1 lines (1 with data), 201.9 kB

{"cells":[{"cell_type":"code","source":["from pyspark.sql import SparkSession\n","spark = SparkSession.builder.appName(\"GenomicAnalysis\").getOrCreate()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":3,"statement_ids":[3],"state":"finished","livy_statement_state":"available","session_id":"5cbeacd0-2fd7-49f7-9362-62346897f60f","normalized_state":"finished","queued_time":"2025-04-01T02:57:54.8877332Z","session_start_time":"2025-04-01T02:57:54.8887801Z","execution_start_time":"2025-04-01T02:58:06.0713055Z","execution_finish_time":"2025-04-01T02:58:06.427771Z","parent_msg_id":"277fe743-8881-490e-853a-5e1dd9edf4e1"},"text/plain":"StatementMeta(, 5cbeacd0-2fd7-49f7-9362-62346897f60f, 3, Finished, Available, Finished)"},"metadata":{}}],"execution_count":1,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"430bac6f-898d-470b-bc84-5ebfc18b3196"},{"cell_type":"code","source":["# Load genomic data from Lakehouse\n","df = spark.read.csv(\n","    \"Files/PDC_biospecimen_manifest_03272025_214257.csv\",\n","    header=True,\n","    inferSchema=True\n",")\n","\n","# Display schema and sample data\n","df.printSchema()\n","display(df.limit(5))"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":4,"statement_ids":[4],"state":"finished","livy_statement_state":"available","session_id":"5cbeacd0-2fd7-49f7-9362-62346897f60f","normalized_state":"finished","queued_time":"2025-04-01T02:58:23.4130581Z","session_start_time":null,"execution_start_time":"2025-04-01T02:58:23.4142215Z","execution_finish_time":"2025-04-01T02:58:28.1814166Z","parent_msg_id":"bc4a0c74-137a-468a-8221-38375cccbf96"},"text/plain":"StatementMeta(, 5cbeacd0-2fd7-49f7-9362-62346897f60f, 4, Finished, Available, Finished)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["root\n |-- Aliquot ID: string (nullable = true)\n |-- Aliquot Submitter ID: string (nullable = true)\n |-- Sample ID: string (nullable = true)\n |-- Sample Submitter ID: string (nullable = true)\n |-- Case ID: string (nullable = true)\n |-- Case Submitter ID: string (nullable = true)\n |-- Project Name: string (nullable = true)\n |-- Sample Type: string (nullable = true)\n |-- Primary Site: string (nullable = true)\n |-- Disease Type: string (nullable = true)\n |-- Aliquot Is Ref: string (nullable = true)\n |-- Aliquot Status: string (nullable = true)\n |-- Aliquot Quantity: string (nullable = true)\n |-- Aliquot Volume: string (nullable = true)\n |-- Amount: string (nullable = true)\n |-- Analyte Type: string (nullable = true)\n |-- Concentration: string (nullable = true)\n |-- Case Status: string (nullable = true)\n |-- Sample Status: string (nullable = true)\n |-- Sample Is Ref: string (nullable = true)\n |-- Biospecimen Anatomic Site: string (nullable = true)\n |-- Biospecimen Laterality: string (nullable = true)\n |-- Composition: string (nullable = true)\n |-- Current Weight: string (nullable = true)\n |-- Days To Collection: string (nullable = true)\n |-- Days To Sample Procurement: string (nullable = true)\n |-- Diagnosis Pathologically Confirmed: string (nullable = true)\n |-- Freezing Method: string (nullable = true)\n |-- Initial Weight: string (nullable = true)\n |-- Intermediate Dimension: string (nullable = true)\n |-- Longest Dimension: string (nullable = true)\n |-- Method Of Sample Procurement: string (nullable = true)\n |-- Pathology Report UUID: string (nullable = true)\n |-- Preservation Method: string (nullable = true)\n |-- Sample Type id: string (nullable = true)\n |-- Sample Ordinal: string (nullable = true)\n |-- Shortest Dimension: string (nullable = true)\n |-- Time Between Clamping And Freezing: string (nullable = true)\n |-- Time Between Excision and Freezing: string (nullable = true)\n |-- Tissue Collection Type: string (nullable = true)\n |-- Tissue Type: string (nullable = true)\n |-- Tumor Code: string (nullable = true)\n |-- Tumor Code ID: string (nullable = true)\n |-- Tumor Descriptor: string (nullable = true)\n |-- Program Name: string (nullable = true)\n\n"]},{"output_type":"display_data","data":{"application/vnd.synapse.widget-view+json":{"widget_id":"6a2743cb-8144-4799-a949-027814db2968","widget_type":"Synapse.DataFrame"},"text/plain":"SynapseWidget(Synapse.DataFrame, 6a2743cb-8144-4799-a949-027814db2968)"},"metadata":{}}],"execution_count":2,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"},"collapsed":false},"id":"1899f566-38ab-4740-bbae-1dbfb39778ff"},{"cell_type":"code","source":["from pyspark.sql.functions import col, count, when\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","## 1. Initialize Spark Session (if not already done)\n","from pyspark.sql import SparkSession\n","spark = SparkSession.builder.appName(\"BiospecimenAnalysis\").getOrCreate()\n","\n","## 2. Load Data (assuming already loaded as 'df')\n","# df = spark.read.csv(\"your_file_path.csv\", header=True)\n","\n","## 3. Verify Data\n","print(\"Available columns:\")\n","print(df.columns)\n","df.printSchema()\n","\n","## 4. Sample Type Analysis\n","def analyze_sample_types(df):\n","    try:\n","        # Count by sample type\n","        sample_counts = df.groupBy(\"Sample Type\").agg(\n","            count(\"*\").alias(\"Count\")\n","        ).orderBy(\"Count\", ascending=False)\n","        \n","        # Convert to pandas for visualization\n","        pdf = sample_counts.toPandas()\n","        \n","        # Plot\n","        plt.figure(figsize=(10, 6))\n","        sns.barplot(x=\"Sample Type\", y=\"Count\", data=pdf)\n","        plt.title(\"Distribution of Sample Types\")\n","        plt.xticks(rotation=45)\n","        plt.tight_layout()\n","        plt.show()\n","        \n","        return sample_counts\n","    except Exception as e:\n","        print(f\"Error in sample type analysis: {str(e)}\")\n","        return None\n","\n","## 5. Disease Type Analysis\n","def analyze_disease_types(df):\n","    try:\n","        # Count by disease type\n","        disease_counts = df.groupBy(\"Disease Type\").agg(\n","            count(\"*\").alias(\"Count\")\n","        ).orderBy(\"Count\", ascending=False)\n","        \n","        # Plot top 10\n","        top_diseases = disease_counts.limit(10).toPandas()\n","        \n","        plt.figure(figsize=(12, 6))\n","        sns.barplot(x=\"Disease Type\", y=\"Count\", data=top_diseases)\n","        plt.title(\"Top 10 Disease Types\")\n","        plt.xticks(rotation=45)\n","        plt.tight_layout()\n","        plt.show()\n","        \n","        return disease_counts\n","    except Exception as e:\n","        print(f\"Error in disease type analysis: {str(e)}\")\n","        return None\n","\n","## 6. Sample Status Analysis\n","def analyze_sample_status(df):\n","    try:\n","        # Status distribution\n","        status_counts = df.groupBy(\"Sample Status\").agg(\n","            count(\"*\").alias(\"Count\")\n","        ).orderBy(\"Count\", ascending=False)\n","        \n","        # Plot\n","        pdf = status_counts.toPandas()\n","        \n","        plt.figure(figsize=(8, 6))\n","        plt.pie(pdf[\"Count\"], labels=pdf[\"Sample Status\"], autopct='%1.1f%%')\n","        plt.title(\"Sample Status Distribution\")\n","        plt.show()\n","        \n","        return status_counts\n","    except Exception as e:\n","        print(f\"Error in sample status analysis: {str(e)}\")\n","        return None\n","\n","## 7. Primary Site Analysis\n","def analyze_primary_sites(df):\n","    try:\n","        # Count by primary site\n","        site_counts = df.groupBy(\"Primary Site\").agg(\n","            count(\"*\").alias(\"Count\")\n","        ).orderBy(\"Count\", ascending=False)\n","        \n","        # Plot top 15\n","        top_sites = site_counts.limit(15).toPandas()\n","        \n","        plt.figure(figsize=(12, 6))\n","        sns.barplot(x=\"Primary Site\", y=\"Count\", data=top_sites)\n","        plt.title(\"Top 15 Primary Sites\")\n","        plt.xticks(rotation=60)\n","        plt.tight_layout()\n","        plt.show()\n","        \n","        return site_counts\n","    except Exception as e:\n","        print(f\"Error in primary site analysis: {str(e)}\")\n","        return None\n","\n","## 8. Main Analysis Pipeline\n","def run_analysis_pipeline(df):\n","    print(\"\\n=== Starting Biospecimen Analysis ===\")\n","    \n","    print(\"\\n1. Sample Type Distribution:\")\n","    sample_results = analyze_sample_types(df)\n","    \n","    print(\"\\n2. Disease Type Distribution:\")\n","    disease_results = analyze_disease_types(df)\n","    \n","    print(\"\\n3. Sample Status Distribution:\")\n","    status_results = analyze_sample_status(df)\n","    \n","    print(\"\\n4. Primary Site Distribution:\")\n","    site_results = analyze_primary_sites(df)\n","    \n","    print(\"\\n=== Analysis Complete ===\")\n","    \n","    return {\n","        \"sample_types\": sample_results,\n","        \"disease_types\": disease_results,\n","        \"sample_status\": status_results,\n","        \"primary_sites\": site_results\n","    }\n","\n","## 9. Execute Analysis\n","analysis_results = run_analysis_pipeline(df)\n","\n","## 10. Save Results (optional)\n","# analysis_results[\"sample_types\"].write.mode(\"overwrite\").parquet(\"sample_type_counts.parquet\")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":9,"statement_ids":[9],"state":"finished","livy_statement_state":"available","session_id":"5cbeacd0-2fd7-49f7-9362-62346897f60f","normalized_state":"finished","queued_time":"2025-04-01T03:06:19.3503308Z","session_start_time":null,"execution_start_time":"2025-04-01T03:06:19.3517929Z","execution_finish_time":"2025-04-01T03:06:29.2897718Z","parent_msg_id":"8e883519-15b0-4898-a423-e3715448d21e"},"text/plain":"StatementMeta(, 5cbeacd0-2fd7-49f7-9362-62346897f60f, 9, Finished, Available, Finished)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Available columns:\n['Aliquot ID', 'Aliquot Submitter ID', 'Sample ID', 'Sample Submitter ID', 'Case ID', 'Case Submitter ID', 'Project Name', 'Sample Type', 'Primary Site', 'Disease Type', 'Aliquot Is Ref', 'Aliquot Status', 'Aliquot Quantity', 'Aliquot Volume', 'Amount', 'Analyte Type', 'Concentration', 'Case Status', 'Sample Status', 'Sample Is Ref', 'Biospecimen Anatomic Site', 'Biospecimen Laterality', 'Composition', 'Current Weight', 'Days To Collection', 'Days To Sample Procurement', 'Diagnosis Pathologically Confirmed', 'Freezing Method', 'Initial Weight', 'Intermediate Dimension', 'Longest Dimension', 'Method Of Sample Procurement', 'Pathology Report UUID', 'Preservation Method', 'Sample Type id', 'Sample Ordinal', 'Shortest Dimension', 'Time Between Clamping And Freezing', 'Time Between Excision and Freezing', 'Tissue Collection Type', 'Tissue Type', 'Tumor Code', 'Tumor Code ID', 'Tumor Descriptor', 'Program Name']\nroot\n |-- Aliquot ID: string (nullable = true)\n |-- Aliquot Submitter ID: string (nullable = true)\n |-- Sample ID: string (nullable = true)\n |-- Sample Submitter ID: string (nullable = true)\n |-- Case ID: string (nullable = true)\n |-- Case Submitter ID: string (nullable = true)\n |-- Project Name: string (nullable = true)\n |-- Sample Type: string (nullable = true)\n |-- Primary Site: string (nullable = true)\n |-- Disease Type: string (nullable = true)\n |-- Aliquot Is Ref: string (nullable = true)\n |-- Aliquot Status: string (nullable = true)\n |-- Aliquot Quantity: string (nullable = true)\n |-- Aliquot Volume: string (nullable = true)\n |-- Amount: string (nullable = true)\n |-- Analyte Type: string (nullable = true)\n |-- Concentration: string (nullable = true)\n |-- Case Status: string (nullable = true)\n |-- Sample Status: string (nullable = true)\n |-- Sample Is Ref: string (nullable = true)\n |-- Biospecimen Anatomic Site: string (nullable = true)\n |-- Biospecimen Laterality: string (nullable = true)\n |-- Composition: string (nullable = true)\n |-- Current Weight: string (nullable = true)\n |-- Days To Collection: string (nullable = true)\n |-- Days To Sample Procurement: string (nullable = true)\n |-- Diagnosis Pathologically Confirmed: string (nullable = true)\n |-- Freezing Method: string (nullable = true)\n |-- Initial Weight: string (nullable = true)\n |-- Intermediate Dimension: string (nullable = true)\n |-- Longest Dimension: string (nullable = true)\n |-- Method Of Sample Procurement: string (nullable = true)\n |-- Pathology Report UUID: string (nullable = true)\n |-- Preservation Method: string (nullable = true)\n |-- Sample Type id: string (nullable = true)\n |-- Sample Ordinal: string (nullable = true)\n |-- Shortest Dimension: string (nullable = true)\n |-- Time Between Clamping And Freezing: string (nullable = true)\n |-- Time Between Excision and Freezing: string (nullable = true)\n |-- Tissue Collection Type: string (nullable = true)\n |-- Tissue Type: string (nullable = true)\n |-- Tumor Code: string (nullable = true)\n |-- Tumor Code ID: string (nullable = true)\n |-- Tumor Descriptor: string (nullable = true)\n |-- Program Name: string (nullable = true)\n\n\n=== Starting Biospecimen Analysis ===\n\n1. Sample Type Distribution:\n"]},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 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"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n2. Disease Type Distribution:\n"]},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x600 with 1 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"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n3. Sample Status Distribution:\n"]},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 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"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n4. Primary Site Distribution:\n"]},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x600 with 1 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"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n=== Analysis Complete ===\n"]}],"execution_count":7,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"e2a71cc0-b1ca-4f3c-a97c-b30e1b42f325"},{"cell_type":"code","source":["# Import libraries\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix\n","from sklearn.preprocessing import LabelEncoder, StandardScaler\n","from sklearn.impute import SimpleImputer\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import pandas as pd\n","import numpy as np\n","\n","## --------------------------------------------\n","## 1. DATA LOADING\n","## --------------------------------------------\n","\n","def load_data_fabric():\n","    \"\"\"Load data using the path that worked for you\"\"\"\n","    try:\n","        file_path = \"/lakehouse/default/Files/PDC_biospecimen_manifest_03272025_214257.csv\"\n","        df = pd.read_csv(file_path)\n","        print(f\"Successfully loaded file from: {file_path}\")\n","        \n","        # Show basic info about the data\n","        print(f\"\\nData shape: {df.shape}\")\n","        print(\"\\nMissing values per column:\")\n","        print(df.isnull().sum())\n","        \n","        return df\n","    except Exception as e:\n","        print(f\"Failed to load data: {str(e)}\")\n","        raise\n","\n","## --------------------------------------------\n","## 2. DATA PREPARATION WITH ROBUST NaN HANDLING\n","## --------------------------------------------\n","\n","def prepare_data(df):\n","    \"\"\"Prepare data with comprehensive NaN handling\"\"\"\n","    try:\n","        # SELECT FEATURES - using columns that exist in your data\n","        feature_cols = [\n","            'Aliquot Quantity', \n","            'Aliquot Volume',\n","            'Concentration',\n","            'Days To Collection',\n","            'Days To Sample Procurement',\n","            'Current Weight',\n","            'Initial Weight'\n","        ]\n","        \n","        # Filter to only columns that actually exist\n","        feature_cols = [col for col in feature_cols if col in df.columns]\n","        \n","        if not feature_cols:\n","            raise ValueError(\"No feature columns found\")\n","        \n","        # SELECT TARGET - using 'Case Status' which exists in your data\n","        target_col = 'Case Status'\n","        if target_col not in df.columns:\n","            raise ValueError(f\"Target column '{target_col}' not found\")\n","        \n","        print(f\"\\nUsing features: {feature_cols}\")\n","        print(f\"Using target: {target_col}\")\n","        \n","        # Handle missing values in features\n","        print(\"\\nMissing values in selected features before imputation:\")\n","        print(df[feature_cols].isnull().sum())\n","        \n","        # Create imputer for numeric features\n","        numeric_imputer = SimpleImputer(strategy='median')\n","        X = numeric_imputer.fit_transform(df[feature_cols])\n","        \n","        # Encode categorical target\n","        le = LabelEncoder()\n","        y = le.fit_transform(df[target_col].astype(str))  # Ensure string type\n","        \n","        # Scale features\n","        scaler = StandardScaler()\n","        X = scaler.fit_transform(X)\n","        \n","        # Verify no NaN values remain\n","        if np.isnan(X).any():\n","            raise ValueError(\"NaN values still present after imputation\")\n","        \n","        return X, y, le.classes_\n","        \n","    except Exception as e:\n","        print(f\"\\nData preparation failed: {str(e)}\")\n","        raise\n","\n","## --------------------------------------------\n","## 3. MODEL TRAINING AND EVALUATION\n","## --------------------------------------------\n","\n","def train_and_evaluate(X, y, class_names):\n","    \"\"\"Train model with comprehensive evaluation\"\"\"\n","    try:\n","        # Split data\n","        X_train, X_test, y_train, y_test = train_test_split(\n","            X, y, test_size=0.2, random_state=42)\n","        \n","        # Train model\n","        model = RandomForestClassifier(\n","            n_estimators=100,\n","            random_state=42,\n","            n_jobs=-1,\n","            class_weight='balanced'\n","        )\n","        model.fit(X_train, y_train)\n","        \n","        # Evaluate\n","        y_pred = model.predict(X_test)\n","        y_proba = model.predict_proba(X_test)\n","        \n","        # Metrics\n","        print(f\"\\nModel Performance:\")\n","        print(f\"Accuracy: {accuracy_score(y_test, y_pred):.4f}\")\n","        try:\n","            print(f\"AUC-ROC: {roc_auc_score(y_test, y_proba, multi_class='ovr'):.4f}\")\n","        except:\n","            print(\"AUC-ROC: Could not calculate (possibly only one class present)\")\n","        \n","        # Confusion Matrix\n","        cm = confusion_matrix(y_test, y_pred)\n","        plt.figure(figsize=(8, 6))\n","        sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n","                    xticklabels=class_names,\n","                    yticklabels=class_names)\n","        plt.xlabel(\"Predicted\")\n","        plt.ylabel(\"Actual\")\n","        plt.title(\"Confusion Matrix\")\n","        plt.show()\n","        \n","        return model\n","        \n","    except Exception as e:\n","        print(f\"\\nModel training failed: {str(e)}\")\n","        raise\n","\n","## --------------------------------------------\n","## MAIN EXECUTION\n","## --------------------------------------------\n","\n","print(\"Starting analysis pipeline...\")\n","\n","try:\n","    # STEP 1: Load data\n","    print(\"\\n1. Loading data...\")\n","    df = load_data_fabric()\n","    \n","    # STEP 2: Prepare data\n","    print(\"\\n2. Preparing data...\")\n","    X, y, classes = prepare_data(df)\n","    \n","    # STEP 3: Train and evaluate\n","    print(\"\\n3. Training model...\")\n","    model = train_and_evaluate(X, y, classes)\n","    \n","    print(\"\\nPipeline completed successfully!\")\n","    \n","except Exception as e:\n","    print(f\"\\nPipeline failed: {str(e)}\")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":26,"statement_ids":[26],"state":"finished","livy_statement_state":"available","session_id":"5cbeacd0-2fd7-49f7-9362-62346897f60f","normalized_state":"finished","queued_time":"2025-04-01T03:48:37.4145235Z","session_start_time":null,"execution_start_time":"2025-04-01T03:48:37.4159023Z","execution_finish_time":"2025-04-01T03:48:53.5828774Z","parent_msg_id":"d13a713e-3ceb-4acd-ba96-e036b0280c04"},"text/plain":"StatementMeta(, 5cbeacd0-2fd7-49f7-9362-62346897f60f, 26, Finished, Available, Finished)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Starting analysis pipeline...\n\n1. Loading data...\nSuccessfully loaded file from: /lakehouse/default/Files/PDC_biospecimen_manifest_03272025_214257.csv\n\nData shape: (452, 45)\n\nMissing values per column:\nAliquot ID                              0\nAliquot Submitter ID                    0\nSample ID                               0\nSample Submitter ID                     0\nCase ID                                 0\nCase Submitter ID                       0\nProject Name                            0\nSample Type                             0\nPrimary Site                            0\nDisease Type                            0\nAliquot Is Ref                          0\nAliquot Status                          0\nAliquot Quantity                      452\nAliquot Volume                        452\nAmount                                452\nAnalyte Type                           76\nConcentration                         452\nCase Status                             0\nSample Status                           0\nSample Is Ref                         158\nBiospecimen Anatomic Site             201\nBiospecimen Laterality                452\nComposition                             0\nCurrent Weight                        452\nDays To Collection                    347\nDays To Sample Procurement            452\nDiagnosis Pathologically Confirmed    201\nFreezing Method                       452\nInitial Weight                        347\nIntermediate Dimension                452\nLongest Dimension                     452\nMethod Of Sample Procurement           91\nPathology Report UUID                 452\nPreservation Method                   201\nSample Type id                        344\nSample Ordinal                        452\nShortest Dimension                    452\nTime Between Clamping And Freezing    452\nTime Between Excision and Freezing    452\nTissue Collection Type                452\nTissue Type                             0\nTumor Code                            452\nTumor Code ID                         452\nTumor Descriptor                      199\nProgram Name                            0\ndtype: int64\n\n2. Preparing data...\n\nUsing features: ['Aliquot Quantity', 'Aliquot Volume', 'Concentration', 'Days To Collection', 'Days To Sample Procurement', 'Current Weight', 'Initial Weight']\nUsing target: Case Status\n\nMissing values in selected features before imputation:\nAliquot Quantity              452\nAliquot Volume                452\nConcentration                 452\nDays To Collection            347\nDays To Sample Procurement    452\nCurrent Weight                452\nInitial Weight                347\ndtype: int64\n\n3. Training model...\n\nModel Performance:\nAccuracy: 1.0000\nAUC-ROC: Could not calculate (possibly only one class present)\n"]},{"output_type":"stream","name":"stderr","text":["/home/trusted-service-user/cluster-env/trident_env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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