict default on a credit card payment</td></tr>\n",
"<tr><td><a href=\"https://www.kaggle.com/rahulsah06/bike-buying-prediction-for-adventure-works-cycles\">Adventure Works Bike Buying</a></td><td>16386</td><td>24</td><td>Predict customers who will buy a bike</td></tr>\n",
"<tr><td><a href=\"https://www.kaggle.com/fedesoriano/stroke-prediction-dataset\">Stroke Prediction Dataset</a></td><td>5109</td><td>12</td><td>Predict who will have a stroke</td></tr>\n",
"<tr><td><a href=\"https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists\">Data Scientist Job Candidates</a></td><td>19158</td><td>14</td><td>Predict which data scientists are willing to change jobs</td></tr>\n",
"<tr><td><a href=\"https://www.kaggle.com/janiobachmann/bank-marketing-dataset\">Bank Marketing Dataset</a></td><td>11162</td><td>17</td><td>Predict Term Deposit Subscriptions</td></tr>\n",
"</table>"
],
"id": "XXhq4ABaCof1"
},
{
"cell_type": "markdown",
"metadata": {
"id": "-pGYZM9bVdxe"
},
"source": [
"## Get things set up"
],
"id": "-pGYZM9bVdxe"
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Qr5m3Buq3j-O",
"outputId": "3e10b0e9-960b-4285-8154-cc0a324390df"
},
"source": [
"pip install category_encoders"
],
"id": "Qr5m3Buq3j-O",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting category_encoders\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/44/57/fcef41c248701ee62e8325026b90c432adea35555cbc870aff9cfba23727/category_encoders-2.2.2-py2.py3-none-any.whl (80kB)\n",
"\r\u001b[K |████ | 10kB 17.9MB/s eta 0:00:01\r\u001b[K |████████▏ | 20kB 10.9MB/s eta 0:00:01\r\u001b[K |████████████▏ | 30kB 9.2MB/s eta 0:00:01\r\u001b[K |████████████████▎ | 40kB 7.6MB/s eta 0:00:01\r\u001b[K |████████████████████▎ | 51kB 4.6MB/s eta 0:00:01\r\u001b[K |████████████████████████▍ | 61kB 5.3MB/s eta 0:00:01\r\u001b[K |████████████████████████████▍ | 71kB 5.6MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 81kB 3.4MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.19.5)\n",
"Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.4.1)\n",
"Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.22.2.post1)\n",
"Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.1.5)\n",
"Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.5.1)\n",
"Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.10.2)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.0.0)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2.8.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
"Installing collected packages: category-encoders\n",
"Successfully installed category-encoders-2.2.2\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IozOrYfi_9C3",
"outputId": "b3a483c5-16bb-4742-82f8-2fdf0b2e693f"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/gdrive')"
],
"id": "IozOrYfi_9C3",
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/gdrive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "handed-involvement",
"outputId": "1421b925-a190-405c-dcd6-86b7d6b0834b"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"from category_encoders.target_encoder import TargetEncoder\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.model_selection import StratifiedKFold\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.svm import LinearSVC \n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"from xgboost import XGBClassifier\n",
"import category_encoders as ce\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.naive_bayes import GaussianNB\n",
"import plotly.graph_objects as go"
],
"id": "handed-involvement",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" import pandas.util.testing as tm\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l04CCfqaVqON"
},
"source": [
"## Create model scoring function\n",
"For each dataset/model combination, we'll run a 5-fold stratified cross-validation 5 times. As shown below, we set up a pipeline to handle missing fields, encode categorical fields, standardize all fields and then run the model. We compute the mean accuracy across all runs."
],
"id": "l04CCfqaVqON"
},
{
"cell_type": "code",
"metadata": {
"id": "NytIO6fFZBNV"
},
"source": [
"def score_model(model, X, y):\n",
"\n",
" numeric_columns = list(X.select_dtypes(include=['int64', 'float64']).columns)\n",
" nominal_columns = list(X.select_dtypes(include=['object']).columns)\n",
" \n",
" numeric_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='constant', fill_value=0)), \n",
" ('scaler', StandardScaler())])\n",
" categorical_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
" ('encoder', TargetEncoder(smoothing=.2)),\n",
" ('scaler', StandardScaler())])\n",
" \n",
" preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', numeric_transformer, numeric_columns),\n",
" ('cat', categorical_transformer, nominal_columns)])\n",
"\n",
" pipe = Pipeline(steps=[('preprocessor', preprocessor),\n",
" ('model', model)])\n",
"\n",
" cv = StratifiedKFold(n_splits=5, shuffle=True)\n",
"\n",
" all_scores = []\n",
" for i in range(5):\n",
" all_scores += list(cross_val_score(pipe, X, y, scoring='accuracy', cv=cv, n_jobs=-1))\n",
"\n",
" mean_score = round((np.mean(all_scores) * 100), 2)\n",
" \n",
" return mean_score\n",
" "
],
"id": "NytIO6fFZBNV",
"execution_count": 25,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "K0_nnyafG0bu"
},
"source": [
"## Create results summary and graphing function"
],
"id": "K0_nnyafG0bu"
},
{
"cell_type": "code",
"metadata": {
"id": "union-equipment"
},
"source": [
"def compare_model_accur(dataset, train_accur, synth_accur):\n",
"\n",
" # Compute average accuracy for training\n",
" train_avg_accuracy = round((sum(train_accur.values()) / len(train_accur)), 2) \n",
"\n",
" # Compute average accuracy for synthetic\n",
" synth_avg_accuracy = round((sum(synth_accur.values()) / len(synth_accur)), 2)\n",
"\n",
" # Generate a graph for dataset showing original and synthetic accuracies per ML model type\n",
" labels = list(train_accur.keys())\n",
" train_colors = [\"purple\"] * len(train_accur)\n",
" synth_colors = [\"aqua\"] * len(synth_accur) \n",
" fig = go.Figure(\n",
" data=[\n",
" go.Bar(\n",
" name=\"Original\",\n",
" x=labels,\n",
" y=list(train_accur.values()),\n",
" text=list(train_accur.values()),\n",
" textposition='auto',\n",
" marker_color=train_colors\n",
" ),\n",
" go.Bar(\n",
" name=\"Synthetic\",\n",
" x=labels,\n",
" y=list(synth_accur.values()),\n",
" text=list(synth_accur.values()),\n",
" textposition='auto',\n",
" marker_color=synth_colors\n",
" )\n",
" ],\n",
" layout=go.Layout(\n",
" title=\"Dataset: \" + dataset,\n",
" yaxis_title=\"Accuracy %\",\n",
" xaxis_title=\"ML Algorithm\",\n",
" font=dict(\n",
" size=18,\n",
" color=\"RebeccaPurple\"),\n",
" width=1700,\n",
" height=550\n",
" )\n",
" )\n",
"\n",
" fig.show()\n",
"\n",
" return train_avg_accuracy, synth_avg_accuracy"
],
"id": "union-equipment",
"execution_count": 38,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "enfZY6h3QPUr"
},
"source": [
"## Now let's test each machine learning algorithm on each dataset"
],
"id": "enfZY6h3QPUr"
},
{
"cell_type": "code",
"metadata": {
"id": "LTrLfhX4eZDc"
},
"source": [
"# Our list of datasets\n",
"datasets = [\"Stroke_Prediction\", \"Data_Scientists_Job_Candidates\", \"Bank_Marketing_Dataset\", \"UCI_Adult_Census_Income\", \"Adventure_Works_Bike_Buying\", \"Telco_Customer_Churn\", \"IBM_HR_Attrition\", \"UCI_Credit_Card_Default\"]\n",
"targets = [\"stroke\", \"target\", \"deposit\", \"income\", \"BikeBuyer\", \"Churn\", \"Attrition\", \"default.payment.next.month\"]"
],
"id": "LTrLfhX4eZDc",
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hjR86I97eZ_2"
},
"source": [
"# Our list of models\n",
"models = []\n",
"models.append(('Logistic Regression', LogisticRegression()))\n",
"models.append(('LDA', LinearDiscriminantAnalysis()))\n",
"models.append(('KNN', KNeighborsClassifier()))\n",
"models.append(('CART', DecisionTreeClassifier()))\n",
"models.append(('Naive Bayes', GaussianNB()))\n",
"models.append(('SVM', LinearSVC()))\n",
"models.append(('Random Forest', RandomForestClassifier()))\n",
"models.append(('XGBoost', XGBClassifier()))"
],
"id": "hjR86I97eZ_2",
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "2JM9D9lKc_kk",
"outputId": "f873c73a-c706-4626-afe3-53faca01f724"
},
"source": [
"path = \"/content/gdrive/Shareddrives/Datasets/ML_Accuracy_Blog_Datasets/\"\n",
"dataset_cnt = len(datasets)\n",
"train_results = []\n",
"synth_results = []\n",
"\n",
"for i in range(dataset_cnt):\n",
" \n",
" #Read in the next dataset\n",
" dataset = datasets[i]\n",
" target = targets[i]\n",
" trainfile = path + dataset + \"/train.csv\"\n",
" train_df = pd.read_csv(trainfile)\n",
" synthfile = path + dataset + \"/synth.csv\"\n",
" synth_df = pd.read_csv(synthfile)\n",
" \n",
" # Sample training set to equal synthetic set or vice versa, ensuring the ratio of positive\n",
" # and negative samples is the same in eaach\n",
" train_cnt = len(train_df.index)\n",
" synth_cnt = len(synth_df.index)\n",
" train_df_use = train_df\n",
" synth_df_use = synth_df\n",
" if train_cnt > synth_cnt: \n",
" synth_targets = synth_df[target].value_counts()\n",
" train0_sample = train_df[train_df[target] == 0].sample(n=synth_targets[0], random_state=1)\n",
" train1_sample = train_df[train_df[target] == 1].sample(n=synth_targets[1], random_state=1)\n",
" train_df_use = pd.concat([train0_sample, train1_sample])\n",
" elif train_cnt < synth_cnt: \n",
" train_targets = train_df[target].value_counts()\n",
" synth0_sample = synth_df[synth_df[target] == 0].sample(n=train_targets[0], random_state=1)\n",
" synth1_sample = synth_df[synth_df[target] == 1].sample(n=train_targets[1], random_state=1)\n",
" synth_df_use = pd.concat([synth0_sample, synth1_sample])\n",
"\n",
" train_accuracies = {}\n",
" synth_accuracies = {}\n",
" \n",
" # First run the training set on a variety of models\n",
" X = train_df_use.drop([target], axis=1)\n",
" y = train_df_use[target].ravel()\n",
" for name, model in models:\n",
" train_accuracies[name] = score_model(model, X, y)\n",
" \n",
" # Now run the synthetic set on the same models\n",
" X = synth_df_use.drop([target], axis=1)\n",
" y = synth_df_use[target].ravel()\n",
" for name, model in models:\n",
" synth_accuracies[name] = score_model(model, X, y)\n",
" \n",
" #Graph the model type results\n",
" train_avg_acc, synth_avg_acc = compare_model_accur(dataset, train_accuracies, synth_accuracies)\n",
" train_results.append(train_avg_acc)\n",
" synth_results.append(synth_avg_acc)"
],
"id": "2JM9D9lKc_kk",
"execution_count": 39,
"outputs": [
{
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}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ifEYmBhTQkBn"
},
"source": [
"## Create an overall dataset summary graph"
],
"id": "ifEYmBhTQkBn"
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
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"outputId": "7de6576a-9f2e-4d7a-9427-35719c8c1399"
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"source": [
"\n",
"labels = datasets \n",
"train_colors = [\"purple\"] * dataset_cnt\n",
"synth_colors = [\"aqua\"] * dataset_cnt\n",
"fig = go.Figure(\n",
" data=[\n",
" go.Bar(\n",
" name=\"Original\",\n",
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" text=train_results,\n",
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" marker_color=train_colors\n",
" ),\n",
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" x=datasets,\n",
" y=synth_results,\n",
" text=synth_results,\n",
" textposition='auto',\n",
" marker_color=synth_colors\n",
" )\n",
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" layout=go.Layout(\n",
" title=\"ML Accuracy Using Original vs Synthetic Data\",\n",
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" xaxis_title=\"Dataset\",\n",
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" width=2000,\n",
" height=700,\n",
" )\n",
")\n",
"fig.show() "
],
"id": "D2FcMDr02tHJ",
"execution_count": 34,
"outputs": [
{
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"## Conclusion\n",
"\n",
"These datasets were chosen with no attempt to highlight only the successes. At Gretel, we’re really proud of how well our synthetic data generation holds onto the statistical integrity of the original data. While tougher datasets might exist, there are also many ways to tune a synthetic model that were unused in this set of experiments.\n",
"\n",
"Synthetic data plays an important role in the future of Artificial Intelligence. Beyond the hurdle of swift access to sensitive data, companies often lack enough relevant data to effectively train a model. To remedy this, synthetic data can be used to augment the original training data. This is particularly true (as described in our earlier [blog](https://gretel.ai/blog/improving-massively-imbalanced-datasets-in-machine-learning-with-synthetic-data)) when instances of the positive class are rare (such as in fraud or cybersecurity). Synthetic data can also be used to broaden the variety of examples used in pre-production testing scenarios.\n"
],
"id": "-Z4dyg0asrmb"
}
]
}
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