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ChernovAndreyChernovAndrey
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fixed lint
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‎examples/limix_classification_example.ipynb‎

Lines changed: 80 additions & 77 deletions
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@@ -24,12 +24,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-17T19:13:35.353551Z",
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"start_time": "2025-12-17T19:13:35.349651Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
@@ -38,9 +40,7 @@
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"from faim_sdk import LimiXPredictRequest, TabularClient"
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],
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"outputs": [],
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"execution_count": 30
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]
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},
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{
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"cell_type": "markdown",
@@ -53,12 +53,25 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-17T19:13:35.382612Z",
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"start_time": "2025-12-17T19:13:35.367160Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training set size: (426, 30)\n",
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"Test set size: (143, 30)\n",
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"Number of features: 30\n",
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"Classes: [0. 1.]\n"
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]
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}
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],
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"source": [
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"# Load breast cancer dataset\n",
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"X, y = load_breast_cancer(return_X_y=True)\n",
@@ -76,20 +89,7 @@
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"print(f\"Test set size: {X_test.shape}\")\n",
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"print(f\"Number of features: {X_train.shape[1]}\")\n",
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"print(f\"Classes: {np.unique(y_train)}\")"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training set size: (426, 30)\n",
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"Test set size: (143, 30)\n",
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"Number of features: 30\n",
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"Classes: [0. 1.]\n"
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]
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}
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],
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"execution_count": 31
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]
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},
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{
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"cell_type": "markdown",
@@ -102,22 +102,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-17T19:13:35.415414Z",
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"start_time": "2025-12-17T19:13:35.410396Z"
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}
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},
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"source": [
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"# Initialize the client\n",
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"client = TabularClient(\n",
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" base_url=\"https://api.faim.it.com\",\n",
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" api_key=os.environ.get(\"FAIM_API_KEY\"), # Replace with your actual API key\n",
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" timeout=120.0,\n",
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")\n",
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"\n",
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"print(\"TabularClient initialized!\")"
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],
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"outputs": [
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{
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"name": "stdout",
@@ -127,7 +118,16 @@
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]
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}
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],
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"execution_count": 32
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"source": [
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"# Initialize the client\n",
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"client = TabularClient(\n",
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" base_url=\"https://api.faim.it.com\",\n",
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" api_key=os.environ.get(\"FAIM_API_KEY\"), # Replace with your actual API key\n",
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" timeout=120.0,\n",
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")\n",
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"\n",
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"print(\"TabularClient initialized!\")"
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]
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},
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{
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"cell_type": "markdown",
@@ -140,23 +140,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-17T19:13:35.443478Z",
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"start_time": "2025-12-17T19:13:35.441032Z"
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}
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},
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"source": [
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"# Create a LimiX classification request\n",
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"request = LimiXPredictRequest(\n",
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" X_train=X_train, y_train=y_train, X_test=X_test, task_type=\"Classification\",\n",
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")\n",
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"\n",
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"print(\"Request prepared:\")\n",
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"print(f\" X_train shape: {request.X_train.shape}\")\n",
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"print(f\" X_test shape: {request.X_test.shape}\")\n",
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"print(f\" Task type: {request.task_type}\")"
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],
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"outputs": [
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{
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"name": "stdout",
@@ -169,7 +159,20 @@
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]
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}
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],
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"execution_count": 33
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"source": [
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"# Create a LimiX classification request\n",
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"request = LimiXPredictRequest(\n",
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" X_train=X_train,\n",
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" y_train=y_train,\n",
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" X_test=X_test,\n",
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" task_type=\"Classification\",\n",
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")\n",
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"\n",
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"print(\"Request prepared:\")\n",
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"print(f\" X_train shape: {request.X_train.shape}\")\n",
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"print(f\" X_test shape: {request.X_test.shape}\")\n",
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"print(f\" Task type: {request.task_type}\")"
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]
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},
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{
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"cell_type": "markdown",
@@ -182,33 +185,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-17T19:13:36.431619Z",
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"start_time": "2025-12-17T19:13:35.466839Z"
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}
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},
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"source": [
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"try:\n",
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" # Make predictions\n",
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" response = client.predict(request)\n",
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"\n",
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" print(f\"Predictions shape: {response.predictions.shape}\")\n",
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" print(f\"First 10 predictions: {response.predictions[:10]}\")\n",
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"\n",
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" if response.probabilities is not None:\n",
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" print(f\"\\nClass probabilities shape: {response.probabilities.shape}\")\n",
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" print(f\"First 3 samples probabilities:\\n{response.probabilities[:3]}\")\n",
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"\n",
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" print(\"\\nMetadata:\")\n",
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" for key, value in response.metadata.items():\n",
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" if key == 'cost_amount':\n",
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" value = float(value)/1e6\n",
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" print(f\" {key}: {value}\")\n",
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"\n",
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"except Exception as e:\n",
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" print(f\"Error: {e}\")"
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],
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"outputs": [
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{
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"name": "stdout",
@@ -233,7 +216,27 @@
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]
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}
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],
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"execution_count": 34
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"source": [
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"try:\n",
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" # Make predictions\n",
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" response = client.predict(request)\n",
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"\n",
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" print(f\"Predictions shape: {response.predictions.shape}\")\n",
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" print(f\"First 10 predictions: {response.predictions[:10]}\")\n",
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"\n",
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" if response.probabilities is not None:\n",
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" print(f\"\\nClass probabilities shape: {response.probabilities.shape}\")\n",
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" print(f\"First 3 samples probabilities:\\n{response.probabilities[:3]}\")\n",
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"\n",
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" print(\"\\nMetadata:\")\n",
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" for key, value in response.metadata.items():\n",
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" if key == \"cost_amount\":\n",
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" value = float(value) / 1e6\n",
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" print(f\" {key}: {value}\")\n",
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"\n",
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"except Exception as e:\n",
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" print(f\"Error: {e}\")"
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]
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},
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{
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"cell_type": "markdown",
@@ -246,12 +249,26 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-17T19:13:36.461088Z",
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"start_time": "2025-12-17T19:13:36.450764Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Classification Metrics:\n",
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" Accuracy: 0.9860\n",
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" Precision: 0.9888\n",
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" Recall: 0.9888\n",
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" F1-Score: 0.9888\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
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"\n",
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" print(f\" F1-Score: {f1:.4f}\")\n",
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"except NameError:\n",
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" print(\"Run prediction cell first to evaluate metrics.\")"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Classification Metrics:\n",
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" Accuracy: 0.9860\n",
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" Precision: 0.9888\n",
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" Recall: 0.9888\n",
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" F1-Score: 0.9888\n"
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]
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}
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],
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"execution_count": 35
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]
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}
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],
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"metadata": {

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