diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6a68d07..6f5b968 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -46,6 +46,16 @@ jobs: run: | python -m pytest tests/test_smoke_qrf.py -v python -m pytest tests/test_basic.py -v + - name: Run pipeline example + if: matrix.python-version == '3.13' + run: | + python examples/pipeline.py + - name: Upload microimputation results + if: always() + uses: actions/upload-artifact@v4 + with: + name: microimputation-results-${{ github.sha }} + path: microimputation-dashboard/public/microimputation_results.csv - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: diff --git a/.github/workflows/pr_code_changes.yaml b/.github/workflows/pr_code_changes.yaml index 0b9fea9..a2118b2 100644 --- a/.github/workflows/pr_code_changes.yaml +++ b/.github/workflows/pr_code_changes.yaml @@ -65,6 +65,16 @@ jobs: run: | python -m pytest tests/test_smoke_qrf.py -v python -m pytest tests/test_basic.py -v + - name: Run pipeline example + if: matrix.python-version == '3.13' + run: | + python examples/pipeline.py + - name: Upload microimputation results + if: always() + uses: actions/upload-artifact@v4 + with: + name: microimputation-results-${{ github.sha }} + path: microimputation-dashboard/public/microimputation_results.csv - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: diff --git a/Makefile b/Makefile index ebc2af6..2187fe9 100644 --- a/Makefile +++ b/Makefile @@ -32,4 +32,22 @@ changelog: build-changelog changelog.yaml --org PolicyEngine --repo microimpute --output CHANGELOG.md --template .github/changelog_template.md bump-version changelog.yaml pyproject.toml rm changelog_entry.yaml || true - touch changelog_entry.yaml \ No newline at end of file + touch changelog_entry.yaml + +# Dashboard commands +dashboard-install: + cd microimputation-dashboard && npm install + +dashboard-dev: + cd microimputation-dashboard && npm run dev + +dashboard-build: + cd microimputation-dashboard && npm run build + +dashboard-start: + cd microimputation-dashboard && npm run start + +dashboard: dashboard-install dashboard-dev + +dashboard-clean: + cd microimputation-dashboard && rm -rf node_modules .next out \ No newline at end of file diff --git a/changelog_entry.yaml b/changelog_entry.yaml index e69de29..625aeb4 100644 --- a/changelog_entry.yaml +++ b/changelog_entry.yaml @@ -0,0 +1,7 @@ +- bump: minor + changes: + added: + - Created microimputation-dashboard directory with initial dashboard components. + - File upload component to load microimputation results. + - Designed structure required for micorimputation_results.csv to load onto dashboard. + - Created `format_csv` function to format results for dashboard compatibility. diff --git a/examples/pipeline.py b/examples/pipeline.py new file mode 100644 index 0000000..70b8cb2 --- /dev/null +++ b/examples/pipeline.py @@ -0,0 +1,270 @@ +""" +Comprehensive pipeline demonstrating the full functionality of microimpute. + +This script demonstrates: +1. Running autoimpute on the diabetes dataset +2. Evaluating distribution preservation +3. Analyzing predictor correlations and mutual information +4. Assessing predictor importance via leave-one-out analysis +5. Analyzing impact of variable ordering via progressive inclusion +6. Formatting all results into a unified CSV for dashboard visualization +""" + +import pandas as pd +import numpy as np +import warnings +from sklearn.datasets import load_diabetes + +# Import microimpute functions +from microimpute.comparisons.autoimpute import autoimpute +from microimpute.comparisons.metrics import compare_distributions +from microimpute.evaluations.predictor_analysis import ( + compute_predictor_correlations, + leave_one_out_analysis, + progressive_predictor_inclusion, +) +from microimpute.utils.dashboard_formatter import format_csv + +# Import model classes +from microimpute.models import OLS + +# Suppress warnings for cleaner output +warnings.filterwarnings("ignore") + + +def run_full_pipeline(output_path="microimpute_results.csv"): + """ + Run the complete microimpute pipeline on the diabetes dataset. + + Parameters + ---------- + output_path : str + Path to save the formatted CSV output + + Returns + ------- + pd.DataFrame + Formatted results ready for dashboard visualization + """ + print("=" * 80) + print("MICROIMPUTE COMPREHENSIVE PIPELINE DEMONSTRATION") + print("=" * 80) + print() + + # ======================================================================== + # STEP 1: Load and prepare data + # ======================================================================== + print("STEP 1: Loading and preparing diabetes dataset...") + print("-" * 80) + + # Load the diabetes dataset + diabetes = load_diabetes() + diabetes_data = pd.DataFrame(diabetes.data, columns=diabetes.feature_names) + + # Split into donor and receiver portions (70/30 split) + np.random.seed(42) + donor_indices = np.random.choice( + len(diabetes_data), + size=int(0.7 * len(diabetes_data)), + replace=False, + ) + receiver_indices = np.array( + [i for i in range(len(diabetes_data)) if i not in donor_indices] + ) + + donor_data = diabetes_data.iloc[donor_indices].reset_index(drop=True) + receiver_data = diabetes_data.iloc[receiver_indices].reset_index(drop=True) + + # Define predictors and variables to impute + predictors = ["age", "sex", "bmi", "bp"] + imputed_variables = ["s1", "s4"] + + # Remove imputed variables from receiver data + receiver_data_without_targets = receiver_data.drop( + columns=imputed_variables + ) + + print(f"Donor data shape: {donor_data.shape}") + print(f"Receiver data shape: {receiver_data_without_targets.shape}") + print(f"Predictors: {predictors}") + print(f"Variables to impute: {imputed_variables}") + print() + + # ======================================================================== + # STEP 2: Run autoimpute + # ======================================================================== + print("STEP 2: Running autoimpute...") + print("-" * 80) + + autoimpute_results = autoimpute( + donor_data=donor_data, + receiver_data=receiver_data_without_targets, + predictors=predictors, + imputed_variables=imputed_variables, + tune_hyperparameters=False, + impute_all=True, # Get imputations from all methods + k_folds=3, + ) + + # Get the best method name + best_method_name = autoimpute_results.fitted_models[ + "best_method" + ].__class__.__name__ + print(f"Best performing method: {best_method_name}") + print() + + # ======================================================================== + # STEP 3: Evaluate distribution preservation + # ======================================================================== + print("STEP 3: Evaluating distribution preservation...") + print("-" * 80) + + # For distribution comparison, we compare donor data with receiver data + # that now has imputed values + distribution_comparison_df = compare_distributions( + donor_data=donor_data, + receiver_data=autoimpute_results.receiver_data, + imputed_variables=imputed_variables, + ) + + print("Distribution comparison results:") + print(distribution_comparison_df) + print() + + # ======================================================================== + # STEP 4: Analyze predictor correlations and mutual information + # ======================================================================== + print("STEP 4: Computing predictor correlations and mutual information...") + print("-" * 80) + + predictor_correlations = compute_predictor_correlations( + data=donor_data, + predictors=predictors, + imputed_variables=imputed_variables, + method="all", # Compute all correlation types + ) + + print("Correlation analysis completed:") + print( + f" - Pearson correlation matrix: {predictor_correlations['pearson'].shape}" + ) + print( + f" - Spearman correlation matrix: {predictor_correlations['spearman'].shape}" + ) + print( + f" - Mutual information matrix: {predictor_correlations['mutual_info'].shape}" + ) + print( + f" - Predictor-target MI: {predictor_correlations['predictor_target_mi'].shape}" + ) + print() + + # ======================================================================== + # STEP 5: Assess predictor importance (leave-one-out analysis) + # ======================================================================== + print("STEP 5: Performing leave-one-out predictor importance analysis...") + print("-" * 80) + + predictor_importance_df = leave_one_out_analysis( + data=donor_data, + predictors=predictors, + imputed_variables=imputed_variables, + model_class=OLS, # Use the same model class for consistency + quantiles=[0.1, 0.5, 0.9], + train_size=0.7, + n_jobs=1, + random_state=42, + ) + + print("Predictor importance results:") + print(predictor_importance_df[["predictor_removed", "relative_impact"]]) + print() + + # ======================================================================== + # STEP 6: Analyze impact of variable ordering + # ======================================================================== + print("STEP 6: Analyzing impact of variable ordering...") + print("-" * 80) + + progressive_results = progressive_predictor_inclusion( + data=donor_data, + predictors=predictors, + imputed_variables=imputed_variables, + model_class=OLS, + quantiles=[0.1, 0.5, 0.9], + train_size=0.7, + random_state=42, + ) + + # Extract results + progressive_inclusion_df = progressive_results["results_df"] + optimal_subset = progressive_results["optimal_subset"] + optimal_loss = progressive_results["optimal_loss"] + + print( + f"Optimal predictor order: {progressive_inclusion_df['predictor_added'].tolist()}" + ) + print(f"Optimal subset: {optimal_subset}") + print(f"Optimal loss: {optimal_loss:.6f}") + print() + + # ======================================================================== + # STEP 7: Format all results into unified CSV + # ======================================================================== + print("STEP 7: Formatting results for dashboard visualization...") + print("-" * 80) + + # Convert autoimpute_results to dictionary format expected by format_csv + autoimpute_dict = {"cv_results": autoimpute_results.cv_results} + + # Format all results + formatted_df = format_csv( + output_path=output_path, + autoimpute_result=autoimpute_dict, + comparison_metrics_df=None, # cv_results already contain this info + distribution_comparison_df=distribution_comparison_df, + predictor_correlations=predictor_correlations, + predictor_importance_df=predictor_importance_df, + progressive_inclusion_df=progressive_inclusion_df, + best_method_name=best_method_name, + ) + + print(f"Formatted DataFrame shape: {formatted_df.shape}") + print(f"Result types included: {formatted_df['type'].unique()}") + print() + + # ======================================================================== + # Summary + # ======================================================================== + print("=" * 80) + print("PIPELINE COMPLETED SUCCESSFULLY!") + print("=" * 80) + print() + print("Summary of results:") + print(f" - Total rows in output: {len(formatted_df)}") + print(f" - Best imputation method: {best_method_name}") + print(f" - Number of predictors analyzed: {len(predictors)}") + print(f" - Number of imputed variables: {len(imputed_variables)}") + print() + print("Output CSV contains:") + for result_type in formatted_df["type"].unique(): + count = len(formatted_df[formatted_df["type"] == result_type]) + print(f" - {result_type}: {count} rows") + print() + print( + f"The output file '{output_path}' is ready for dashboard visualization." + ) + print() + + return formatted_df + + +if __name__ == "__main__": + # Run the full pipeline + results_df = run_full_pipeline( + output_path="microimputation-dashboard/public/microimputation_results.csv" + ) + + # Display a preview of the results + print("Preview of formatted results (first 10 rows):") + print(results_df.head(10)) diff --git a/microimputation-dashboard/.gitignore b/microimputation-dashboard/.gitignore new file mode 100644 index 0000000..5ef6a52 --- /dev/null +++ b/microimputation-dashboard/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/microimputation-dashboard/README.md b/microimputation-dashboard/README.md new file mode 100644 index 0000000..101add8 --- /dev/null +++ b/microimputation-dashboard/README.md @@ -0,0 +1,40 @@ +# Microimputation dashboard + +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +##Β Features + +- πŸ“Š **Sample data**: Pre-loaded impuation results for immediate testing +- πŸ“ **File upload**: Drag & drop CSV file support with comprehensive validation +- 🌐 **URL loading**: Load CSV files from remote URLs with error handling +- πŸ”— **GitHub integration**: Load calibration data directly from GitHub Actions artifacts +- πŸ“ˆ **Interactive visualizations**: Charts and metrics coming soon... + +## Getting started + +First, run the development server: + +```bash +make dashboard +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn more + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/microimputation-dashboard/app/favicon.ico b/microimputation-dashboard/app/favicon.ico new file mode 100644 index 0000000..718d6fe Binary files /dev/null and b/microimputation-dashboard/app/favicon.ico differ diff --git a/microimputation-dashboard/app/globals.css b/microimputation-dashboard/app/globals.css new file mode 100644 index 0000000..a2dc41e --- /dev/null +++ b/microimputation-dashboard/app/globals.css @@ -0,0 +1,26 @@ +@import "tailwindcss"; + +:root { + --background: #ffffff; + --foreground: #171717; +} + +@theme inline { + --color-background: var(--background); + --color-foreground: var(--foreground); + --font-sans: var(--font-geist-sans); + --font-mono: var(--font-geist-mono); +} + +@media (prefers-color-scheme: dark) { + :root { + --background: #0a0a0a; + --foreground: #ededed; + } +} + +body { + background: var(--background); + color: var(--foreground); + font-family: Arial, Helvetica, sans-serif; +} diff --git a/microimputation-dashboard/app/layout.tsx b/microimputation-dashboard/app/layout.tsx new file mode 100644 index 0000000..f7fa87e --- /dev/null +++ b/microimputation-dashboard/app/layout.tsx @@ -0,0 +1,34 @@ +import type { Metadata } from "next"; +import { Geist, Geist_Mono } from "next/font/google"; +import "./globals.css"; + +const geistSans = Geist({ + variable: "--font-geist-sans", + subsets: ["latin"], +}); + +const geistMono = Geist_Mono({ + variable: "--font-geist-mono", + subsets: ["latin"], +}); + +export const metadata: Metadata = { + title: "Create Next App", + description: "Generated by create next app", +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + + {children} + + + ); +} diff --git a/microimputation-dashboard/app/page.tsx b/microimputation-dashboard/app/page.tsx new file mode 100644 index 0000000..3f1a3a0 --- /dev/null +++ b/microimputation-dashboard/app/page.tsx @@ -0,0 +1,175 @@ +'use client'; + +import { useState, useEffect } from 'react'; +import { useSearchParams } from 'next/navigation'; +import FileUpload from '@/components/FileUpload'; +import VisualizationDashboard from '@/components/VisualizationDashboard'; +import { parseImputationCSV } from '@/utils/csvParser'; +import { ImputationDataPoint } from '@/types/imputation'; +import { parseDeeplinkParams, GitHubArtifactInfo } from '@/utils/deeplinks'; + +export default function Home() { + const [data, setData] = useState([]); + const [fileName, setFileName] = useState(''); + const [showDashboard, setShowDashboard] = useState(false); + const [isLoadingFromDeeplink, setIsLoadingFromDeeplink] = useState(false); + const [githubArtifactInfo, setGithubArtifactInfo] = useState<{ + primary: GitHubArtifactInfo | null; + secondary?: GitHubArtifactInfo | null; + } | null>(null); + + // Comparison mode state + const [comparisonData, setComparisonData] = useState<{ + data1: ImputationDataPoint[]; + filename1: string; + data2: ImputationDataPoint[]; + filename2: string; + } | null>(null); + + const searchParams = useSearchParams(); + const deeplinkParams = parseDeeplinkParams(searchParams); + + useEffect(() => { + if (deeplinkParams) { + setIsLoadingFromDeeplink(true); + } + }, [deeplinkParams]); + + const handleFileLoad = (csvContent: string, filename: string) => { + try { + const parsedData = parseImputationCSV(csvContent); + setData(parsedData); + setFileName(filename); + setComparisonData(null); // Clear comparison data when loading single file + } catch (error) { + console.error('Error parsing CSV:', error); + alert('Failed to parse CSV file. Please check the file format.'); + } + }; + + const handleCompareLoad = (content1: string, filename1: string, content2: string, filename2: string) => { + try { + const data1 = parseImputationCSV(content1); + const data2 = parseImputationCSV(content2); + setComparisonData({ + data1, + filename1, + data2, + filename2 + }); + setData([]); // Clear single data when loading comparison + } catch (error) { + console.error('Error parsing comparison CSVs:', error); + alert('Failed to parse one or both CSV files. Please check the file formats.'); + } + }; + + const handleViewDashboard = () => { + if (data.length > 0 || comparisonData) { + setShowDashboard(true); + } + }; + + const handleBackToUpload = () => { + setShowDashboard(false); + setData([]); + setFileName(''); + setComparisonData(null); + setGithubArtifactInfo(null); + }; + + const handleDeeplinkLoadComplete = (primary: GitHubArtifactInfo | null, secondary?: GitHubArtifactInfo | null) => { + setIsLoadingFromDeeplink(false); + if (primary) { + setGithubArtifactInfo({ primary, secondary: secondary || undefined }); + setShowDashboard(true); + } + }; + + const handleGithubLoad = (primary: GitHubArtifactInfo | null, secondary?: GitHubArtifactInfo | null) => { + if (primary) { + setGithubArtifactInfo({ primary, secondary: secondary || undefined }); + } + }; + + return ( +
+ {/* Header */} +
+
+
+
+

+ Microimpute Dashboard +

+ + Beta + +
+ {showDashboard && ( + + )} +
+
+
+ + {/* Main content */} +
+ {!showDashboard ? ( + + ) : ( + + )} +
+ + {/* Footer */} + +
+ ); +} diff --git a/microimputation-dashboard/components/FileUpload.tsx b/microimputation-dashboard/components/FileUpload.tsx new file mode 100644 index 0000000..08e51e3 --- /dev/null +++ b/microimputation-dashboard/components/FileUpload.tsx @@ -0,0 +1,1607 @@ +'use client'; + +import { useState, useEffect, useCallback } from 'react'; +import { Upload, File as FileIcon, Link, Database, GitBranch } from 'lucide-react'; +import JSZip from 'jszip'; +import { DeeplinkParams, GitHubArtifactInfo } from '@/utils/deeplinks'; + +interface FileUploadProps { + onFileLoad: (content: string, filename: string) => void; + onViewDashboard: () => void; + onCompareLoad?: (content1: string, filename1: string, content2: string, filename2: string) => void; + deeplinkParams?: DeeplinkParams | null; + isLoadingFromDeeplink?: boolean; + onDeeplinkLoadComplete?: (primary: GitHubArtifactInfo | null, secondary?: GitHubArtifactInfo | null | undefined) => void; + onGithubLoad?: (primary: GitHubArtifactInfo | null, secondary?: GitHubArtifactInfo | null) => void; +} + +interface GitHubCommit { + sha: string; + commit: { + message: string; + author: { + date: string; + }; + }; +} + +interface GitHubBranch { + name: string; + commit: { + sha: string; + }; +} + +interface GitHubArtifact { + id: number; + name: string; + archive_download_url: string; + size_in_bytes: number; + created_at: string; +} + +export default function FileUpload({ + onFileLoad, + onViewDashboard, + onCompareLoad, + deeplinkParams, + isLoadingFromDeeplink, + onDeeplinkLoadComplete, + onGithubLoad +}: FileUploadProps) { + const [isDragOver, setIsDragOver] = useState(false); + const [isLoading, setIsLoading] = useState(false); + const [urlInput, setUrlInput] = useState(''); + const [activeTab, setActiveTab] = useState<'drop' | 'url' | 'sample' | 'github'>('drop'); + const [loadedFile, setLoadedFile] = useState(''); + const [error, setError] = useState(''); + + // GitHub-specific state + const [githubRepo, setGithubRepo] = useState(''); + const [githubBranches, setGithubBranches] = useState([]); + const [selectedBranch, setSelectedBranch] = useState(''); + const [githubCommits, setGithubCommits] = useState([]); + const [selectedCommit, setSelectedCommit] = useState(''); + const [availableArtifacts, setAvailableArtifacts] = useState([]); + const [selectedArtifact, setSelectedArtifact] = useState(''); + const [isLoadingGithubData, setIsLoadingGithubData] = useState(false); + + // Comparison mode state + const [comparisonMode, setComparisonMode] = useState(false); + const [selectedSecondBranch, setSelectedSecondBranch] = useState(''); + const [secondCommits, setSecondCommits] = useState([]); + const [selectedSecondCommit, setSelectedSecondCommit] = useState(''); + const [secondArtifacts, setSecondArtifacts] = useState([]); + const [selectedSecondArtifact, setSelectedSecondArtifact] = useState(''); + + // Helper function to load a single artifact from deeplink parameters + const loadArtifactFromDeeplink = useCallback(async (artifactInfo: GitHubArtifactInfo, githubToken: string): Promise => { + // First, get the artifacts for the specific commit + const [owner, repo] = artifactInfo.repo.split('/'); + const runsResponse = await fetch(`https://api.github.com/repos/${owner}/${repo}/actions/runs?head_sha=${artifactInfo.commit}`, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }); + + if (!runsResponse.ok) { + throw new Error(`Failed to fetch workflow runs: ${runsResponse.status} ${runsResponse.statusText}`); + } + + const runsData = await runsResponse.json(); + const completedRuns = runsData.workflow_runs.filter((run: { status: string }) => run.status === 'completed'); + + if (completedRuns.length === 0) { + throw new Error('No completed workflow runs found for this commit'); + } + + // Find the artifact by name + let targetArtifact = null; + for (const run of completedRuns) { + const artifactsResponse = await fetch(`https://api.github.com/repos/${owner}/${repo}/actions/runs/${run.id}/artifacts`, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }); + + if (artifactsResponse.ok) { + const artifactsData = await artifactsResponse.json(); + targetArtifact = artifactsData.artifacts.find((artifact: { name: string }) => artifact.name === artifactInfo.artifact); + if (targetArtifact) break; + } + } + + if (!targetArtifact) { + throw new Error(`Artifact "${artifactInfo.artifact}" not found for commit ${artifactInfo.commit}`); + } + + // Download and extract the artifact + const downloadResponse = await fetch(targetArtifact.archive_download_url, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }); + + if (!downloadResponse.ok) { + throw new Error(`Failed to download artifact: ${downloadResponse.status} ${downloadResponse.statusText}`); + } + + const zipBuffer = await downloadResponse.arrayBuffer(); + const zip = new JSZip(); + const zipContents = await zip.loadAsync(zipBuffer); + + // Find CSV files in the zip + const csvFiles = Object.keys(zipContents.files).filter(filename => filename.endsWith('.csv')); + + if (csvFiles.length === 0) { + throw new Error('No CSV files found in the artifact'); + } + + // Use the first CSV file found + const csvFile = zipContents.files[csvFiles[0]]; + const csvContent = await csvFile.async('text'); + + // Validate CSV format + validateCSVFormat(csvContent); + + return csvContent; + }, []); + + // Load GitHub artifacts directly from deeplink parameters + const loadDeeplinkArtifacts = useCallback(async (primary: GitHubArtifactInfo, secondary?: GitHubArtifactInfo) => { + const githubToken = process.env.NEXT_PUBLIC_GITHUB_TOKEN; + if (!githubToken) { + setError('GitHub token not configured. Please set NEXT_PUBLIC_GITHUB_TOKEN environment variable.'); + return; + } + + setIsLoading(true); + setError(''); + + try { + setError('πŸ”„ Loading data from GitHub artifacts...'); + + // Load primary artifact + const primaryData = await loadArtifactFromDeeplink(primary, githubToken); + + if (secondary && onCompareLoad) { + // Load secondary artifact for comparison + const secondaryData = await loadArtifactFromDeeplink(secondary, githubToken); + + // Generate display names with commit info + const primaryDisplayName = `${primary.repo}@${primary.branch} (${primary.commit.substring(0, 7)}) - ${primary.artifact}`; + const secondaryDisplayName = `${secondary.repo}@${secondary.branch} (${secondary.commit.substring(0, 7)}) - ${secondary.artifact}`; + + onCompareLoad(primaryData, primaryDisplayName, secondaryData, secondaryDisplayName); + setLoadedFile(`Comparison: ${primaryDisplayName} vs ${secondaryDisplayName}`); + } else { + // Single artifact load + const displayName = `${primary.repo}@${primary.branch} (${primary.commit.substring(0, 7)}) - ${primary.artifact}`; + onFileLoad(primaryData, displayName); + setLoadedFile(displayName); + } + + // Notify parent component that deeplink loading is complete + if (onDeeplinkLoadComplete) { + onDeeplinkLoadComplete(primary, secondary); + } + + setError(''); + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to load artifacts from deeplink'); + if (onDeeplinkLoadComplete) { + onDeeplinkLoadComplete(null); + } + } finally { + setIsLoading(false); + } + }, [onFileLoad, onCompareLoad, onDeeplinkLoadComplete, loadArtifactFromDeeplink]); + + // Handle deeplink loading on mount + useEffect(() => { + if (deeplinkParams && isLoadingFromDeeplink) { + setActiveTab('github'); + + if (deeplinkParams.mode === 'comparison' && deeplinkParams.primary && deeplinkParams.secondary) { + setComparisonMode(true); + setGithubRepo(deeplinkParams.primary.repo); + setSelectedBranch(deeplinkParams.primary.branch); + setSelectedCommit(deeplinkParams.primary.commit); + setSelectedArtifact(deeplinkParams.primary.artifact); + setSelectedSecondBranch(deeplinkParams.secondary.branch); + setSelectedSecondCommit(deeplinkParams.secondary.commit); + setSelectedSecondArtifact(deeplinkParams.secondary.artifact); + + // Auto-load comparison data + loadDeeplinkArtifacts(deeplinkParams.primary, deeplinkParams.secondary); + } else if (deeplinkParams.primary) { + setComparisonMode(false); + setGithubRepo(deeplinkParams.primary.repo); + setSelectedBranch(deeplinkParams.primary.branch); + setSelectedCommit(deeplinkParams.primary.commit); + setSelectedArtifact(deeplinkParams.primary.artifact); + + // Auto-load single artifact data + loadDeeplinkArtifacts(deeplinkParams.primary); + } + } + }, [deeplinkParams, isLoadingFromDeeplink, loadDeeplinkArtifacts]); + + function validateCSVFormat(content: string): void { + // Expected column names for microimputation dashboard + const EXPECTED_COLUMNS = [ + 'type', + 'method', + 'variable', + 'quantile', + 'metric_name', + 'metric_value', + 'split', + 'additional_info' + ]; + + const lines = content.trim().split('\n'); + + // Parse header + const header = lines[0].split(',').map(col => col.trim()); + + // Check if all expected columns are present + const missingColumns = EXPECTED_COLUMNS.filter(col => !header.includes(col)); + if (missingColumns.length > 0) { + throw new Error( + `Invalid CSV format: Missing required columns: ${missingColumns.join(', ')}. ` + + `Expected columns: ${EXPECTED_COLUMNS.join(', ')}` + ); + } + + // Check for extra columns + const extraColumns = header.filter(col => !EXPECTED_COLUMNS.includes(col)); + if (extraColumns.length > 0) { + throw new Error( + `Invalid CSV format: Unexpected columns found: ${extraColumns.join(', ')}. ` + + `Expected columns: ${EXPECTED_COLUMNS.join(', ')}` + ); + } + + // Check column order matches expected + const columnOrderMismatch = EXPECTED_COLUMNS.some((col, idx) => header[idx] !== col); + if (columnOrderMismatch) { + throw new Error( + `Invalid CSV format: Columns are not in the expected order. ` + + `Expected order: ${EXPECTED_COLUMNS.join(', ')}` + ); + } + + // Find the index of additional_info column + const additionalInfoIndex = header.indexOf('additional_info'); + + // Validate JSON format in additional_info column for a sample of rows + // We'll check the first 10 data rows to avoid processing large files + const maxRowsToCheck = Math.min(10, lines.length - 1); + + for (let i = 1; i <= maxRowsToCheck; i++) { + const line = lines[i]; + if (!line.trim()) continue; // Skip empty lines + + // Parse CSV row (basic parsing - doesn't handle quoted commas) + const columns = line.split(','); + + if (columns.length !== EXPECTED_COLUMNS.length) { + throw new Error( + `Invalid CSV format: Row ${i + 1} has ${columns.length} columns, ` + + `but ${EXPECTED_COLUMNS.length} columns are expected.` + ); + } + + let additionalInfo = columns[additionalInfoIndex]?.trim(); + + if (additionalInfo) { + // Handle CSV quote escaping: pandas doubles quotes when writing to CSV + // e.g., {"key": "value"} becomes "{""key"": ""value""}" in the CSV + // Remove outer quotes if present and unescape inner quotes + if (additionalInfo.startsWith('"') && additionalInfo.endsWith('"')) { + additionalInfo = additionalInfo.slice(1, -1); // Remove outer quotes + additionalInfo = additionalInfo.replace(/""/g, '"'); // Unescape doubled quotes + } + + try { + JSON.parse(additionalInfo); + } catch (e) { + throw new Error( + `Invalid CSV format: Row ${i + 1} contains invalid JSON in the 'additional_info' column. ` + + `Value: "${additionalInfo.substring(0, 50)}${additionalInfo.length > 50 ? '...' : ''}"` + ); + } + } + } + } + + async function processFile(file: globalThis.File) { + setIsLoading(true); + try { + const content = await file.text(); + + // Basic CSV validation + if (!content.trim()) { + throw new Error('The file appears to be empty or contains only whitespace.'); + } + + // Check for basic CSV structure + const lines = content.trim().split('\n'); + if (lines.length < 2) { + throw new Error('The file must contain at least a header row and one data row.'); + } + + // Validate CSV format (columns and JSON) + validateCSVFormat(content); + + onFileLoad(content, file.name); + setLoadedFile(file.name); + } catch (err) { + setError( + err instanceof Error + ? `File processing error: ${err.message}` + : 'Failed to read file. Please ensure it is a valid CSV file and try again.' + ); + } finally { + setIsLoading(false); + } + } + + function handleDragOver(e: React.DragEvent) { + e.preventDefault(); + setIsDragOver(true); + } + + function handleDragLeave(e: React.DragEvent) { + e.preventDefault(); + setIsDragOver(false); + } + + function handleDrop(e: React.DragEvent) { + e.preventDefault(); + setIsDragOver(false); + setError(''); + + const files = Array.from(e.dataTransfer.files); + + if (files.length === 0) { + setError('No files were dropped. Please try again.'); + return; + } + + if (files.length > 1) { + setError('Please drop only one file at a time. Multiple files are not supported.'); + return; + } + + const file = files[0]; + + if (!file.name.endsWith('.csv')) { + setError(`Invalid file type: "${file.name}". Please drop a CSV file (.csv extension required).`); + return; + } + + if (file.size === 0) { + setError('The dropped file appears to be empty. Please check your file and try again.'); + return; + } + + if (file.size > 500 * 1024 * 1024) { + // 500 MB limit + setError('File is too large (over 500 MB). Please use a smaller CSV file.'); + return; + } + + processFile(file); + } + + function handleFileInput(e: React.ChangeEvent) { + const file = e.target.files?.[0]; + setError(''); + + if (!file) { + setError('No file was selected. Please try again.'); + return; + } + + if (!file.name.endsWith('.csv')) { + setError(`Invalid file type: "${file.name}". Please select a CSV file (.csv extension required).`); + return; + } + + if (file.size === 0) { + setError('The selected file appears to be empty. Please check your file and try again.'); + return; + } + + if (file.size > 500 * 1024 * 1024) { + setError('File is too large (over 500 MB). Please select a smaller CSV file.'); + return; + } + + processFile(file); + } + + async function handleUrlLoad() { + if (!urlInput.trim()) { + setError('Please enter a URL to load a CSV file.'); + return; + } + + let url: URL; + try { + url = new URL(urlInput.trim()); + } catch { + setError('Invalid URL format. Please enter a valid URL (e.g., https://example.com/data.csv).'); + return; + } + + if (!url.pathname.toLowerCase().endsWith('.csv') && !urlInput.toLowerCase().includes('csv')) { + setError('URL should point to a CSV file. Please ensure the URL ends with .csv or contains CSV data.'); + return; + } + + if (url.protocol !== 'https:' && url.protocol !== 'http:') { + setError('Only HTTP and HTTPS URLs are supported. Please use a web URL.'); + return; + } + + setIsLoading(true); + setError(''); + + try { + const controller = new AbortController(); + const timeoutId = setTimeout(() => controller.abort(), 30_000); // 30 s timeout + + const response = await fetch(urlInput.trim(), { + signal: controller.signal, + headers: { Accept: 'text/csv, text/plain, */*' } + }); + + clearTimeout(timeoutId); + + if (!response.ok) { + const status = response.status; + const map: Record = { + 404: 'File not found (404). Please check the URL and try again.', + 403: 'Access forbidden (403). The server denied access to this file.', + 401: 'Authentication required (401). This file requires login credentials.', + 500: 'Server error (500). The remote server encountered an error.', + 503: 'Service unavailable (503). The server is temporarily unavailable.' + }; + throw new Error(map[status] || `HTTP error ${status}: ${response.statusText}`); + } + + const contentType = response.headers.get('content-type') || ''; + if ( + !contentType.includes('text/csv') && + !contentType.includes('text/plain') && + !contentType.includes('application/csv') + ) { + setError(`Warning: Server returned content type "${contentType}". This may not be a CSV file.`); + } + + const content = await response.text(); + + if (!content.trim()) { + throw new Error('The downloaded file appears to be empty.'); + } + + const lines = content.trim().split('\n'); + if (lines.length < 2) { + throw new Error('The downloaded file must contain at least a header row and one data row.'); + } + + // Validate CSV format (columns and JSON) + validateCSVFormat(content); + + const filename = url.pathname.split('/').pop() || 'remote-file.csv'; + onFileLoad(content, filename); + setLoadedFile(filename); + } catch (err) { + if (err instanceof Error) { + if (err.name === 'AbortError') { + setError('Request timed out after 30 s. Please check your connection and try again.'); + } else if (err.message.includes('Failed to fetch')) { + setError('Network error: Unable to reach the URL. Please check the address and your connection.'); + } else { + setError(`Failed to load from URL: ${err.message}`); + } + } else { + setError('Unknown error occurred while loading the URL. Please try again.'); + } + } finally { + setIsLoading(false); + } + } + + async function handleSampleLoad() { + setIsLoading(true); + setError(''); + + try { + const response = await fetch('/microimputation_results.csv'); + if (!response.ok) { + throw new Error(`HTTP error! status: ${response.status}`); + } + const content = await response.text(); + + // Validate CSV format (columns and JSON) + validateCSVFormat(content); + + onFileLoad(content, 'microimputation_results.csv'); + setLoadedFile('microimputation_results.csv'); + } catch (err) { + setError(`Failed to load sample data: ${err instanceof Error ? err.message : 'Unknown error'}`); + } finally { + setIsLoading(false); + } + } + + async function fetchGithubBranches() { + if (!githubRepo.trim()) { + setError('Please enter a GitHub repository (e.g., owner/repo)'); + return; + } + + const repoMatch = githubRepo.trim().match(/^([^/]+)\/([^/]+)$/); + if (!repoMatch) { + setError('Invalid repository format. Use "owner/repo" format (e.g., "PolicyEngine/microimpute")'); + return; + } + + const githubToken = process.env.NEXT_PUBLIC_GITHUB_TOKEN; + if (!githubToken) { + setError('GitHub token not configured. Please set NEXT_PUBLIC_GITHUB_TOKEN environment variable.'); + return; + } + + setIsLoadingGithubData(true); + setError(''); + + try { + // Fetch all branches with pagination support + const allBranches: GitHubBranch[] = []; + let page = 1; + const perPage = 100; // Maximum allowed by GitHub API + + while (true) { + const response = await fetch(`https://api.github.com/repos/${githubRepo}/branches?per_page=${perPage}&page=${page}`, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }); + + if (!response.ok) { + if (response.status === 404) { + throw new Error('Repository not found. Please check the repository name and ensure it is accessible.'); + } else if (response.status === 403) { + throw new Error('Access forbidden. Please check your GitHub token permissions or repository access.'); + } + throw new Error(`Failed to fetch branches: ${response.status} ${response.statusText}`); + } + + const branches: GitHubBranch[] = await response.json(); + + if (branches.length === 0) { + // No more branches to fetch + break; + } + + allBranches.push(...branches); + + // If we got fewer branches than requested, we've reached the end + if (branches.length < perPage) { + break; + } + + page++; + + // Safety check to prevent infinite loops (GitHub repos rarely have more than 1000 branches) + if (page > 10) { + console.warn('Stopped fetching branches after 10 pages (1000 branches) to prevent excessive API calls'); + break; + } + } + + setGithubBranches(allBranches); + + // Auto-select main/master branch if available + const defaultBranch = allBranches.find(b => b.name === 'main' || b.name === 'master'); + if (defaultBranch) { + setSelectedBranch(defaultBranch.name); + await fetchGithubCommits(defaultBranch.name); + } + } catch (err) { + setError(`GitHub API error: ${err instanceof Error ? err.message : 'Unknown error'}`); + } finally { + setIsLoadingGithubData(false); + } + } + + async function fetchGithubCommits(branch: string) { + if (!githubRepo.trim() || !branch) return; + + const githubToken = process.env.NEXT_PUBLIC_GITHUB_TOKEN; + if (!githubToken) { + setError('GitHub token not configured. Please set NEXT_PUBLIC_GITHUB_TOKEN environment variable.'); + return; + } + + setIsLoadingGithubData(true); + try { + const response = await fetch(`https://api.github.com/repos/${githubRepo}/commits?sha=${branch}&per_page=20`, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }); + if (!response.ok) { + if (response.status === 404) { + throw new Error('Branch not found or repository is private.'); + } else if (response.status === 403) { + throw new Error('Access forbidden. Please check your GitHub token permissions or repository access.'); + } + throw new Error(`Failed to fetch commits: ${response.status} ${response.statusText}`); + } + + const commits: GitHubCommit[] = await response.json(); + setGithubCommits(commits); + + // Auto-select latest commit and fetch its artifacts + if (commits.length > 0) { + setSelectedCommit(commits[0].sha); + await fetchGithubArtifacts(commits[0].sha); + } + } catch (err) { + setError(`GitHub API error: ${err instanceof Error ? err.message : 'Unknown error'}`); + } finally { + setIsLoadingGithubData(false); + } + } + + async function fetchGithubArtifacts(commitSha: string) { + if (!githubRepo.trim() || !commitSha) return; + + const githubToken = process.env.NEXT_PUBLIC_GITHUB_TOKEN; + if (!githubToken) { + setError('GitHub token not configured. Please set NEXT_PUBLIC_GITHUB_TOKEN environment variable.'); + return; + } + + setIsLoadingGithubData(true); + setAvailableArtifacts([]); + setSelectedArtifact(''); + + try { + const [owner, repo] = githubRepo.split('/'); + + // Get workflow runs for the commit + const runsResponse = await fetch( + `https://api.github.com/repos/${owner}/${repo}/actions/runs?head_sha=${commitSha}`, + { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + } + ); + + if (!runsResponse.ok) { + if (runsResponse.status === 403) { + throw new Error(`GitHub API rate limit exceeded or token permissions insufficient (403). Please try again later or check your token permissions.`); + } else if (runsResponse.status === 404) { + throw new Error(`Repository or commit not found (404). Please check the repository name and commit SHA.`); + } else { + throw new Error(`Failed to fetch workflow runs: ${runsResponse.status} ${runsResponse.statusText}`); + } + } + + const runsData = await runsResponse.json(); + const runs = runsData.workflow_runs; + + if (!runs || runs.length === 0) { + setError('No workflow runs found for this commit.'); + return; + } + + // Collect all imputation artifacts from completed runs + const allArtifacts: GitHubArtifact[] = []; + + for (const run of runs) { + if (run.status !== 'completed') continue; + + try { + const artifactsResponse = await fetch( + `https://api.github.com/repos/${owner}/${repo}/actions/runs/${run.id}/artifacts`, + { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + } + ); + + if (!artifactsResponse.ok) continue; + + const artifactsData = await artifactsResponse.json(); + const artifacts = artifactsData.artifacts; + + // Filter for imputation artifacts + const imputationArtifacts = artifacts.filter((artifact: GitHubArtifact) => + artifact.name.toLowerCase().includes('impute') || + artifact.name.toLowerCase().includes('imputation') || + artifact.name.toLowerCase().includes('result') || + artifact.name.toLowerCase().includes('.csv') + ); + + allArtifacts.push(...imputationArtifacts); + } catch { + continue; + } + } + + if (allArtifacts.length === 0) { + setError('No imputation artifacts found for this commit.'); + return; + } + + // Remove duplicates and sort by creation date (newest first) + const uniqueArtifacts = allArtifacts + .filter((artifact, index, self) => + index === self.findIndex(a => a.name === artifact.name) + ) + .sort((a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime()); + + setAvailableArtifacts(uniqueArtifacts); + + // Auto-select the first artifact + if (uniqueArtifacts.length > 0) { + setSelectedArtifact(uniqueArtifacts[0].id.toString()); + } + + } catch (err) { + setError(`Failed to fetch artifacts: ${err instanceof Error ? err.message : 'Unknown error'}`); + } finally { + setIsLoadingGithubData(false); + } + } + + async function loadGithubArtifact() { + if (!selectedArtifact) { + setError('Please select an artifact to load'); + return; + } + + const artifact = availableArtifacts.find(a => a.id.toString() === selectedArtifact); + if (!artifact) { + setError('Selected artifact not found'); + return; + } + + const githubToken = process.env.NEXT_PUBLIC_GITHUB_TOKEN; + if (!githubToken) { + setError('GitHub token not configured. Please set NEXT_PUBLIC_GITHUB_TOKEN environment variable.'); + return; + } + + setIsLoading(true); + setError(''); + + try { + setError('πŸ”„ Downloading and extracting CSV from artifact...'); + + const downloadResponse = await fetch(artifact.archive_download_url, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }); + + if (!downloadResponse.ok) { + throw new Error(`Failed to download artifact: ${downloadResponse.status}`); + } + + const zipBuffer = await downloadResponse.arrayBuffer(); + const zip = new JSZip(); + const zipContent = await zip.loadAsync(zipBuffer); + + // Find CSV files in the ZIP + const csvFiles = Object.keys(zipContent.files).filter(filename => + filename.toLowerCase().endsWith('.csv') && !zipContent.files[filename].dir + ); + + if (csvFiles.length === 0) { + throw new Error('No CSV files found in the artifact ZIP'); + } + + // Use the first CSV file found + const csvFilename = csvFiles[0]; + const csvFile = zipContent.files[csvFilename]; + const csvContent = await csvFile.async('text'); + + // Validate the CSV content + if (!csvContent.trim()) { + throw new Error('The extracted CSV file is empty'); + } + + // Check for basic CSV structure + const lines = csvContent.trim().split('\n'); + if (lines.length < 2) { + throw new Error('The CSV must contain at least a header row and one data row'); + } + + // Validate CSV format (columns and JSON) + validateCSVFormat(csvContent); + + // Success! Load the CSV into the dashboard with commit info + const commitShort = selectedCommit.slice(0, 8); + const branchInfo = selectedBranch ? ` (${selectedBranch})` : ''; + const displayName = `${csvFilename} @ ${commitShort}${branchInfo}`; + + onFileLoad(csvContent, displayName); + setLoadedFile(displayName); + setError(''); + + // Notify parent component about GitHub artifact info for sharing + if (onGithubLoad) { + const artifactInfo: GitHubArtifactInfo = { + repo: githubRepo, + branch: selectedBranch, + commit: selectedCommit, + artifact: artifact.name + }; + onGithubLoad(artifactInfo, null); + } + + // Clear the GitHub state since we successfully loaded the file + setGithubRepo(''); + setGithubBranches([]); + setSelectedBranch(''); + setGithubCommits([]); + setSelectedCommit(''); + setAvailableArtifacts([]); + setSelectedArtifact(''); + + } catch (extractError) { + console.error('CSV extraction error:', extractError); + setError(`❌ Failed to extract CSV: ${extractError instanceof Error ? extractError.message : 'Unknown error'}. Try using the URL tab with a direct CSV link.`); + } finally { + setIsLoading(false); + } + } + + async function fetchSecondBranchCommits(branch: string) { + if (!githubRepo.trim() || !branch) return; + + const githubToken = process.env.NEXT_PUBLIC_GITHUB_TOKEN; + if (!githubToken) { + setError('GitHub token not configured. Please set NEXT_PUBLIC_GITHUB_TOKEN environment variable.'); + return; + } + + setIsLoadingGithubData(true); + try { + const response = await fetch(`https://api.github.com/repos/${githubRepo}/commits?sha=${branch}&per_page=20`, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }); + if (!response.ok) { + if (response.status === 404) { + throw new Error('Branch not found or repository is private.'); + } else if (response.status === 403) { + throw new Error('Access forbidden. Please check your GitHub token permissions or repository access.'); + } + throw new Error(`Failed to fetch commits: ${response.status} ${response.statusText}`); + } + + const commits: GitHubCommit[] = await response.json(); + setSecondCommits(commits); + + // Auto-select latest commit and fetch its artifacts + if (commits.length > 0) { + setSelectedSecondCommit(commits[0].sha); + await fetchSecondArtifacts(commits[0].sha); + } + } catch (err) { + setError(`GitHub API error: ${err instanceof Error ? err.message : 'Unknown error'}`); + } finally { + setIsLoadingGithubData(false); + } + } + + async function fetchSecondArtifacts(commitSha: string) { + if (!githubRepo.trim() || !commitSha) return; + + const githubToken = process.env.NEXT_PUBLIC_GITHUB_TOKEN; + if (!githubToken) { + setError('GitHub token not configured. Please set NEXT_PUBLIC_GITHUB_TOKEN environment variable.'); + return; + } + + setIsLoadingGithubData(true); + setSecondArtifacts([]); + setSelectedSecondArtifact(''); + + try { + const [owner, repo] = githubRepo.split('/'); + + // Get workflow runs for the commit + const runsResponse = await fetch( + `https://api.github.com/repos/${owner}/${repo}/actions/runs?head_sha=${commitSha}`, + { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + } + ); + + if (!runsResponse.ok) { + if (runsResponse.status === 403) { + throw new Error(`GitHub API rate limit exceeded or token permissions insufficient (403). Please try again later or check your token permissions.`); + } else if (runsResponse.status === 404) { + throw new Error(`Repository or commit not found (404). Please check the repository name and commit SHA.`); + } else { + throw new Error(`Failed to fetch workflow runs: ${runsResponse.status} ${runsResponse.statusText}`); + } + } + + const runsData = await runsResponse.json(); + const runs = runsData.workflow_runs; + + if (!runs || runs.length === 0) { + setError('No workflow runs found for this commit.'); + return; + } + + // Collect all imputation artifacts from completed runs + const allArtifacts: GitHubArtifact[] = []; + + for (const run of runs) { + if (run.status !== 'completed') continue; + + try { + const artifactsResponse = await fetch( + `https://api.github.com/repos/${owner}/${repo}/actions/runs/${run.id}/artifacts`, + { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + } + ); + + if (!artifactsResponse.ok) continue; + + const artifactsData = await artifactsResponse.json(); + const artifacts = artifactsData.artifacts; + + // Filter for imputation artifacts + const imputationArtifacts = artifacts.filter((artifact: GitHubArtifact) => + artifact.name.toLowerCase().includes('impute') || + artifact.name.toLowerCase().includes('imputation') || + artifact.name.toLowerCase().includes('result') || + artifact.name.toLowerCase().includes('.csv') + ); + + allArtifacts.push(...imputationArtifacts); + } catch { + continue; + } + } + + if (allArtifacts.length === 0) { + setError('No imputation artifacts found for this commit.'); + return; + } + + // Remove duplicates and sort by creation date (newest first) + const uniqueArtifacts = allArtifacts + .filter((artifact, index, self) => + index === self.findIndex(a => a.name === artifact.name) + ) + .sort((a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime()); + + setSecondArtifacts(uniqueArtifacts); + + // Auto-select the first artifact + if (uniqueArtifacts.length > 0) { + setSelectedSecondArtifact(uniqueArtifacts[0].id.toString()); + } + + } catch (err) { + setError(`Failed to fetch artifacts: ${err instanceof Error ? err.message : 'Unknown error'}`); + } finally { + setIsLoadingGithubData(false); + } + } + + async function loadComparisonData() { + if (!selectedArtifact || !selectedSecondArtifact || !onCompareLoad) { + setError('Please select artifacts from both commits to compare'); + return; + } + + const firstArtifact = availableArtifacts.find(a => a.id.toString() === selectedArtifact); + const secondArtifact = secondArtifacts.find(a => a.id.toString() === selectedSecondArtifact); + + if (!firstArtifact || !secondArtifact) { + setError('Selected artifacts not found'); + return; + } + + const githubToken = process.env.NEXT_PUBLIC_GITHUB_TOKEN; + if (!githubToken) { + setError('GitHub token not configured. Please set NEXT_PUBLIC_GITHUB_TOKEN environment variable.'); + return; + } + + setIsLoading(true); + setError(''); + + try { + setError('πŸ”„ Downloading and extracting CSV files for comparison...'); + + // Download both artifacts + const [firstDownload, secondDownload] = await Promise.all([ + fetch(firstArtifact.archive_download_url, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }), + fetch(secondArtifact.archive_download_url, { + headers: { + 'Authorization': `Bearer ${githubToken}`, + 'Accept': 'application/vnd.github.v3+json', + 'User-Agent': 'PolicyEngine-Dashboard/1.0' + } + }) + ]); + + if (!firstDownload.ok || !secondDownload.ok) { + throw new Error('Failed to download one or both artifacts'); + } + + // Extract CSVs from both artifacts + const [firstZipBuffer, secondZipBuffer] = await Promise.all([ + firstDownload.arrayBuffer(), + secondDownload.arrayBuffer() + ]); + + const firstZip = new JSZip(); + const secondZip = new JSZip(); + const [firstZipContent, secondZipContent] = await Promise.all([ + firstZip.loadAsync(firstZipBuffer), + secondZip.loadAsync(secondZipBuffer) + ]); + + // Find CSV files in both ZIPs + const firstCsvFiles = Object.keys(firstZipContent.files).filter(filename => + filename.toLowerCase().endsWith('.csv') && !firstZipContent.files[filename].dir + ); + const secondCsvFiles = Object.keys(secondZipContent.files).filter(filename => + filename.toLowerCase().endsWith('.csv') && !secondZipContent.files[filename].dir + ); + + if (firstCsvFiles.length === 0 || secondCsvFiles.length === 0) { + throw new Error('No CSV files found in one or both artifacts'); + } + + // Extract CSV content + const [firstCsvContent, secondCsvContent] = await Promise.all([ + firstZipContent.files[firstCsvFiles[0]].async('text'), + secondZipContent.files[secondCsvFiles[0]].async('text') + ]); + + // Create display names with commit info + const firstCommitShort = selectedCommit.slice(0, 8); + const secondCommitShort = selectedSecondCommit.slice(0, 8); + + const firstBranchInfo = selectedBranch !== selectedSecondBranch ? ` (${selectedBranch})` : ''; + const secondBranchInfo = selectedBranch !== selectedSecondBranch ? ` (${selectedSecondBranch})` : ''; + + const firstName = `${firstCsvFiles[0]} @ ${firstCommitShort}${firstBranchInfo}`; + const secondName = `${secondCsvFiles[0]} @ ${secondCommitShort}${secondBranchInfo}`; + + // Load into comparison mode + onCompareLoad(firstCsvContent, firstName, secondCsvContent, secondName); + + // Notify parent component about GitHub artifact info for sharing + if (onGithubLoad) { + const primaryArtifactInfo: GitHubArtifactInfo = { + repo: githubRepo, + branch: selectedBranch, + commit: selectedCommit, + artifact: firstArtifact.name + }; + const secondaryArtifactInfo: GitHubArtifactInfo = { + repo: githubRepo, + branch: selectedSecondBranch, + commit: selectedSecondCommit, + artifact: secondArtifact.name + }; + onGithubLoad(primaryArtifactInfo, secondaryArtifactInfo); + } + + setError(''); + + } catch (extractError) { + console.error('Comparison extraction error:', extractError); + setError(`❌ Failed to extract comparison data: ${extractError instanceof Error ? extractError.message : 'Unknown error'}`); + } finally { + setIsLoading(false); + } + } + + return ( +
+
+

Load imputation data

+

Choose how you would like to load your CSV file

+
+ + {error && ( +
+

{error}

+
+ )} + + {loadedFile && ( +
+

Successfully loaded: {loadedFile}

+
+ )} + + {/* Tab navigation */} +
+ + + + +
+ + {/* Tab content */} + {activeTab === 'drop' && ( +
+
+ +

Drop your CSV file here

+

or click to browse files

+ + +
+
+ )} + + {activeTab === 'url' && ( +
+
+ +
+ setUrlInput(e.target.value)} + placeholder="https://example.com/data.csv" + className="flex-1 px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent" + /> + +
+

Enter a direct URL to a CSV file accessible via HTTP/HTTPS

+
+
+ )} + + {activeTab === 'sample' && ( +
+
+
+ +
+

Load sample imputation data

+

+ Try the dashboard with sample microimputation results to explore the visualization features. +

+
    +
  • β€’ Shows imputation results for various variables
    • +
  • β€’ Benchmarks different imputation methods
    • +
  • β€’ Includes validation metrics
    • +
+ +
+
+
+
+ )} + + {activeTab === 'github' && ( +
+
+
+ +
+

+ Load from GitHub repository +

+

+ Load imputation data from GitHub Actions artifacts in public repositories. +

+ + {/* Repository Input */} +
+ +
+ setGithubRepo(e.target.value)} + placeholder="PolicyEngine/microimpute" + className="flex-1 px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500" + /> + +
+
+ + {/* Comparison Mode Toggle */} +
+ +

+ Enable this to compare imputation results between different branches or commits +

+
+ + {/* Branch Selection */} + {githubBranches.length > 0 && ( +
+ + +
+ )} + + {/* Commit Selection */} + {githubCommits.length > 0 && ( +
+ + + {selectedCommit && ( +

+ {githubCommits.find(c => c.sha === selectedCommit)?.commit.author.date && + new Date(githubCommits.find(c => c.sha === selectedCommit)!.commit.author.date).toLocaleString() + } +

+ )} +
+ )} + + {/* Artifact Selection */} + {availableArtifacts.length > 0 && ( +
+ + + {selectedArtifact && ( +

+ {availableArtifacts.find(a => a.id.toString() === selectedArtifact)?.created_at && + `Created: ${new Date(availableArtifacts.find(a => a.id.toString() === selectedArtifact)!.created_at).toLocaleString()}` + } +

+ )} +
+ )} + + {/* Second Selection for Comparison */} + {comparisonMode && githubBranches.length > 0 && ( +
+

Second Imputation Run (for comparison)

+ + {/* Second Branch Selection */} +
+ + +
+ + {/* Second Commit Selection */} + {secondCommits.length > 0 && ( +
+ + + {selectedSecondCommit && ( +

+ {secondCommits.find(c => c.sha === selectedSecondCommit)?.commit.author.date && + new Date(secondCommits.find(c => c.sha === selectedSecondCommit)!.commit.author.date).toLocaleString() + } +

+ )} +
+ )} + + {/* Second Artifact Selection */} + {secondArtifacts.length > 0 && ( +
+ + + {selectedSecondArtifact && ( +

+ {secondArtifacts.find(a => a.id.toString() === selectedSecondArtifact)?.created_at && + `Created: ${new Date(secondArtifacts.find(a => a.id.toString() === selectedSecondArtifact)!.created_at).toLocaleString()}` + } +

+ )} +
+ )} +
+ )} + + {/* Load Button */} + {selectedArtifact && (!comparisonMode || selectedSecondArtifact) && ( + + )} + +
+

πŸ“Œ Note: This feature finds imputation CSV files in GitHub Actions artifacts.

+
    +
  • β€’ βœ… Works with PolicyEngine public repositories
    • +
  • β€’ βœ… Authenticated access to download artifacts
    • +
  • β€’ βœ… Automatically finds imputation results from CI/CD runs
    • +
  • β€’ βœ… Full CSV extraction from ZIP artifacts
    • +
+
+
+
+
+
+ )} + + {/* View Dashboard Button */} + {loadedFile && ( +
+ +
+ )} + + {/* Global loading indicator */} + {(isLoading || isLoadingGithubData) && ( +
+
+

+ {isLoadingGithubData ? 'Loading GitHub data...' : 'Loading file...'} +

+
+ )} +
+ ); +} \ No newline at end of file diff --git a/microimputation-dashboard/components/VisualizationDashboard.tsx b/microimputation-dashboard/components/VisualizationDashboard.tsx new file mode 100644 index 0000000..f5806da --- /dev/null +++ b/microimputation-dashboard/components/VisualizationDashboard.tsx @@ -0,0 +1,49 @@ +'use client'; + +import { ImputationDataPoint } from '@/types/imputation'; +import { GitHubArtifactInfo } from '@/utils/deeplinks'; + +interface VisualizationDashboardProps { + data: ImputationDataPoint[]; + fileName: string; + comparisonData?: { + data: ImputationDataPoint[]; + filename: string; + }; + githubArtifactInfo?: { + primary: GitHubArtifactInfo | null; + secondary?: GitHubArtifactInfo | null; + } | null; +} + +export default function VisualizationDashboard({ + data, + fileName, + comparisonData, + githubArtifactInfo +}: VisualizationDashboardProps) { + return ( +
+
+

Visualization Dashboard

+

Coming Soon...

+

+ The visualization components for microimputation results will be implemented here. +

+
+

+ Successfully loaded: {fileName} +

+

+ Records: {data.length} +

+ {comparisonData && ( +

+ Comparison file: {comparisonData.filename} ({comparisonData.data.length} records) +

+ )} +
+
+
+ ); +} \ No newline at end of file diff --git a/microimputation-dashboard/eslint.config.mjs b/microimputation-dashboard/eslint.config.mjs new file mode 100644 index 0000000..719cea2 --- /dev/null +++ b/microimputation-dashboard/eslint.config.mjs @@ -0,0 +1,25 @@ +import { dirname } from "path"; +import { fileURLToPath } from "url"; +import { FlatCompat } from "@eslint/eslintrc"; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = dirname(__filename); + +const compat = new FlatCompat({ + baseDirectory: __dirname, +}); + +const eslintConfig = [ + ...compat.extends("next/core-web-vitals", "next/typescript"), + { + ignores: [ + "node_modules/**", + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ], + }, +]; + +export default eslintConfig; diff --git a/microimputation-dashboard/next.config.ts b/microimputation-dashboard/next.config.ts new file mode 100644 index 0000000..e9ffa30 --- /dev/null +++ b/microimputation-dashboard/next.config.ts @@ -0,0 +1,7 @@ +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + /* config options here */ +}; + +export default nextConfig; diff --git a/microimputation-dashboard/package-lock.json b/microimputation-dashboard/package-lock.json new file mode 100644 index 0000000..36ac1d9 --- /dev/null +++ b/microimputation-dashboard/package-lock.json @@ -0,0 +1,6617 @@ +{ + "name": "microimputation-dashboard", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "microimputation-dashboard", + "version": "0.1.0", + "dependencies": { + "jszip": "^3.10.1", + "lucide-react": "^0.546.0", + "next": "15.5.6", + "papaparse": "^5.5.3", + "react": "19.1.0", + "react-dom": "19.1.0", + "recharts": "^3.3.0" + }, + "devDependencies": { + "@eslint/eslintrc": "^3", + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/papaparse": "^5.3.16", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "15.5.6", + 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"https://github.com/sponsors/sindresorhus" + } + } + } +} diff --git a/microimputation-dashboard/package.json b/microimputation-dashboard/package.json new file mode 100644 index 0000000..ab76b36 --- /dev/null +++ b/microimputation-dashboard/package.json @@ -0,0 +1,32 @@ +{ + "name": "microimputation-dashboard", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev --turbopack", + "build": "next build --turbopack", + "start": "next start", + "lint": "eslint" + }, + "dependencies": { + "jszip": "^3.10.1", + "lucide-react": "^0.546.0", + "next": "15.5.6", + "papaparse": "^5.5.3", + "react": "19.1.0", + "react-dom": "19.1.0", + "recharts": "^3.3.0" + }, + "devDependencies": { + "@eslint/eslintrc": "^3", + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/papaparse": "^5.3.16", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "15.5.6", + "tailwindcss": "^4", + "typescript": "^5" + } +} diff --git a/microimputation-dashboard/postcss.config.mjs b/microimputation-dashboard/postcss.config.mjs new file mode 100644 index 0000000..c7bcb4b --- /dev/null +++ b/microimputation-dashboard/postcss.config.mjs @@ -0,0 +1,5 @@ +const config = { + plugins: ["@tailwindcss/postcss"], +}; + +export default config; diff --git a/microimputation-dashboard/public/file.svg b/microimputation-dashboard/public/file.svg new file mode 100644 index 0000000..004145c --- /dev/null +++ b/microimputation-dashboard/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/microimputation-dashboard/public/globe.svg b/microimputation-dashboard/public/globe.svg new file mode 100644 index 0000000..567f17b --- /dev/null +++ b/microimputation-dashboard/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/microimputation-dashboard/public/next.svg b/microimputation-dashboard/public/next.svg new file mode 100644 index 0000000..5174b28 --- /dev/null +++ b/microimputation-dashboard/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/microimputation-dashboard/public/vercel.svg b/microimputation-dashboard/public/vercel.svg new file mode 100644 index 0000000..7705396 --- /dev/null +++ b/microimputation-dashboard/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/microimputation-dashboard/public/window.svg b/microimputation-dashboard/public/window.svg new file mode 100644 index 0000000..b2b2a44 --- /dev/null +++ b/microimputation-dashboard/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/microimputation-dashboard/tsconfig.json b/microimputation-dashboard/tsconfig.json new file mode 100644 index 0000000..d8b9323 --- /dev/null +++ b/microimputation-dashboard/tsconfig.json @@ -0,0 +1,27 @@ +{ + "compilerOptions": { + "target": "ES2017", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "preserve", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./*"] + } + }, + "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"], + "exclude": ["node_modules"] +} diff --git a/microimputation-dashboard/types/imputation.ts b/microimputation-dashboard/types/imputation.ts new file mode 100644 index 0000000..c6163bc --- /dev/null +++ b/microimputation-dashboard/types/imputation.ts @@ -0,0 +1,25 @@ +// Type definitions for microimputation data +export interface ImputationDataPoint { + // Add fields based on what microimpute outputs + // These are placeholder fields that will be updated based on actual CSV structure + id?: string; + variable?: string; + original_value?: number; + imputed_value?: number; + method?: string; + confidence?: number; + [key: string]: any; // Allow additional fields +} + +export interface ImputationMetrics { + totalRecords: number; + imputedCount: number; + variables: string[]; + methods: string[]; +} + +export interface FileInfo { + filename: string; + loaded: boolean; + data: ImputationDataPoint[]; +} \ No newline at end of file diff --git a/microimputation-dashboard/utils/csvParser.ts b/microimputation-dashboard/utils/csvParser.ts new file mode 100644 index 0000000..726e08c --- /dev/null +++ b/microimputation-dashboard/utils/csvParser.ts @@ -0,0 +1,49 @@ +import Papa from 'papaparse'; +import { ImputationDataPoint, ImputationMetrics } from '@/types/imputation'; + +export function parseImputationCSV(csvContent: string): ImputationDataPoint[] { + const result = Papa.parse(csvContent, { + header: true, + dynamicTyping: true, + skipEmptyLines: true, + }); + + if (result.errors.length > 0) { + console.warn('CSV parsing warnings:', result.errors); + // Continue parsing despite warnings + } + + // Return all parsed data - structure will depend on actual microimpute output + return result.data.filter(row => row && Object.keys(row).length > 0); +} + +export function getImputationMetrics(data: ImputationDataPoint[]): ImputationMetrics { + if (data.length === 0) { + return { + totalRecords: 0, + imputedCount: 0, + variables: [], + methods: [], + }; + } + + // Extract unique variables if present + const variables = data[0].variable + ? [...new Set(data.map(d => d.variable).filter(Boolean) as string[])] + : Object.keys(data[0]).filter(key => key !== 'id'); + + // Extract unique methods if present + const methods = data[0].method + ? [...new Set(data.map(d => d.method).filter(Boolean) as string[])] + : []; + + // Count imputed records (this logic will be updated based on actual data structure) + const imputedCount = data.filter(d => d.imputed_value !== undefined).length; + + return { + totalRecords: data.length, + imputedCount, + variables, + methods, + }; +} \ No newline at end of file diff --git a/microimputation-dashboard/utils/deeplinks.ts b/microimputation-dashboard/utils/deeplinks.ts new file mode 100644 index 0000000..55b7af3 --- /dev/null +++ b/microimputation-dashboard/utils/deeplinks.ts @@ -0,0 +1,110 @@ +// Deeplink utilities for GitHub artifact sharing + +export interface GitHubArtifactInfo { + repo: string; + branch: string; + commit: string; + artifact: string; +} + +export interface DeeplinkParams { + mode?: 'single' | 'comparison'; + primary?: GitHubArtifactInfo; + secondary?: GitHubArtifactInfo; +} + +export function parseDeeplinkParams(searchParams: URLSearchParams): DeeplinkParams | null { + const mode = searchParams.get('mode') || 'single'; + + const primaryRepo = searchParams.get('repo'); + const primaryBranch = searchParams.get('branch'); + const primaryCommit = searchParams.get('commit'); + const primaryArtifact = searchParams.get('artifact'); + + if (!primaryRepo || !primaryBranch || !primaryCommit || !primaryArtifact) { + // Check for comparison mode parameters + const repo1 = searchParams.get('repo1'); + const branch1 = searchParams.get('branch1'); + const commit1 = searchParams.get('commit1'); + const artifact1 = searchParams.get('artifact1'); + + const repo2 = searchParams.get('repo2'); + const branch2 = searchParams.get('branch2'); + const commit2 = searchParams.get('commit2'); + const artifact2 = searchParams.get('artifact2'); + + if (repo1 && branch1 && commit1 && artifact1 && repo2 && branch2 && commit2 && artifact2) { + return { + mode: 'comparison', + primary: { + repo: repo1, + branch: branch1, + commit: commit1, + artifact: artifact1, + }, + secondary: { + repo: repo2, + branch: branch2, + commit: commit2, + artifact: artifact2, + }, + }; + } + + return null; + } + + const params: DeeplinkParams = { + mode: mode as 'single' | 'comparison', + primary: { + repo: primaryRepo, + branch: primaryBranch, + commit: primaryCommit, + artifact: primaryArtifact, + }, + }; + + // Check for secondary parameters for comparison mode + const secondaryRepo = searchParams.get('repo2') || primaryRepo; + const secondaryBranch = searchParams.get('branch2'); + const secondaryCommit = searchParams.get('commit2'); + const secondaryArtifact = searchParams.get('artifact2'); + + if (secondaryBranch && secondaryCommit && secondaryArtifact) { + params.mode = 'comparison'; + params.secondary = { + repo: secondaryRepo, + branch: secondaryBranch, + commit: secondaryCommit, + artifact: secondaryArtifact, + }; + } + + return params; +} + +export function createShareableUrl(baseUrl: string, artifactInfo: GitHubArtifactInfo, secondaryInfo?: GitHubArtifactInfo): string { + const url = new URL(baseUrl); + + if (secondaryInfo) { + // Comparison mode + url.searchParams.set('mode', 'comparison'); + url.searchParams.set('repo1', artifactInfo.repo); + url.searchParams.set('branch1', artifactInfo.branch); + url.searchParams.set('commit1', artifactInfo.commit); + url.searchParams.set('artifact1', artifactInfo.artifact); + url.searchParams.set('repo2', secondaryInfo.repo); + url.searchParams.set('branch2', secondaryInfo.branch); + url.searchParams.set('commit2', secondaryInfo.commit); + url.searchParams.set('artifact2', secondaryInfo.artifact); + } else { + // Single mode + url.searchParams.set('mode', 'single'); + url.searchParams.set('repo', artifactInfo.repo); + url.searchParams.set('branch', artifactInfo.branch); + url.searchParams.set('commit', artifactInfo.commit); + url.searchParams.set('artifact', artifactInfo.artifact); + } + + return url.toString(); +} \ No newline at end of file diff --git a/microimpute/evaluations/predictor_analysis.py b/microimpute/evaluations/predictor_analysis.py index 767aa13..febf6ec 100644 --- a/microimpute/evaluations/predictor_analysis.py +++ b/microimpute/evaluations/predictor_analysis.py @@ -410,9 +410,9 @@ def progressive_predictor_inclusion( Returns: Dictionary containing: - - inclusion_order: List of predictors in order of inclusion - - performance_curve: List of losses at each inclusion step - - predictor_impacts: List of dicts with detailed impact information + - results_df: DataFrame with columns ['step', 'predictor_added', + 'predictors_included', 'avg_quantile_loss', 'avg_log_loss', + 'cumulative_improvement', 'marginal_improvement'] - optimal_subset: Predictors in the best performing subset - optimal_loss: Loss of the optimal subset @@ -423,7 +423,8 @@ def progressive_predictor_inclusion( ... imputed_variables=['health_score'], ... model_class=QRF ... ) - >>> print(f"Optimal order: {results['inclusion_order']}") + >>> print(f"Optimal subset: {results['optimal_subset']}") + >>> print(results['results_df']) """ if max_predictors is None: max_predictors = len(predictors) @@ -433,17 +434,12 @@ def progressive_predictor_inclusion( data, train_size=train_size, random_state=random_state ) - results = { - "inclusion_order": [], - "performance_curve": [], - "predictor_impacts": [], - "optimal_subset": [], - "optimal_loss": float("inf"), - } - + # Track results for DataFrame + df_rows = [] remaining_predictors = predictors.copy() selected_predictors = [] previous_loss = float("inf") + first_loss = None best_overall_loss = float("inf") best_subset = [] @@ -492,18 +488,28 @@ def progressive_predictor_inclusion( selected_predictors.append(best_predictor) remaining_predictors.remove(best_predictor) - improvement = previous_loss - best_step_loss + # Track first loss for cumulative improvement calculation + if first_loss is None: + first_loss = best_step_loss + + # Calculate improvements + marginal_improvement = ( + previous_loss - best_step_loss + if previous_loss != float("inf") + else 0.0 + ) + cumulative_improvement = first_loss - best_step_loss - results["inclusion_order"].append(best_predictor) - results["performance_curve"].append(best_step_loss) - results["predictor_impacts"].append( + # Add row to DataFrame + df_rows.append( { - "predictor": best_predictor, "step": step + 1, - "loss_reduction": improvement, - "cumulative_loss": best_step_loss, - "quantile_loss": best_losses_detail["quantile_loss"], - "log_loss": best_losses_detail.get("log_loss", 0), + "predictor_added": best_predictor, + "predictors_included": selected_predictors.copy(), + "avg_quantile_loss": best_losses_detail["quantile_loss"], + "avg_log_loss": best_losses_detail.get("log_loss", 0), + "cumulative_improvement": cumulative_improvement, + "marginal_improvement": marginal_improvement, } ) @@ -516,16 +522,20 @@ def progressive_predictor_inclusion( log.info( f"Step {step + 1}: Added '{best_predictor}', " - f"loss = {best_step_loss:.6f} (improvement: {improvement:.6f})" + f"loss = {best_step_loss:.6f} (improvement: {marginal_improvement:.6f})" ) else: log.warning(f"No valid predictor found at step {step + 1}") break - results["optimal_subset"] = best_subset - results["optimal_loss"] = best_overall_loss + # Create DataFrame from rows + results_df = pd.DataFrame(df_rows) - return results + return { + "results_df": results_df, + "optimal_subset": best_subset, + "optimal_loss": best_overall_loss, + } # Helper functions diff --git a/microimpute/utils/__init__.py b/microimpute/utils/__init__.py index 7437931..256ae4e 100644 --- a/microimpute/utils/__init__.py +++ b/microimpute/utils/__init__.py @@ -10,6 +10,7 @@ - nnd_hotdeck_using_rpy2: R-based nearest neighbor hot deck imputation (optional) """ +from microimpute.utils.dashboard_formatter import format_csv from microimpute.utils.data import preprocess_data, unnormalize_predictions from microimpute.utils.type_handling import VariableTypeDetector diff --git a/microimpute/utils/dashboard_formatter.py b/microimpute/utils/dashboard_formatter.py new file mode 100644 index 0000000..a5602fa --- /dev/null +++ b/microimpute/utils/dashboard_formatter.py @@ -0,0 +1,380 @@ +""" +Utility function to format various imputation outputs into a unified CSV format for dashboard visualization. +""" + +import json +from typing import Any, Dict, List, Optional, Union + +import pandas as pd + + +def format_csv( + output_path: Optional[str] = None, + autoimpute_result: Optional[Dict] = None, + comparison_metrics_df: Optional[pd.DataFrame] = None, + distribution_comparison_df: Optional[pd.DataFrame] = None, + predictor_correlations: Optional[Dict[str, pd.DataFrame]] = None, + predictor_importance_df: Optional[pd.DataFrame] = None, + progressive_inclusion_df: Optional[pd.DataFrame] = None, + best_method_name: Optional[str] = None, +) -> pd.DataFrame: + """ + Format various imputation outputs into a unified long-format CSV for dashboard visualization. + + Parameters + ---------- + output_path : str + Path to save the formatted CSV file. + + autoimpute_result : Dict, optional + Result from autoimpute containing cv_results with benchmark losses. + Expected structure: {'cv_results': {method: {'quantile_loss': {...}, 'log_loss': {...}}}} + + comparison_metrics_df : pd.DataFrame, optional + DataFrame from compare_metrics() with columns: + ['Method', 'Imputed Variable', 'Percentile', 'Loss', 'Metric'] + + distribution_comparison_df : pd.DataFrame, optional + DataFrame from compare_distributions() with columns: + ['Variable', 'Metric', 'Distance'] + + predictor_correlations : Dict[str, pd.DataFrame], optional + Dict from compute_predictor_correlations() with keys like 'pearson', 'spearman', 'mutual_info' + and correlation matrices as values. Also can include 'predictor_target_mi'. + + predictor_importance_df : pd.DataFrame, optional + DataFrame from leave_one_out_analysis() with columns: + ['predictor_removed', 'avg_quantile_loss', 'avg_log_loss', 'loss_increase', 'relative_impact'] + + progressive_inclusion_df : pd.DataFrame, optional + DataFrame from progressive_predictor_inclusion()['results_df'] with columns: + ['step', 'predictor_added', 'predictors_included', 'avg_quantile_loss', + 'avg_log_loss', 'cumulative_improvement', 'marginal_improvement'] + + best_method_name : str, optional + Name of the best method to append "_best_method" suffix to. + + Returns + ------- + pd.DataFrame + Unified long-format DataFrame with columns: + ['type', 'method', 'variable', 'quantile', 'metric_name', 'metric_value', 'split', 'additional_info'] + """ + rows = [] + + # 1. Process autoimpute benchmark losses from cv_results + if autoimpute_result and "cv_results" in autoimpute_result: + for method, cv_result in autoimpute_result["cv_results"].items(): + # Append "_best_method" if this is the best method + method_label = ( + f"{method}_best_method" + if method == best_method_name + else method + ) + + for metric_type in ["quantile_loss", "log_loss"]: + if metric_type in cv_result: + data = cv_result[metric_type] + results_df = data.get("results") + variables = data.get("variables", []) + + if results_df is not None: + # Add individual quantile results + for split in ["train", "test"]: + if split in results_df.index: + for quantile in results_df.columns: + # Add mean_all row for this quantile + rows.append( + { + "type": "benchmark_loss", + "method": method_label, + "variable": f"{metric_type}_mean_all", + "quantile": float(quantile), + "metric_name": metric_type, + "metric_value": results_df.loc[ + split, quantile + ], + "split": split, + "additional_info": json.dumps( + {"n_variables": len(variables)} + ), + } + ) + + # Add mean across all quantiles + if "mean_train" in data: + rows.append( + { + "type": "benchmark_loss", + "method": method_label, + "variable": f"{metric_type}_mean_all", + "quantile": "mean", + "metric_name": metric_type, + "metric_value": data["mean_train"], + "split": "train", + "additional_info": json.dumps( + {"n_variables": len(variables)} + ), + } + ) + + if "mean_test" in data: + rows.append( + { + "type": "benchmark_loss", + "method": method_label, + "variable": f"{metric_type}_mean_all", + "quantile": "mean", + "metric_name": metric_type, + "metric_value": data["mean_test"], + "split": "test", + "additional_info": json.dumps( + {"n_variables": len(variables)} + ), + } + ) + + # 2. Process comparison metrics (per-variable benchmark losses) + if comparison_metrics_df is not None and not comparison_metrics_df.empty: + for _, row in comparison_metrics_df.iterrows(): + method = row["Method"] + method_label = ( + f"{method}_best_method" + if method == best_method_name + else method + ) + + # Handle variable naming - check if it's an aggregate + variable = row["Imputed Variable"] + if "mean_quantile_loss" in variable: + variable = "quantile_loss_mean_all" + elif "mean_log_loss" in variable: + variable = "log_loss_mean_all" + + # Handle percentile - can be a number or "mean_loss" + percentile = row["Percentile"] + if percentile == "mean_loss": + quantile = "mean" + else: + quantile = float(percentile) if pd.notna(percentile) else "N/A" + + rows.append( + { + "type": "benchmark_loss", + "method": method_label, + "variable": variable, + "quantile": quantile, + "metric_name": row["Metric"], + "metric_value": row["Loss"], + "split": "test", # Comparison metrics are typically on test set + "additional_info": "{}", + } + ) + + # 3. Process distribution comparison metrics + if ( + distribution_comparison_df is not None + and not distribution_comparison_df.empty + ): + for _, row in distribution_comparison_df.iterrows(): + rows.append( + { + "type": "distribution_distance", + "method": best_method_name if best_method_name else "N/A", + "variable": row["Variable"], + "quantile": "N/A", + "metric_name": row["Metric"] + .lower() + .replace(" ", "_"), # e.g., "wasserstein_distance" + "metric_value": row["Distance"], + "split": "full", + "additional_info": "{}", + } + ) + + # 4. Process predictor correlations + if predictor_correlations: + # Handle correlation matrices (pearson, spearman, mutual_info between predictors) + for corr_type in ["pearson", "spearman", "mutual_info"]: + if corr_type in predictor_correlations: + corr_matrix = predictor_correlations[corr_type] + # Extract upper triangle (excluding diagonal) + for i in range(len(corr_matrix.index)): + for j in range(i + 1, len(corr_matrix.columns)): + pred1 = corr_matrix.index[i] + pred2 = corr_matrix.columns[j] + rows.append( + { + "type": "predictor_correlation", + "method": "N/A", + "variable": pred1, + "quantile": "N/A", + "metric_name": corr_type, + "metric_value": corr_matrix.iloc[i, j], + "split": "full", + "additional_info": json.dumps( + {"predictor2": pred2} + ), + } + ) + + # Handle predictor-target MI + if "predictor_target_mi" in predictor_correlations: + mi_df = predictor_correlations["predictor_target_mi"] + for predictor in mi_df.index: + for target in mi_df.columns: + rows.append( + { + "type": "predictor_target_mi", + "method": "N/A", + "variable": predictor, + "quantile": "N/A", + "metric_name": "mutual_info", + "metric_value": mi_df.loc[predictor, target], + "split": "full", + "additional_info": json.dumps({"target": target}), + } + ) + + # 5. Process predictor importance (leave-one-out) + if ( + predictor_importance_df is not None + and not predictor_importance_df.empty + ): + for _, row in predictor_importance_df.iterrows(): + predictor = row["predictor_removed"] + + # Add relative impact + rows.append( + { + "type": "predictor_importance", + "method": best_method_name if best_method_name else "N/A", + "variable": predictor, + "quantile": "N/A", + "metric_name": "relative_impact", + "metric_value": row["relative_impact"], + "split": "test", + "additional_info": json.dumps( + {"removed_predictor": predictor} + ), + } + ) + + # Add loss increase + rows.append( + { + "type": "predictor_importance", + "method": best_method_name if best_method_name else "N/A", + "variable": predictor, + "quantile": "N/A", + "metric_name": "loss_increase", + "metric_value": row["loss_increase"], + "split": "test", + "additional_info": json.dumps( + {"removed_predictor": predictor} + ), + } + ) + + # 6. Process progressive predictor inclusion + if ( + progressive_inclusion_df is not None + and not progressive_inclusion_df.empty + ): + for _, row in progressive_inclusion_df.iterrows(): + step = row["step"] + predictor_added = row["predictor_added"] + predictors_included = row["predictors_included"] + + # Add cumulative improvement + if "cumulative_improvement" in row and pd.notna( + row["cumulative_improvement"] + ): + rows.append( + { + "type": "progressive_inclusion", + "method": ( + best_method_name if best_method_name else "N/A" + ), + "variable": "N/A", + "quantile": "N/A", + "metric_name": "cumulative_improvement", + "metric_value": row["cumulative_improvement"], + "split": "test", + "additional_info": json.dumps( + { + "step": int(step), + "predictor_added": predictor_added, + "predictors": ( + predictors_included + if isinstance(predictors_included, list) + else [predictors_included] + ), + } + ), + } + ) + + # Add marginal improvement + if "marginal_improvement" in row and pd.notna( + row["marginal_improvement"] + ): + rows.append( + { + "type": "progressive_inclusion", + "method": ( + best_method_name if best_method_name else "N/A" + ), + "variable": "N/A", + "quantile": "N/A", + "metric_name": "marginal_improvement", + "metric_value": row["marginal_improvement"], + "split": "test", + "additional_info": json.dumps( + { + "step": int(step), + "predictor_added": predictor_added, + } + ), + } + ) + + # Create DataFrame from rows + if not rows: + # Return empty DataFrame with correct columns if no data + return pd.DataFrame( + columns=[ + "type", + "method", + "variable", + "quantile", + "metric_name", + "metric_value", + "split", + "additional_info", + ] + ) + + df = pd.DataFrame(rows) + + # Ensure correct data types + df["metric_value"] = pd.to_numeric(df["metric_value"], errors="coerce") + + # Convert quantile to numeric where possible (keep 'mean' and 'N/A' as strings) + def convert_quantile(q): + if isinstance(q, (int, float)): + return float(q) + elif q in ["mean", "N/A"]: + return q + else: + try: + return float(q) + except: + return q + + df["quantile"] = df["quantile"].apply(convert_quantile) + + if output_path: + df.to_csv(output_path, index=False) + + return df diff --git a/tests/test_dashboard_formatter.py b/tests/test_dashboard_formatter.py new file mode 100644 index 0000000..41bf210 --- /dev/null +++ b/tests/test_dashboard_formatter.py @@ -0,0 +1,700 @@ +"""Comprehensive tests for dashboard_formatter module. + +This module tests the format_csv and save_formatted_csv functions +to ensure correct data formatting for dashboard visualization. +""" + +import json +import tempfile +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from microimpute.utils.dashboard_formatter import format_csv + + +# Valid type values that should appear in the output +VALID_TYPES = { + "benchmark_loss", + "distribution_distance", + "predictor_correlation", + "predictor_target_mi", + "predictor_importance", + "progressive_inclusion", +} + +# Expected columns in the output DataFrame +EXPECTED_COLUMNS = [ + "type", + "method", + "variable", + "quantile", + "metric_name", + "metric_value", + "split", + "additional_info", +] + + +@pytest.fixture +def sample_autoimpute_result(): + """Create sample autoimpute result for testing.""" + return { + "cv_results": { + "OLS": { + "quantile_loss": { + "results": pd.DataFrame( + { + 0.1: [0.01, 0.02], + 0.5: [0.015, 0.025], + 0.9: [0.02, 0.03], + }, + index=["train", "test"], + ), + "mean_train": 0.015, + "mean_test": 0.025, + "variables": ["var1", "var2"], + }, + "log_loss": { + "results": pd.DataFrame( + { + 0.5: [0.1, 0.15], + }, + index=["train", "test"], + ), + "mean_train": 0.1, + "mean_test": 0.15, + "variables": ["cat_var"], + }, + }, + "QRF": { + "quantile_loss": { + "results": pd.DataFrame( + { + 0.1: [0.012, 0.022], + 0.5: [0.017, 0.027], + 0.9: [0.022, 0.032], + }, + index=["train", "test"], + ), + "mean_train": 0.017, + "mean_test": 0.027, + "variables": ["var1", "var2"], + }, + }, + } + } + + +@pytest.fixture +def sample_distribution_comparison(): + """Create sample distribution comparison DataFrame.""" + return pd.DataFrame( + { + "Variable": ["var1", "var2"], + "Metric": ["wasserstein_distance", "wasserstein_distance"], + "Distance": [0.05, 0.03], + } + ) + + +@pytest.fixture +def sample_predictor_correlations(): + """Create sample predictor correlations dictionary.""" + predictors = ["pred1", "pred2", "pred3"] + targets = ["target1", "target2"] + + return { + "pearson": pd.DataFrame( + [[1.0, 0.7, 0.3], [0.7, 1.0, 0.4], [0.3, 0.4, 1.0]], + index=predictors, + columns=predictors, + ), + "spearman": pd.DataFrame( + [[1.0, 0.65, 0.25], [0.65, 1.0, 0.35], [0.25, 0.35, 1.0]], + index=predictors, + columns=predictors, + ), + "mutual_info": pd.DataFrame( + [[1.0, 0.6, 0.2], [0.6, 1.0, 0.3], [0.2, 0.3, 1.0]], + index=predictors, + columns=predictors, + ), + "predictor_target_mi": pd.DataFrame( + [[0.5, 0.3], [0.6, 0.4], [0.2, 0.1]], + index=predictors, + columns=targets, + ), + } + + +@pytest.fixture +def sample_predictor_importance(): + """Create sample predictor importance DataFrame.""" + return pd.DataFrame( + { + "predictor_removed": ["pred1", "pred2", "pred3"], + "avg_quantile_loss": [0.02, 0.025, 0.018], + "avg_log_loss": [0.1, 0.12, 0.08], + "loss_increase": [0.005, 0.01, 0.003], + "relative_impact": [5.0, 10.0, 3.0], + } + ) + + +@pytest.fixture +def sample_progressive_inclusion(): + """Create sample progressive inclusion DataFrame.""" + return pd.DataFrame( + { + "step": [1, 2, 3], + "predictor_added": ["pred2", "pred1", "pred3"], + "predictors_included": [ + ["pred2"], + ["pred2", "pred1"], + ["pred2", "pred1", "pred3"], + ], + "avg_quantile_loss": [0.025, 0.018, 0.015], + "avg_log_loss": [0.12, 0.09, 0.08], + "cumulative_improvement": [0.0, 0.007, 0.010], + "marginal_improvement": [0.0, 0.007, 0.003], + } + ) + + +class TestFormatCSVBasic: + """Basic tests for format_csv function.""" + + def test_empty_inputs_returns_empty_dataframe(self): + """Test that empty inputs return an empty DataFrame with correct columns.""" + result = format_csv() + + assert isinstance(result, pd.DataFrame) + assert list(result.columns) == EXPECTED_COLUMNS + assert len(result) == 0 + + def test_output_has_correct_columns(self, sample_autoimpute_result): + """Test that output has all expected columns.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + autoimpute_result=sample_autoimpute_result, + ) + + assert list(result.columns) == EXPECTED_COLUMNS + finally: + Path(output_path).unlink() + + def test_all_types_are_valid( + self, + sample_autoimpute_result, + sample_distribution_comparison, + sample_predictor_correlations, + sample_predictor_importance, + sample_progressive_inclusion, + ): + """Test that all type values are from the valid set.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + autoimpute_result=sample_autoimpute_result, + distribution_comparison_df=sample_distribution_comparison, + predictor_correlations=sample_predictor_correlations, + predictor_importance_df=sample_predictor_importance, + progressive_inclusion_df=sample_progressive_inclusion, + best_method_name="OLS", + ) + + unique_types = set(result["type"].unique()) + assert unique_types.issubset( + VALID_TYPES + ), f"Invalid types found: {unique_types - VALID_TYPES}" + assert len(unique_types) > 0 + finally: + Path(output_path).unlink() + + def test_metric_values_are_numeric( + self, sample_autoimpute_result, sample_distribution_comparison + ): + """Test that metric_value column contains numeric values.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + autoimpute_result=sample_autoimpute_result, + distribution_comparison_df=sample_distribution_comparison, + ) + + assert pd.api.types.is_numeric_dtype(result["metric_value"]) + assert not result["metric_value"].isna().any() + finally: + Path(output_path).unlink() + + def test_additional_info_is_valid_json( + self, sample_autoimpute_result, sample_progressive_inclusion + ): + """Test that additional_info contains valid JSON.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + autoimpute_result=sample_autoimpute_result, + progressive_inclusion_df=sample_progressive_inclusion, + ) + + # Try to parse each additional_info value as JSON + for info in result["additional_info"]: + try: + parsed = json.loads(info) + assert isinstance(parsed, dict) + except json.JSONDecodeError: + pytest.fail(f"Invalid JSON in additional_info: {info}") + finally: + Path(output_path).unlink() + + +class TestFormatCSVBenchmarkLoss: + """Tests for benchmark_loss type formatting.""" + + def test_benchmark_loss_from_autoimpute(self, sample_autoimpute_result): + """Test benchmark loss formatting from autoimpute results.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + autoimpute_result=sample_autoimpute_result, + ) + + benchmark_rows = result[result["type"] == "benchmark_loss"] + assert len(benchmark_rows) > 0 + + # Check that both methods are present + methods = benchmark_rows["method"].unique() + assert "OLS" in methods + assert "QRF" in methods + + # Check that split values are valid + splits = benchmark_rows["split"].unique() + assert all(split in ["train", "test"] for split in splits) + + # Check quantile values + quantiles = benchmark_rows["quantile"].unique() + assert "mean" in quantiles # Mean should be present + numeric_quantiles = [ + q for q in quantiles if isinstance(q, (int, float)) + ] + assert all( + 0 <= q <= 1 for q in numeric_quantiles + ) # Valid quantile range + finally: + Path(output_path).unlink() + + def test_best_method_marked_correctly(self, sample_autoimpute_result): + """Test that best method has correct suffix.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + autoimpute_result=sample_autoimpute_result, + best_method_name="OLS", + ) + + benchmark_rows = result[result["type"] == "benchmark_loss"] + + # Check that OLS has _best_method suffix + ols_rows = benchmark_rows[ + benchmark_rows["method"].str.contains("OLS") + ] + assert all( + "_best_method" in method for method in ols_rows["method"] + ) + + # Check that QRF does not have the suffix + qrf_rows = benchmark_rows[ + benchmark_rows["method"].str.contains("QRF") + ] + assert all( + "_best_method" not in method for method in qrf_rows["method"] + ) + finally: + Path(output_path).unlink() + + +class TestFormatCSVDistributionDistance: + """Tests for distribution_distance type formatting.""" + + def test_distribution_distance_formatting( + self, sample_distribution_comparison + ): + """Test distribution distance formatting.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + distribution_comparison_df=sample_distribution_comparison, + best_method_name="OLS", + ) + + dist_rows = result[result["type"] == "distribution_distance"] + assert len(dist_rows) == 2 # Two variables + + # Check variables are present + assert set(dist_rows["variable"]) == {"var1", "var2"} + + # Check metric name is formatted correctly + assert all( + metric == "wasserstein_distance" + for metric in dist_rows["metric_name"] + ) + + # Check split is 'full' + assert all(split == "full" for split in dist_rows["split"]) + + # Check quantile is 'N/A' + assert all(q == "N/A" for q in dist_rows["quantile"]) + finally: + Path(output_path).unlink() + + +class TestFormatCSVPredictorCorrelations: + """Tests for predictor_correlation and predictor_target_mi types.""" + + def test_predictor_correlation_formatting( + self, sample_predictor_correlations + ): + """Test predictor correlation formatting.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + predictor_correlations=sample_predictor_correlations, + ) + + corr_rows = result[result["type"] == "predictor_correlation"] + + # Check that correlation types are present + metric_names = set(corr_rows["metric_name"]) + assert "pearson" in metric_names + assert "spearman" in metric_names + assert "mutual_info" in metric_names + + # Check that additional_info contains predictor2 + for info in corr_rows["additional_info"]: + parsed = json.loads(info) + assert "predictor2" in parsed + + # Check that only upper triangle is included (no duplicates) + # For 3 predictors, should have 3 pairs: (1,2), (1,3), (2,3) + # Times 3 correlation types = 9 rows + assert len(corr_rows) == 9 + finally: + Path(output_path).unlink() + + def test_predictor_target_mi_formatting( + self, sample_predictor_correlations + ): + """Test predictor-target MI formatting.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + predictor_correlations=sample_predictor_correlations, + ) + + mi_rows = result[result["type"] == "predictor_target_mi"] + + # Should have 3 predictors Γ— 2 targets = 6 rows + assert len(mi_rows) == 6 + + # Check metric name + assert all( + metric == "mutual_info" for metric in mi_rows["metric_name"] + ) + + # Check additional_info contains target + for info in mi_rows["additional_info"]: + parsed = json.loads(info) + assert "target" in parsed + assert parsed["target"] in ["target1", "target2"] + finally: + Path(output_path).unlink() + + +class TestFormatCSVPredictorImportance: + """Tests for predictor_importance type formatting.""" + + def test_predictor_importance_formatting( + self, sample_predictor_importance + ): + """Test predictor importance formatting.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + predictor_importance_df=sample_predictor_importance, + best_method_name="OLS", + ) + + imp_rows = result[result["type"] == "predictor_importance"] + + # Should have 3 predictors Γ— 2 metrics = 6 rows + assert len(imp_rows) == 6 + + # Check metric names + metric_names = set(imp_rows["metric_name"]) + assert metric_names == {"relative_impact", "loss_increase"} + + # Check that all predictors are present + variables = imp_rows["variable"].unique() + assert set(variables) == {"pred1", "pred2", "pred3"} + + # Check split is 'test' + assert all(split == "test" for split in imp_rows["split"]) + finally: + Path(output_path).unlink() + + +class TestFormatCSVProgressiveInclusion: + """Tests for progressive_inclusion type formatting.""" + + def test_progressive_inclusion_formatting( + self, sample_progressive_inclusion + ): + """Test progressive inclusion formatting.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + progressive_inclusion_df=sample_progressive_inclusion, + best_method_name="OLS", + ) + + prog_rows = result[result["type"] == "progressive_inclusion"] + + # Should have 3 steps Γ— 2 metrics = 6 rows + assert len(prog_rows) == 6 + + # Check metric names + metric_names = set(prog_rows["metric_name"]) + assert metric_names == { + "cumulative_improvement", + "marginal_improvement", + } + + # Check additional_info contains step and predictor_added + for info in prog_rows["additional_info"]: + parsed = json.loads(info) + assert "step" in parsed + assert "predictor_added" in parsed + assert parsed["step"] in [1, 2, 3] + assert parsed["predictor_added"] in ["pred1", "pred2", "pred3"] + + # Check that steps are in order for cumulative improvement + cumulative_rows = prog_rows[ + prog_rows["metric_name"] == "cumulative_improvement" + ] + steps = [ + json.loads(info)["step"] + for info in cumulative_rows["additional_info"] + ] + assert steps == sorted(steps) # Should be in ascending order + finally: + Path(output_path).unlink() + + def test_progressive_inclusion_step_ordering( + self, sample_progressive_inclusion + ): + """Test that steps are correctly numbered and ordered.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + progressive_inclusion_df=sample_progressive_inclusion, + ) + + prog_rows = result[result["type"] == "progressive_inclusion"] + + # Extract steps from additional_info + steps = [] + for info in prog_rows["additional_info"]: + parsed = json.loads(info) + steps.append(parsed["step"]) + + # Steps should be 1, 2, 3 (repeated for each metric) + unique_steps = sorted(set(steps)) + assert unique_steps == [1, 2, 3] + finally: + Path(output_path).unlink() + + +class TestSaveFormattedCSV: + """Tests for CSV file saving via format_csv.""" + + def test_format_csv_creates_file(self, sample_autoimpute_result): + """Test that format_csv creates a file when output_path is provided.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_path = Path(tmpdir) / "test_output.csv" + + df = format_csv( + output_path=str(output_path), + autoimpute_result=sample_autoimpute_result, + ) + + # File should be created + assert output_path.exists() + + # Verify content can be read back + loaded_df = pd.read_csv(output_path) + assert len(loaded_df) == len(df) + assert list(loaded_df.columns) == EXPECTED_COLUMNS + + def test_saved_csv_preserves_data(self, sample_autoimpute_result): + """Test that saved CSV preserves data correctly.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_path = Path(tmpdir) / "test_output.csv" + + df = format_csv( + output_path=str(output_path), + autoimpute_result=sample_autoimpute_result, + ) + + # Read back and compare + loaded_df = pd.read_csv(output_path) + + # Check that numeric values are preserved + assert np.allclose( + df["metric_value"].values, + loaded_df["metric_value"].values, + rtol=1e-10, + ) + + # Check that string columns are preserved + assert list(df["type"]) == list(loaded_df["type"]) + assert list(df["method"]) == list(loaded_df["method"]) + + +class TestEdgeCases: + """Test edge cases and error handling.""" + + def test_none_values_handled_correctly(self): + """Test that None values for optional parameters work.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + autoimpute_result=None, + comparison_metrics_df=None, + distribution_comparison_df=None, + predictor_correlations=None, + predictor_importance_df=None, + progressive_inclusion_df=None, + best_method_name=None, + ) + + assert isinstance(result, pd.DataFrame) + assert len(result) == 0 + finally: + Path(output_path).unlink() + + def test_empty_dataframes_handled_correctly(self): + """Test that empty DataFrames are handled gracefully.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + distribution_comparison_df=pd.DataFrame(), + predictor_importance_df=pd.DataFrame(), + progressive_inclusion_df=pd.DataFrame(), + ) + + assert isinstance(result, pd.DataFrame) + # Should have correct columns even with empty input + assert list(result.columns) == EXPECTED_COLUMNS + finally: + Path(output_path).unlink() + + def test_mixed_quantile_types(self, sample_autoimpute_result): + """Test that mixed quantile types (numeric and string) are handled.""" + with tempfile.NamedTemporaryFile( + mode="w", delete=False, suffix=".csv" + ) as f: + output_path = f.name + + try: + result = format_csv( + output_path=output_path, + autoimpute_result=sample_autoimpute_result, + ) + + # Check that we have both numeric quantiles and 'mean' + quantiles = result["quantile"].unique() + has_numeric = any( + isinstance(q, (int, float)) and not pd.isna(q) + for q in quantiles + ) + has_mean = "mean" in quantiles + + assert has_numeric + assert has_mean + finally: + Path(output_path).unlink() diff --git a/tests/test_predictor_analysis.py b/tests/test_predictor_analysis.py index 7b813e3..2c167da 100644 --- a/tests/test_predictor_analysis.py +++ b/tests/test_predictor_analysis.py @@ -392,26 +392,44 @@ def test_basic_progressive_inclusion(self, sample_regression_data): # Check output structure assert isinstance(results, dict) - assert "inclusion_order" in results - assert "performance_curve" in results - assert "predictor_impacts" in results + assert "results_df" in results assert "optimal_subset" in results assert "optimal_loss" in results - # Check inclusion order - assert len(results["inclusion_order"]) <= 3 - assert all(pred in predictors for pred in results["inclusion_order"]) + # Check results_df is a DataFrame + results_df = results["results_df"] + assert isinstance(results_df, pd.DataFrame) - # Check performance curve is monotonically decreasing (or flat) - perf_curve = results["performance_curve"] - for i in range(1, len(perf_curve)): - assert ( - perf_curve[i] <= perf_curve[i - 1] + 1e-6 - ) # Allow small numerical errors + # Check DataFrame has correct columns + expected_columns = [ + "step", + "predictor_added", + "predictors_included", + "avg_quantile_loss", + "avg_log_loss", + "cumulative_improvement", + "marginal_improvement", + ] + assert list(results_df.columns) == expected_columns + + # Check inclusion order from DataFrame + assert len(results_df) <= 3 + assert all( + pred in predictors for pred in results_df["predictor_added"] + ) + + # Check step numbering is correct + assert list(results_df["step"]) == list(range(1, len(results_df) + 1)) + + # Check performance is monotonically improving + # (cumulative_improvement should be non-decreasing) + cumulative_impr = results_df["cumulative_improvement"].values + for i in range(1, len(cumulative_impr)): + assert cumulative_impr[i] >= cumulative_impr[i - 1] - 1e-6 # Check optimal subset assert len(results["optimal_subset"]) <= 3 - assert results["optimal_loss"] == min(results["performance_curve"]) + assert results["optimal_loss"] > 0 # Should be a positive loss value def test_progressive_inclusion_all_predictors( self, sample_regression_data @@ -431,8 +449,13 @@ def test_progressive_inclusion_all_predictors( ) # Should include all predictors - assert len(results["inclusion_order"]) == len(predictors) - assert set(results["inclusion_order"]) == set(predictors) + results_df = results["results_df"] + assert len(results_df) == len(predictors) + assert set(results_df["predictor_added"]) == set(predictors) + + # Check that predictors_included grows correctly + for i, row in results_df.iterrows(): + assert len(row["predictors_included"]) == row["step"] def test_progressive_inclusion_with_mixed_types(self, sample_mixed_data): """Test progressive inclusion with mixed predictor types.""" @@ -450,17 +473,15 @@ def test_progressive_inclusion_with_mixed_types(self, sample_mixed_data): ) assert isinstance(results, dict) - assert len(results["inclusion_order"]) <= 3 - - # Check that predictor impacts are recorded - assert len(results["predictor_impacts"]) == len( - results["inclusion_order"] + results_df = results["results_df"] + assert len(results_df) <= 3 + + # Check that values are valid + assert (results_df["avg_quantile_loss"] >= 0).all() + assert (results_df["avg_log_loss"] >= 0).all() + assert results_df["step"].tolist() == list( + range(1, len(results_df) + 1) ) - for impact in results["predictor_impacts"]: - assert "predictor" in impact - assert "step" in impact - assert "loss_reduction" in impact - assert "cumulative_loss" in impact class TestIntegration: @@ -502,7 +523,7 @@ def test_full_predictor_analysis_workflow(self, sample_mixed_data): max_predictors=3, random_state=42, ) - assert len(prog_results["inclusion_order"]) <= 3 + assert len(prog_results["results_df"]) <= 3 # Check consistency: highly correlated predictors should have similar importance high_corr_pairs = []