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NickNtampNickNtamp
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Notebook fixes (#149)
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+34
-42
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2 files changed

+34
-42
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‎.env.dev‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -7,4 +7,4 @@ DATABASE_URL=postgresql://postgres:postgres@localhost:5432/postgres
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VERSION=0.1.0
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METRICS_CRON=*/15 * * * *
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MODEL_PATH=models
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MODEL_PATH=models

‎examples/notebooks/sdk-example.ipynb‎

Lines changed: 33 additions & 41 deletions
Original file line numberDiff line numberDiff line change
@@ -5,14 +5,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Usaging Whitebox SDK\n",
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"# Use of Whitebox SDK\n",
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"\n",
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"First of all we need to import the Whitebox class:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -29,11 +29,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"wb = Whitebox(host=\"http://127.0.0.1:8000\", api_key=\"1073a9a03e5c6bf06b3f7a0c23d44ff923842a63dc75929ff0543705bbd3fa26\")"
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"wb = Whitebox(host=\"http://127.0.0.1:8000\", api_key=\"69ca68d71f804cbb00ef6c741768769f2b7bdf6bc43aec1a55d0386709e51a8e\")"
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]
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},
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{
@@ -48,27 +48,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'id': '5dce48be-a16f-45dc-a878-d80b5b02a1f2',\n",
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" 'created_at': '2023-01-17T15:02:27.356199',\n",
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" 'updated_at': '2023-01-17T15:02:27.356199',\n",
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"{'id': 'dae9007d-894f-4f19-9772-7b7048a86347',\n",
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" 'created_at': '2023-02-24T14:11:36.400473',\n",
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" 'updated_at': '2023-02-24T14:11:36.400473',\n",
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" 'name': 'Model 1',\n",
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" 'description': '',\n",
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" 'type': 'binary',\n",
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" 'features': {'additionalProp1': 'numerical',\n",
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" 'additionalProp2': 'numerical',\n",
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" 'target': 'numerical'},\n",
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" 'labels': {'additionalProp1': 0, 'additionalProp2': 1},\n",
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" 'prediction': 'y_prediction_multi',\n",
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" 'probability': 'proba'}"
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" 'target_column': 'y_prediction_multi',\n",
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" 'labels': {'additionalProp1': 0, 'additionalProp2': 1}}"
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]
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},
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"execution_count": 3,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -87,33 +83,29 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'id': '5dce48be-a16f-45dc-a878-d80b5b02a1f2',\n",
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" 'created_at': '2023-01-17T15:02:27.356199',\n",
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" 'updated_at': '2023-01-17T15:02:27.356199',\n",
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"{'id': 'dae9007d-894f-4f19-9772-7b7048a86347',\n",
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" 'created_at': '2023-02-24T14:11:36.400473',\n",
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" 'updated_at': '2023-02-24T14:11:36.400473',\n",
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" 'name': 'Model 1',\n",
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" 'description': '',\n",
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" 'type': 'binary',\n",
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" 'features': {'additionalProp1': 'numerical',\n",
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" 'additionalProp2': 'numerical',\n",
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" 'target': 'numerical'},\n",
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" 'labels': {'additionalProp1': 0, 'additionalProp2': 1},\n",
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" 'prediction': 'y_prediction_multi',\n",
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" 'probability': 'proba'}"
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" 'target_column': 'y_prediction_multi',\n",
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" 'labels': {'additionalProp1': 0, 'additionalProp2': 1}}"
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]
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},
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"execution_count": 4,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"wb.get_model(\"5dce48be-a16f-45dc-a878-d80b5b02a1f2\")"
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"wb.get_model(\"dae9007d-894f-4f19-9772-7b7048a86347\")"
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]
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},
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{
@@ -130,7 +122,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
@@ -139,16 +131,16 @@
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"True"
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]
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},
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"execution_count": 5,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"df = pd.read_csv(\"src/analytics/data/testing/classification_test_data.csv\")\n",
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"df = pd.read_csv(\"whitebox/analytics/data/testing/classification_test_data.csv\")\n",
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"\n",
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"wb.log_training_dataset(model_id=\"5dce48be-a16f-45dc-a878-d80b5b02a1f2\", processed=df, non_processed=df)"
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"wb.log_training_dataset(model_id=\"dae9007d-894f-4f19-9772-7b7048a86347\", processed=df, non_processed=df)"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"True"
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]
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},
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"execution_count": 6,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"from whitebox.tests.v1.mock_data import timestamps, mixed_actuals\n",
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"df = pd.read_csv(\"src/analytics/data/testing/classification_test_data.csv\")\n",
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"df = pd.read_csv(\"whitebox/analytics/data/testing/classification_test_data.csv\")\n",
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"\n",
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"\n",
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"wb.log_inferences(model_id=\"5dce48be-a16f-45dc-a878-d80b5b02a1f2\", processed=df, non_processed=df, timestamps=timestamps, actuals=mixed_actuals)"
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"wb.log_inferences(model_id=\"dae9007d-894f-4f19-9772-7b7048a86347\", processed=df, non_processed=df, timestamps=timestamps, actuals=mixed_actuals)"
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]
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},
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{
@@ -227,7 +219,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"True"
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]
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},
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"execution_count": 7,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"wb.delete_model(\"5dce48be-a16f-45dc-a878-d80b5b02a1f2\")"
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"wb.delete_model(\"dae9007d-894f-4f19-9772-7b7048a86347\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": "Python 3.10.8",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.6"
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"version": "3.10.8"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "6b8a8ae524dcca06b04542d2d49be160be0f11dd19d43d7b2673d555344c6092"
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"hash": "4de3d24b14351dd1d776fcd4034cd3e8d8527433466e9293dd76189fddd35128"
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}
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}
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},

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