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{
"cells": [
{
"cell_type": "code",
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"id": "5226926e",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Cervical_Cancer\"\n",
"\n",
"# Input paths\n",
"tcga_root_dir = \"../../input/TCGA\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Cervical_Cancer/TCGA.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Cervical_Cancer/gene_data/TCGA.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Cervical_Cancer/clinical_data/TCGA.csv\"\n",
"json_path = \"../../output/preprocess/Cervical_Cancer/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "4b73f5a7",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ac34519a",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found potential match: TCGA_Cervical_Cancer_(CESC)\n",
"Selected as best match: TCGA_Cervical_Cancer_(CESC)\n",
"Selected directory: TCGA_Cervical_Cancer_(CESC)\n",
"Clinical file: TCGA.CESC.sampleMap_CESC_clinicalMatrix\n",
"Genetic file: TCGA.CESC.sampleMap_HiSeqV2_PANCAN.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Clinical data columns:\n",
"['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'additional_pharmaceutical_therapy', 'additional_radiation_therapy', 'additional_treatment_completion_success_outcome', 'adjuvant_rad_therapy_prior_admin', 'age_at_initial_pathologic_diagnosis', 'age_began_smoking_in_years', 'agent_total_dose_count', 'assessment_timepoint_category', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'birth_control_pill_history_usage_category', 'brachytherapy_administered_status', 'brachytherapy_first_reference_point_administered_total_dose', 'brachytherapy_method_other_specify_text', 'brachytherapy_method_type', 'cervical_carcinoma_corpus_uteri_involvement_indicator', 'cervical_carcinoma_pelvic_extension_text', 'cervical_neoplasm_pathologic_margin_involved_text', 'cervical_neoplasm_pathologic_margin_involved_type', 'chemotherapy_negation_radiation_therapy_concurrent_adminstrd_txt', 'chemotherapy_negation_radiation_therapy_concurrnt_nt_dmnstrd_rsn', 'chemotherapy_regimen_type', 'clinical_stage', 'concurrent_chemotherapy_dose', 'days_to_birth', 'days_to_brachytherapy_begin_occurrence', 'days_to_brachytherapy_end_occurrence', 'days_to_chemotherapy_end', 'days_to_chemotherapy_start', 'days_to_collection', 'days_to_death', 'days_to_diagnostic_computed_tomography_performed', 'days_to_diagnostic_mri_performed', 'days_to_fdg_or_ct_pet_performed', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_additional_surgery_procedure', 'days_to_new_tumor_event_after_initial_treatment', 'days_to_performance_status_assessment', 'days_to_radiation_therapy_end', 'days_to_radiation_therapy_start', 'death_cause_text', 'diagnostic_ct_result_outcome', 'diagnostic_mri_result_outcome', 'dose_frequency_text', 'eastern_cancer_oncology_group', 'ectopic_pregnancy_count', 'external_beam_radiation_therapy_administered_status', 'external_beam_radiation_therapy_administrd_prrtc_rgn_lymph_nd_ds', 'fdg_or_ct_pet_performed_outcome', 'female_breast_feeding_or_pregnancy_status_indicator', 'followup_case_report_form_submission_reason', 'form_completion_date', 'gender', 'height', 'histological_type', 'history_of_neoadjuvant_treatment', 'human_papillomavirus_laboratory_procedure_performed_name', 'human_papillomavirus_laboratory_procedure_performed_text', 'human_papillomavirus_other_type_text', 'human_papillomavirus_type', 'hysterectomy_performed_text', 'hysterectomy_performed_type', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'init_pathology_dx_method_other', 'initial_pathologic_diagnosis_method', 'initial_weight', 'is_ffpe', 'keratinizing_squamous_cell_carcinoma_present_indicator', 'lost_follow_up', 'lymph_node_examined_count', 'lymph_node_location_positive_pathology_name', 'lymph_node_location_positive_pathology_text', 'lymphovascular_invasion_indicator', 'menopause_status', 'neoplasm_histologic_grade', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_post_initial_therapy_diagnosis_method_text', 'new_neoplasm_event_post_initial_therapy_diagnosis_method_type', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_additional_surgery_procedure', 'new_tumor_event_after_initial_treatment', 'number_of_lymphnodes_positive_by_he', 'number_of_lymphnodes_positive_by_ihc', 'number_of_successful_pregnancies_which_resultd_n_t_lst_1_lv_brth', 'number_pack_years_smoked', 'oct_embedded', 'oligonucleotide_primer_pair_laboratory_procedure_performed_name', 'other_chemotherapy_agent_administration_specify', 'other_dx', 'pathologic_M', 'pathologic_N', 'pathologic_T', 'pathology_report_file_name', 'patient_death_reason', 'patient_history_immune_system_and_related_disorders_name', 'patient_history_immune_system_and_related_disorders_text', 'patient_id', 'patient_pregnancy_spontaneous_abortion_count', 'patient_pregnancy_therapeutic_abortion_count', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'pregnancy_stillbirth_count', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'radiation_therapy', 'radiation_therapy_not_administered_reason', 'radiation_therapy_not_administered_specify', 'radiation_type_notes', 'residual_disease_post_new_tumor_event_margin_status', 'rt_administered_type', 'rt_pelvis_administered_total_dose', 'sample_type', 'sample_type_id', 'standardized_uptake_value_cervix_uteri_assessment_measurement', 'stopped_smoking_year', 'system_version', 'targeted_molecular_therapy', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tobacco_smoking_history', 'total_number_of_pregnancies', 'tumor_response_cdus_type', 'tumor_tissue_site', 'vial_number', 'vital_status', 'weight', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_CESC_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_CESC_miRNA_HiSeq', '_GENOMIC_ID_TCGA_CESC_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_data/public/TCGA/CESC/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_CESC_PDMRNAseq', '_GENOMIC_ID_TCGA_CESC_RPPA', '_GENOMIC_ID_TCGA_CESC_hMethyl450', '_GENOMIC_ID_TCGA_CESC_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_CESC_exp_HiSeqV2_percentile', '_GENOMIC_ID_TCGA_CESC_mutation', '_GENOMIC_ID_TCGA_CESC_mutation_broad_gene', '_GENOMIC_ID_TCGA_CESC_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_CESC_mutation_curated_wustl_gene', '_GENOMIC_ID_TCGA_CESC_exp_HiSeqV2', '_GENOMIC_ID_TCGA_CESC_gistic2', '_GENOMIC_ID_TCGA_CESC_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_CESC_gistic2thd']\n",
"\n",
"Clinical data shape: (313, 157)\n",
"Genetic data shape: (20530, 308)\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"# 1. Find the most relevant directory for Cervical Cancer\n",
"subdirectories = os.listdir(tcga_root_dir)\n",
"target_trait = trait.lower().replace(\"_\", \" \") # Convert to lowercase for case-insensitive matching\n",
"\n",
"# Search for related terms to Cervical Cancer\n",
"related_terms = [\"cervical\", \"cervix\", \"cesc\"]\n",
"matched_dir = None\n",
"\n",
"for subdir in subdirectories:\n",
" subdir_lower = subdir.lower()\n",
" # Check if any related term is in the directory name\n",
" if any(term in subdir_lower for term in related_terms):\n",
" matched_dir = subdir\n",
" print(f\"Found potential match: {subdir}\")\n",
" # If exact match found, select it\n",
" if \"cervical_cancer\" in subdir_lower.replace(\" \", \"_\"):\n",
" print(f\"Selected as best match: {subdir}\")\n",
" matched_dir = subdir\n",
" break\n",
"\n",
"# If we found a potential match, use it\n",
"if matched_dir:\n",
" print(f\"Selected directory: {matched_dir}\")\n",
" \n",
" # 2. Get the clinical and genetic data file paths\n",
" cohort_dir = os.path.join(tcga_root_dir, matched_dir)\n",
" clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
" \n",
" print(f\"Clinical file: {os.path.basename(clinical_file_path)}\")\n",
" print(f\"Genetic file: {os.path.basename(genetic_file_path)}\")\n",
" \n",
" # 3. Load the data files\n",
" clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
" genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
" \n",
" # 4. Print clinical data columns for inspection\n",
" print(\"\\nClinical data columns:\")\n",
" print(clinical_df.columns.tolist())\n",
" \n",
" # Print basic information about the datasets\n",
" print(f\"\\nClinical data shape: {clinical_df.shape}\")\n",
" print(f\"Genetic data shape: {genetic_df.shape}\")\n",
" \n",
" # Check if we have both gene and trait data\n",
" is_gene_available = genetic_df.shape[0] > 0\n",
" is_trait_available = clinical_df.shape[0] > 0\n",
" \n",
"else:\n",
" print(f\"No suitable directory found for {trait}.\")\n",
" is_gene_available = False\n",
" is_trait_available = False\n",
"\n",
"# Record the data availability\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# Exit if no suitable directory was found\n",
"if not matched_dir:\n",
" print(\"Skipping this trait as no suitable data was found.\")\n"
]
},
{
"cell_type": "markdown",
"id": "86393b98",
"metadata": {},
"source": [
"### Step 2: Find Candidate Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a060f7a3",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Age columns preview:\n",
"{'age_at_initial_pathologic_diagnosis': [51.0, 31.0, 53.0, 48.0, 49.0], 'age_began_smoking_in_years': [nan, nan, 22.0, nan, nan], 'days_to_birth': [-18886.0, -11611.0, -19473.0, -17839.0, -18215.0]}\n",
"\n",
"Gender columns preview:\n",
"{'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'FEMALE', 'FEMALE']}\n"
]
}
],
"source": [
"# 1. Identify candidate columns for age and gender\n",
"clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, \"TCGA_Cervical_Cancer_(CESC)\"))\n",
"clinical_data = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
"\n",
"# Candidate columns for age and gender\n",
"candidate_age_cols = [\n",
" 'age_at_initial_pathologic_diagnosis', \n",
" 'age_began_smoking_in_years', \n",
" 'days_to_birth'\n",
"]\n",
"candidate_gender_cols = ['gender']\n",
"\n",
"# 2. Preview the candidate columns\n",
"age_preview = {}\n",
"for col in candidate_age_cols:\n",
" if col in clinical_data.columns:\n",
" age_preview[col] = clinical_data[col].head(5).tolist()\n",
"\n",
"gender_preview = {}\n",
"for col in candidate_gender_cols:\n",
" if col in clinical_data.columns:\n",
" gender_preview[col] = clinical_data[col].head(5).tolist()\n",
"\n",
"print(\"Age columns preview:\")\n",
"print(age_preview)\n",
"print(\"\\nGender columns preview:\")\n",
"print(gender_preview)\n"
]
},
{
"cell_type": "markdown",
"id": "86c70047",
"metadata": {},
"source": [
"### Step 3: Select Demographic Features"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f9300dc5",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selecting demographic columns for age and gender information:\n",
"- age_at_initial_pathologic_diagnosis: Contains direct age values\n",
"- age_began_smoking_in_years: Contains age when patient started smoking (too many NaN values)\n",
"- days_to_birth: Contains negative days (days from birth to diagnosis date)\n",
"- gender: Contains direct gender information (FEMALE/MALE)\n",
"\n",
"Selected demographic columns:\n",
"Age column: age_at_initial_pathologic_diagnosis\n",
"Gender column: gender\n"
]
}
],
"source": [
"# Examine age columns\n",
"print(\"Selecting demographic columns for age and gender information:\")\n",
"\n",
"age_cols_info = {\n",
" 'age_at_initial_pathologic_diagnosis': \"Contains direct age values\",\n",
" 'age_began_smoking_in_years': \"Contains age when patient started smoking (too many NaN values)\",\n",
" 'days_to_birth': \"Contains negative days (days from birth to diagnosis date)\"\n",
"}\n",
"\n",
"for col, desc in age_cols_info.items():\n",
" print(f\"- {col}: {desc}\")\n",
"\n",
"# Select the most appropriate age column\n",
"# 'age_at_initial_pathologic_diagnosis' is the best choice as it directly contains patient age\n",
"age_col = 'age_at_initial_pathologic_diagnosis'\n",
"\n",
"# Examine gender columns\n",
"gender_cols_info = {\n",
" 'gender': \"Contains direct gender information (FEMALE/MALE)\"\n",
"}\n",
"\n",
"for col, desc in gender_cols_info.items():\n",
" print(f\"- {col}: {desc}\")\n",
"\n",
"# Select the gender column\n",
"gender_col = 'gender'\n",
"\n",
"# Print the selected columns\n",
"print(\"\\nSelected demographic columns:\")\n",
"print(f\"Age column: {age_col}\")\n",
"print(f\"Gender column: {gender_col}\")\n"
]
},
{
"cell_type": "markdown",
"id": "bd183d17",
"metadata": {},
"source": [
"### Step 4: Feature Engineering and Validation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "51a78be2",
"metadata": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene expression data saved to ../../output/preprocess/Cervical_Cancer/gene_data/TCGA.csv\n",
"Gene expression data shape after normalization: (19848, 308)\n",
"Clinical data saved to ../../output/preprocess/Cervical_Cancer/clinical_data/TCGA.csv\n",
"Clinical data shape: (313, 3)\n",
"Number of samples in clinical data: 313\n",
"Number of samples in genetic data: 308\n",
"Number of common samples: 308\n",
"Linked data shape: (308, 19851)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape after handling missing values: (308, 19851)\n",
"For the feature 'Cervical_Cancer', the least common label is '0' with 3 occurrences. This represents 0.97% of the dataset.\n",
"The distribution of the feature 'Cervical_Cancer' in this dataset is severely biased.\n",
"\n",
"Quartiles for 'Age':\n",
" 25%: 38.0\n",
" 50% (Median): 46.0\n",
" 75%: 56.0\n",
"Min: 20.0\n",
"Max: 88.0\n",
"The distribution of the feature 'Age' in this dataset is fine.\n",
"\n",
"For the feature 'Gender', the least common label is '0.0' with 308 occurrences. This represents 100.00% of the dataset.\n",
"The distribution of the feature 'Gender' in this dataset is severely biased.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset deemed not usable based on validation criteria. Data not saved.\n",
"Preprocessing completed.\n"
]
}
],
"source": [
"# Step 1: Extract and standardize clinical features\n",
"# Create clinical features dataframe with trait (Canavan Disease) using patient IDs\n",
"clinical_features = tcga_select_clinical_features(\n",
" clinical_df, \n",
" trait=trait, \n",
" age_col=age_col, \n",
" gender_col=gender_col\n",
")\n",
"\n",
"# Step 2: Normalize gene symbols in the gene expression data\n",
"# The gene symbols in TCGA genetic data are already standardized, but we'll normalize them for consistency\n",
"normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)\n",
"\n",
"# Save the normalized gene data\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_df.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
"print(f\"Gene expression data shape after normalization: {normalized_gene_df.shape}\")\n",
"\n",
"# Step 3: Link clinical and genetic data\n",
"# Transpose genetic data to have samples as rows and genes as columns\n",
"genetic_df_t = normalized_gene_df.T\n",
"# Save the clinical data for reference\n",
"os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
"clinical_features.to_csv(out_clinical_data_file)\n",
"print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
"print(f\"Clinical data shape: {clinical_features.shape}\")\n",
"\n",
"# Verify common indices between clinical and genetic data\n",
"clinical_indices = set(clinical_features.index)\n",
"genetic_indices = set(genetic_df_t.index)\n",
"common_indices = clinical_indices.intersection(genetic_indices)\n",
"print(f\"Number of samples in clinical data: {len(clinical_indices)}\")\n",
"print(f\"Number of samples in genetic data: {len(genetic_indices)}\")\n",
"print(f\"Number of common samples: {len(common_indices)}\")\n",
"\n",
"# Link the data by using the common indices\n",
"linked_data = pd.concat([clinical_features.loc[list(common_indices)], genetic_df_t.loc[list(common_indices)]], axis=1)\n",
"print(f\"Linked data shape: {linked_data.shape}\")\n",
"\n",
"# Step 4: Handle missing values in the linked data\n",
"linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
"print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
"\n",
"# Step 5: Determine whether the trait and demographic features are severely biased\n",
"trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)\n",
"\n",
"# Step 6: Conduct final quality validation and save information\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True,\n",
" cohort=\"TCGA\",\n",
" info_path=json_path,\n",
" is_gene_available=True,\n",
" is_trait_available=True,\n",
" is_biased=trait_biased,\n",
" df=linked_data,\n",
" note=f\"Dataset contains TCGA glioma and brain tumor samples with gene expression and clinical information for {trait}.\"\n",
")\n",
"\n",
"# Step 7: Save linked data if usable\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Dataset deemed not usable based on validation criteria. Data not saved.\")\n",
"\n",
"print(\"Preprocessing completed.\")"
]
}
],
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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