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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "5ROgtvTfTdy9"
},
"source": [
"## FlexTab Examples\n",
"Model page: https://huggingface.co/SAP/FlexTab"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "N-54dB6iTdy_"
},
"source": [
"The model you are trying to use is gated. Please make sure you have access to it by visiting the model page.To run inference, either set HF_TOKEN in your environment variables/ Secrets or run the following cell to login. 🤗"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "o7iIwHVHTdzA"
},
"outputs": [],
"source": [
"from huggingface_hub import login\n",
"login(new_session=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uninstall conflicting packages from the default Colab environment\n",
"!pip uninstall torch torchvision torchaudio -y`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6GqTz9gYTdzB"
},
"outputs": [],
"source": [
"# Install FlexTab core code\n",
"!pip install git+https://github.com/SAP-samples/flextab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jzA1dexsTdzB"
},
"outputs": [],
"source": [
"from sklearn.datasets import load_breast_cancer\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from flextab import FlexTabClassifier\n",
"\n",
"# Load sample data\n",
"X, y = load_breast_cancer(return_X_y=True)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n",
"\n",
"# Initialize a classifier, 8k context and 8-fold bagging gives best performance, reduce if running out of memory\n",
"clf = FlexTabClassifier(max_context_size=8192, bagging=8)\n",
"\n",
"clf.fit(X_train, y_train)\n",
"\n",
"# Predict probabilities\n",
"prediction_probabilities = clf.predict_proba(X_test)\n",
"# Predict labels\n",
"predictions = clf.predict(X_test)\n",
"print(\"Accuracy\", accuracy_score(y_test, predictions))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZlG2jXfWTdzC"
},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_openml\n",
"from sklearn.metrics import r2_score\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from flextab import FlexTabRegressor\n",
"\n",
"# Load sample data\n",
"df = fetch_openml(data_id=531, as_frame=True)\n",
"X = df.data\n",
"y = df.target.astype(float)\n",
"\n",
"# Train-test split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n",
"\n",
"# Initialize the regressor, 8k context and 8-fold bagging gives best performance, reduce if running out of memory\n",
"regressor = FlexTabRegressor(max_context_size=8192, bagging=8)\n",
"\n",
"regressor.fit(X_train, y_train)\n",
"\n",
"# Predict on the test set\n",
"predictions = regressor.predict(X_test)\n",
"\n",
"r2 = r2_score(y_test, predictions)\n",
"print(\"R² Score:\", r2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score, roc_auc_score\n",
"\n",
"from flextab import FlexTabMatcher\n",
"from flextab.utils.test_utils import load_febrl4\n",
"\n",
"left_train, left_test, right_train, right_test, y_train, y_test = load_febrl4()\n",
"\n",
"matcher = FlexTabMatcher(max_context_size=8192, bagging=1)\n",
"matcher.fit(left_train, right_train, y_train)\n",
"predictions = matcher.predict(left_test, right_test)\n",
"print(f'Accuracy {accuracy_score(y_test, predictions):.2%}')\n",
"# Probabilities (e.g. for thresholding or AUROC):\n",
"# probas = matcher.predict_proba_matching(left_test, right_test)\n",
"# print(f'AUROC {roc_auc_score(y_test, probas[:, 1]):.2%}')"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"machine_shape": "hm",
"provenance": []
},
"kaggle": {
"accelerator": "gpu"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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