diff --git a/.claude/skills/pdf/SKILL.md b/.claude/skills/pdf/SKILL.md new file mode 100644 index 0000000..5e341c9 --- /dev/null +++ b/.claude/skills/pdf/SKILL.md @@ -0,0 +1,223 @@ +--- +name: pdf +description: Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill. +--- + +# PDF Processing + +## Quick Start + +```python +from pypdf import PdfReader, PdfWriter + +reader = PdfReader("document.pdf") +print(f"Pages: {len(reader.pages)}") + +text = "" +for page in reader.pages: + text += page.extract_text() +``` + +## Python Libraries + +### pypdf — Basic Operations + +**Merge PDFs:** +```python +from pypdf import PdfWriter, PdfReader + +writer = PdfWriter() +for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]: + reader = PdfReader(pdf_file) + for page in reader.pages: + writer.add_page(page) + +with open("merged.pdf", "wb") as output: + writer.write(output) +``` + +**Split PDF:** +```python +reader = PdfReader("input.pdf") +for i, page in enumerate(reader.pages): + writer = PdfWriter() + writer.add_page(page) + with open(f"page_{i+1}.pdf", "wb") as output: + writer.write(output) +``` + +**Extract Metadata:** +```python +reader = PdfReader("document.pdf") +meta = reader.metadata +print(f"Title: {meta.title}") +print(f"Author: {meta.author}") +``` + +**Rotate Pages:** +```python +reader = PdfReader("input.pdf") +writer = PdfWriter() +page = reader.pages[0] +page.rotate(90) +writer.add_page(page) +with open("rotated.pdf", "wb") as output: + writer.write(output) +``` + +### pdfplumber — Text and Table Extraction + +**Extract Text with Layout:** +```python +import pdfplumber + +with pdfplumber.open("document.pdf") as pdf: + for page in pdf.pages: + text = page.extract_text() + print(text) +``` + +**Extract Tables:** +```python +with pdfplumber.open("document.pdf") as pdf: + for i, page in enumerate(pdf.pages): + tables = page.extract_tables() + for j, table in enumerate(tables): + print(f"Table {j+1} on page {i+1}:") + for row in table: + print(row) +``` + +**Extract Tables to DataFrame:** +```python +import pandas as pd +import pdfplumber + +with pdfplumber.open("document.pdf") as pdf: + all_tables = [] + for page in pdf.pages: + tables = page.extract_tables() + for table in tables: + if table: + df = pd.DataFrame(table[1:], columns=table[0]) + all_tables.append(df) + +if all_tables: + combined_df = pd.concat(all_tables, ignore_index=True) + combined_df.to_excel("extracted_tables.xlsx", index=False) +``` + +### reportlab — Create PDFs + +**Basic PDF Creation:** +```python +from reportlab.lib.pagesizes import letter +from reportlab.pdfgen import canvas + +c = canvas.Canvas("hello.pdf", pagesize=letter) +width, height = letter +c.drawString(100, height - 100, "Hello World!") +c.save() +``` + +**Multi-page PDF:** +```python +from reportlab.lib.pagesizes import letter +from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak +from reportlab.lib.styles import getSampleStyleSheet + +doc = SimpleDocTemplate("report.pdf", pagesize=letter) +styles = getSampleStyleSheet() +story = [] + +story.append(Paragraph("Report Title", styles['Title'])) +story.append(Spacer(1, 12)) +story.append(Paragraph("Body content here.", styles['Normal'])) +story.append(PageBreak()) +story.append(Paragraph("Page 2", styles['Heading1'])) +doc.build(story) +``` + +⚠️ **IMPORTANT**: Never use Unicode subscript/superscript characters (₀₁₂₃, ⁰¹²³) in ReportLab — use `` and `` tags instead. + +## Command-Line Tools + +**pdftotext (poppler-utils):** +```bash +pdftotext input.pdf output.txt +pdftotext -layout input.pdf output.txt # preserve layout +pdftotext -f 1 -l 5 input.pdf output.txt # pages 1-5 +``` + +**qpdf:** +```bash +qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf +qpdf input.pdf --pages . 1-5 -- pages1-5.pdf +qpdf input.pdf output.pdf --rotate=+90:1 +qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf +``` + +## Common Tasks + +**OCR Scanned PDFs:** +```python +import pytesseract +from pdf2image import convert_from_path + +images = convert_from_path('scanned.pdf') +text = "" +for i, image in enumerate(images): + text += f"Page {i+1}:\n" + text += pytesseract.image_to_string(image) + text += "\n\n" +``` + +**Add Watermark:** +```python +from pypdf import PdfReader, PdfWriter + +watermark = PdfReader("watermark.pdf").pages[0] +reader = PdfReader("document.pdf") +writer = PdfWriter() +for page in reader.pages: + page.merge_page(watermark) + writer.add_page(page) +with open("watermarked.pdf", "wb") as output: + writer.write(output) +``` + +**Password Protection:** +```python +from pypdf import PdfReader, PdfWriter + +reader = PdfReader("input.pdf") +writer = PdfWriter() +for page in reader.pages: + writer.add_page(page) +writer.encrypt("userpassword", "ownerpassword") +with open("encrypted.pdf", "wb") as output: + writer.write(output) +``` + +**Extract Images (CLI):** +```bash +pdfimages -j input.pdf output_prefix +``` + +## Quick Reference + +| Task | Best Tool | +|------|-----------| +| Merge PDFs | pypdf | +| Split PDFs | pypdf | +| Extract text | pdfplumber | +| Extract tables | pdfplumber | +| Create PDFs | reportlab | +| CLI merge | qpdf | +| OCR scanned PDFs | pytesseract + pdf2image | +| Fill PDF forms | scripts/ (see forms.md) | + +## References + +- **Form filling**: See [references/forms.md](references/forms.md) — use bundled scripts in `scripts/` +- **Advanced usage** (pypdfium2, pdf-lib JS, batch processing, troubleshooting): See [references/reference.md](references/reference.md) diff --git a/.claude/skills/pdf/references/forms.md b/.claude/skills/pdf/references/forms.md new file mode 100644 index 0000000..d2612b0 --- /dev/null +++ b/.claude/skills/pdf/references/forms.md @@ -0,0 +1,104 @@ +# PDF Form Filling + +## Initial Assessment + +First check if the PDF has fillable form fields: +```bash +python scripts/check_fillable_fields.py file.pdf +``` + +--- + +## For Fillable Forms + +**Step 1 — Extract field info:** +```bash +python scripts/extract_form_field_info.py input.pdf fields.json +``` +Outputs JSON cataloging all fields (IDs, locations, types: text/checkbox/radio/choice). + +**Step 2 — Visually verify (optional):** +```bash +mkdir pages && python scripts/convert_pdf_to_images.py input.pdf pages/ +``` + +**Step 3 — Create field values JSON:** +```json +[ + {"field_id": "FirstName", "page": 1, "value": "John"}, + {"field_id": "Agree", "page": 1, "value": "/Yes"}, + {"field_id": "Gender", "page": 1, "value": "/Male"} +] +``` +- Checkboxes: use the `checked_value` shown in the field info JSON +- Radio groups: use one of the `radio_options[].value` values +- Choice fields: use one of the `choice_options[].value` values + +**Step 4 — Fill the form:** +```bash +python scripts/fill_fillable_fields.py input.pdf field_values.json output.pdf +``` + +--- + +## For Non-Fillable Forms (Text Annotations) + +### Approach A — Preferred (digitally-created PDFs) + +Extract structural coordinates: +```bash +python scripts/extract_form_structure.py input.pdf structure.json +``` + +Build a `fields.json` with `form_fields` array using PDF coordinates: +```json +{ + "pages": [{"page_number": 1, "pdf_width": 612, "pdf_height": 792}], + "form_fields": [ + { + "description": "Name field", + "page_number": 1, + "label_bounding_box": [72, 100, 150, 115], + "entry_bounding_box": [155, 98, 400, 116], + "entry_text": {"text": "John Smith", "font": "Helvetica", "font_size": 11} + } + ] +} +``` + +### Approach B — Fallback (scanned PDFs) + +Convert to images, determine pixel coordinates visually, then use image-based coordinates: +```json +{ + "pages": [{"page_number": 1, "image_width": 1000, "image_height": 1294}], + "form_fields": [...] +} +``` + +### Hybrid + +Use Approach A for most fields and Approach B for anything extract_form_structure misses. + +--- + +## Validation + +Before generating output, validate bounding boxes: +```bash +python scripts/check_bounding_boxes.py fields.json +``` + +Visually verify a page: +```bash +python scripts/create_validation_image.py 1 fields.json pages/page_1.png validation_page_1.png +``` + +Red boxes = entry areas, Blue boxes = label areas. + +**Fill with annotations:** +```bash +python scripts/fill_pdf_form_with_annotations.py input.pdf fields.json output.pdf +``` + +Then convert output to images to verify text placement. diff --git a/.claude/skills/pdf/references/reference.md b/.claude/skills/pdf/references/reference.md new file mode 100644 index 0000000..36a14b0 --- /dev/null +++ b/.claude/skills/pdf/references/reference.md @@ -0,0 +1,174 @@ +# PDF Advanced Reference + +## pypdfium2 — High-Performance Rendering + +```python +import pypdfium2 as pdfium + +pdf = pdfium.PdfDocument("document.pdf") + +# Render page to image +page = pdf[0] +bitmap = page.render(scale=2) # 2x scale = 144 DPI +pil_image = bitmap.to_pil() +pil_image.save("page_1.png") + +# Extract text with coordinates +textpage = page.get_textpage() +for i in range(textpage.count_chars()): + char = textpage.get_charbox(i) + print(f"Char: {textpage.get_text_range(i, 1)}, Box: {char}") + +pdf.close() +``` + +**When to prefer pypdfium2**: High-quality image rendering, pixel-accurate text extraction, processing large batches. + +--- + +## pdf-lib (JavaScript) + +```javascript +import { PDFDocument, rgb, StandardFonts } from 'pdf-lib'; +import fs from 'fs'; + +// Load and modify +const pdfBytes = fs.readFileSync('input.pdf'); +const pdfDoc = await PDFDocument.load(pdfBytes); + +const page = pdfDoc.getPages()[0]; +const font = await pdfDoc.embedFont(StandardFonts.Helvetica); +page.drawText('Hello World', { x: 50, y: 700, size: 20, font, color: rgb(0, 0, 0) }); + +const output = await pdfDoc.save(); +fs.writeFileSync('output.pdf', output); +``` + +**When to use**: Node.js environments, browser-side PDF generation. + +--- + +## Batch Processing + +```python +import os +from pathlib import Path +from pypdf import PdfReader +import logging + +logging.basicConfig(level=logging.INFO) + +def process_pdfs(input_dir: str, output_dir: str): + Path(output_dir).mkdir(exist_ok=True) + errors = [] + + for pdf_file in Path(input_dir).glob("*.pdf"): + try: + reader = PdfReader(str(pdf_file)) + text = "\n".join(page.extract_text() or "" for page in reader.pages) + + out_path = Path(output_dir) / (pdf_file.stem + ".txt") + out_path.write_text(text, encoding="utf-8") + logging.info(f"Processed: {pdf_file.name}") + except Exception as e: + logging.error(f"Failed: {pdf_file.name} — {e}") + errors.append((str(pdf_file), str(e))) + + return errors +``` + +--- + +## Memory-Efficient Large File Processing + +```python +import pdfplumber + +def stream_text(pdf_path: str): + """Yield text page by page to avoid loading entire PDF into memory.""" + with pdfplumber.open(pdf_path) as pdf: + for i, page in enumerate(pdf.pages): + yield i + 1, page.extract_text() or "" + +for page_num, text in stream_text("large_document.pdf"): + print(f"--- Page {page_num} ---") + print(text) +``` + +--- + +## Handling Encrypted / Corrupted PDFs + +```python +from pypdf import PdfReader + +# Encrypted PDF +reader = PdfReader("encrypted.pdf") +if reader.is_encrypted: + reader.decrypt("password") + +# Corrupted PDF — try qpdf first +import subprocess +result = subprocess.run( + ["qpdf", "--recover", "corrupted.pdf", "repaired.pdf"], + capture_output=True, text=True +) +if result.returncode != 0: + print("qpdf repair failed:", result.stderr) +``` + +--- + +## OCR Fallback for Mixed PDFs + +```python +import pdfplumber +import pytesseract +from pdf2image import convert_from_path + +def extract_with_ocr_fallback(pdf_path: str) -> list[str]: + """Use pdfplumber first; fall back to OCR if page has no text.""" + pages_text = [] + images = None # lazy-load + + with pdfplumber.open(pdf_path) as pdf: + for i, page in enumerate(pdf.pages): + text = page.extract_text() + if text and text.strip(): + pages_text.append(text) + else: + if images is None: + images = convert_from_path(pdf_path, dpi=200) + pages_text.append(pytesseract.image_to_string(images[i])) + + return pages_text +``` + +--- + +## Tool Selection Guide + +| Use case | Recommended tool | +|----------|-----------------| +| Simple text extraction | pdfplumber | +| Tables | pdfplumber | +| High-quality image rendering | pypdfium2 | +| Merge / split / rotate | pypdf | +| Create new PDFs | reportlab | +| JavaScript / browser | pdf-lib | +| CLI text extraction | pdftotext (poppler) | +| CLI manipulation | qpdf | +| OCR | pytesseract + pdf2image | +| Repair corrupted | qpdf --recover | + +## Library Licenses + +| Library | License | +|---------|---------| +| pypdf | BSD | +| pdfplumber | MIT | +| reportlab | BSD | +| pypdfium2 | Apache 2.0 | +| pdf-lib | MIT | +| poppler-utils | GPL-2 | +| qpdf | Apache 2.0 | diff --git a/.claude/skills/pdf/scripts/check_bounding_boxes.py b/.claude/skills/pdf/scripts/check_bounding_boxes.py new file mode 100644 index 0000000..2fc9876 --- /dev/null +++ b/.claude/skills/pdf/scripts/check_bounding_boxes.py @@ -0,0 +1,64 @@ +from dataclasses import dataclass +import json +import sys + + +@dataclass +class RectAndField: + rect: list[float] + rect_type: str + field: dict + + +def get_bounding_box_messages(fields_json_stream) -> list[str]: + messages = [] + fields = json.load(fields_json_stream) + messages.append(f"Read {len(fields['form_fields'])} fields") + + def rects_intersect(r1, r2): + disjoint_horizontal = r1[0] >= r2[2] or r1[2] <= r2[0] + disjoint_vertical = r1[1] >= r2[3] or r1[3] <= r2[1] + return not (disjoint_horizontal or disjoint_vertical) + + rects_and_fields = [] + for f in fields["form_fields"]: + rects_and_fields.append(RectAndField(f["label_bounding_box"], "label", f)) + rects_and_fields.append(RectAndField(f["entry_bounding_box"], "entry", f)) + + has_error = False + for i, ri in enumerate(rects_and_fields): + for j in range(i + 1, len(rects_and_fields)): + rj = rects_and_fields[j] + if ri.field["page_number"] == rj.field["page_number"] and rects_intersect(ri.rect, rj.rect): + has_error = True + if ri.field is rj.field: + messages.append(f"FAILURE: intersection between label and entry bounding boxes for `{ri.field['description']}` ({ri.rect}, {rj.rect})") + else: + messages.append(f"FAILURE: intersection between {ri.rect_type} bounding box for `{ri.field['description']}` ({ri.rect}) and {rj.rect_type} bounding box for `{rj.field['description']}` ({rj.rect})") + if len(messages) >= 20: + messages.append("Aborting further checks; fix bounding boxes and try again") + return messages + if ri.rect_type == "entry": + if "entry_text" in ri.field: + font_size = ri.field["entry_text"].get("font_size", 14) + entry_height = ri.rect[3] - ri.rect[1] + if entry_height < font_size: + has_error = True + messages.append(f"FAILURE: entry bounding box height ({entry_height}) for `{ri.field['description']}` is too short for the text content (font size: {font_size}). Increase the box height or decrease the font size.") + if len(messages) >= 20: + messages.append("Aborting further checks; fix bounding boxes and try again") + return messages + + if not has_error: + messages.append("SUCCESS: All bounding boxes are valid") + return messages + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: check_bounding_boxes.py [fields.json]") + sys.exit(1) + with open(sys.argv[1]) as f: + messages = get_bounding_box_messages(f) + for msg in messages: + print(msg) diff --git a/.claude/skills/pdf/scripts/check_fillable_fields.py b/.claude/skills/pdf/scripts/check_fillable_fields.py new file mode 100644 index 0000000..ddeff88 --- /dev/null +++ b/.claude/skills/pdf/scripts/check_fillable_fields.py @@ -0,0 +1,9 @@ +import sys +from pypdf import PdfReader + + +reader = PdfReader(sys.argv[1]) +if reader.get_fields(): + print("This PDF has fillable form fields") +else: + print("This PDF does not have fillable form fields; you will need to visually determine where to enter data") diff --git a/.claude/skills/pdf/scripts/convert_pdf_to_images.py b/.claude/skills/pdf/scripts/convert_pdf_to_images.py new file mode 100644 index 0000000..c35a760 --- /dev/null +++ b/.claude/skills/pdf/scripts/convert_pdf_to_images.py @@ -0,0 +1,31 @@ +import os +import sys + +from pdf2image import convert_from_path + + +def convert(pdf_path, output_dir, max_dim=1000): + images = convert_from_path(pdf_path, dpi=200) + + for i, image in enumerate(images): + width, height = image.size + if width > max_dim or height > max_dim: + scale_factor = min(max_dim / width, max_dim / height) + new_width = int(width * scale_factor) + new_height = int(height * scale_factor) + image = image.resize((new_width, new_height)) + + image_path = os.path.join(output_dir, f"page_{i+1}.png") + image.save(image_path) + print(f"Saved page {i+1} as {image_path} (size: {image.size})") + + print(f"Converted {len(images)} pages to PNG images") + + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: convert_pdf_to_images.py [input pdf] [output directory]") + sys.exit(1) + pdf_path = sys.argv[1] + output_directory = sys.argv[2] + convert(pdf_path, output_directory) diff --git a/.claude/skills/pdf/scripts/create_validation_image.py b/.claude/skills/pdf/scripts/create_validation_image.py new file mode 100644 index 0000000..8a41eac --- /dev/null +++ b/.claude/skills/pdf/scripts/create_validation_image.py @@ -0,0 +1,35 @@ +import json +import sys + +from PIL import Image, ImageDraw + + +def create_validation_image(page_number, fields_json_path, input_path, output_path): + with open(fields_json_path, 'r') as f: + data = json.load(f) + + img = Image.open(input_path) + draw = ImageDraw.Draw(img) + num_boxes = 0 + + for field in data["form_fields"]: + if field["page_number"] == page_number: + entry_box = field['entry_bounding_box'] + label_box = field['label_bounding_box'] + draw.rectangle(entry_box, outline='red', width=2) + draw.rectangle(label_box, outline='blue', width=2) + num_boxes += 2 + + img.save(output_path) + print(f"Created validation image at {output_path} with {num_boxes} bounding boxes") + + +if __name__ == "__main__": + if len(sys.argv) != 5: + print("Usage: create_validation_image.py [page number] [fields.json file] [input image path] [output image path]") + sys.exit(1) + page_number = int(sys.argv[1]) + fields_json_path = sys.argv[2] + input_image_path = sys.argv[3] + output_image_path = sys.argv[4] + create_validation_image(page_number, fields_json_path, input_image_path, output_image_path) diff --git a/.claude/skills/pdf/scripts/extract_form_field_info.py b/.claude/skills/pdf/scripts/extract_form_field_info.py new file mode 100644 index 0000000..59b7bb6 --- /dev/null +++ b/.claude/skills/pdf/scripts/extract_form_field_info.py @@ -0,0 +1,119 @@ +import json +import sys + +from pypdf import PdfReader + + +def get_full_annotation_field_id(annotation): + components = [] + while annotation: + field_name = annotation.get('/T') + if field_name: + components.append(field_name) + annotation = annotation.get('/Parent') + return ".".join(reversed(components)) if components else None + + +def make_field_dict(field, field_id): + field_dict = {"field_id": field_id} + ft = field.get('/FT') + if ft == "/Tx": + field_dict["type"] = "text" + elif ft == "/Btn": + field_dict["type"] = "checkbox" + states = field.get("/_States_", []) + if len(states) == 2: + if "/Off" in states: + field_dict["checked_value"] = states[0] if states[0] != "/Off" else states[1] + field_dict["unchecked_value"] = "/Off" + else: + print(f"Unexpected state values for checkbox `${field_id}`. Its checked and unchecked values may not be correct; if you're trying to check it, visually verify the results.") + field_dict["checked_value"] = states[0] + field_dict["unchecked_value"] = states[1] + elif ft == "/Ch": + field_dict["type"] = "choice" + states = field.get("/_States_", []) + field_dict["choice_options"] = [{ + "value": state[0], + "text": state[1], + } for state in states] + else: + field_dict["type"] = f"unknown ({ft})" + return field_dict + + +def get_field_info(reader: PdfReader): + fields = reader.get_fields() + + field_info_by_id = {} + possible_radio_names = set() + + for field_id, field in fields.items(): + if field.get("/Kids"): + if field.get("/FT") == "/Btn": + possible_radio_names.add(field_id) + continue + field_info_by_id[field_id] = make_field_dict(field, field_id) + + radio_fields_by_id = {} + + for page_index, page in enumerate(reader.pages): + annotations = page.get('/Annots', []) + for ann in annotations: + field_id = get_full_annotation_field_id(ann) + if field_id in field_info_by_id: + field_info_by_id[field_id]["page"] = page_index + 1 + field_info_by_id[field_id]["rect"] = ann.get('/Rect') + elif field_id in possible_radio_names: + try: + on_values = [v for v in ann["/AP"]["/N"] if v != "/Off"] + except KeyError: + continue + if len(on_values) == 1: + rect = ann.get("/Rect") + if field_id not in radio_fields_by_id: + radio_fields_by_id[field_id] = { + "field_id": field_id, + "type": "radio_group", + "page": page_index + 1, + "radio_options": [], + } + radio_fields_by_id[field_id]["radio_options"].append({ + "value": on_values[0], + "rect": rect, + }) + + fields_with_location = [] + for field_info in field_info_by_id.values(): + if "page" in field_info: + fields_with_location.append(field_info) + else: + print(f"Unable to determine location for field id: {field_info.get('field_id')}, ignoring") + + def sort_key(f): + if "radio_options" in f: + rect = f["radio_options"][0]["rect"] or [0, 0, 0, 0] + else: + rect = f.get("rect") or [0, 0, 0, 0] + adjusted_position = [-rect[1], rect[0]] + return [f.get("page"), adjusted_position] + + sorted_fields = fields_with_location + list(radio_fields_by_id.values()) + sorted_fields.sort(key=sort_key) + + return sorted_fields + + +def write_field_info(pdf_path: str, json_output_path: str): + reader = PdfReader(pdf_path) + field_info = get_field_info(reader) + with open(json_output_path, "w") as f: + json.dump(field_info, f, indent=2) + print(f"Wrote {len(field_info)} fields to {json_output_path}") + + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: extract_form_field_info.py [input pdf] [output json]") + sys.exit(1) + write_field_info(sys.argv[1], sys.argv[2]) diff --git a/.claude/skills/pdf/scripts/extract_form_structure.py b/.claude/skills/pdf/scripts/extract_form_structure.py new file mode 100644 index 0000000..5e175d9 --- /dev/null +++ b/.claude/skills/pdf/scripts/extract_form_structure.py @@ -0,0 +1,101 @@ +import json +import sys +import pdfplumber + + +def extract_form_structure(pdf_path): + structure = { + "pages": [], + "labels": [], + "lines": [], + "checkboxes": [], + "row_boundaries": [] + } + + with pdfplumber.open(pdf_path) as pdf: + for page_num, page in enumerate(pdf.pages, 1): + structure["pages"].append({ + "page_number": page_num, + "width": float(page.width), + "height": float(page.height) + }) + + words = page.extract_words() + for word in words: + structure["labels"].append({ + "page": page_num, + "text": word["text"], + "x0": round(float(word["x0"]), 1), + "top": round(float(word["top"]), 1), + "x1": round(float(word["x1"]), 1), + "bottom": round(float(word["bottom"]), 1) + }) + + for line in page.lines: + if abs(float(line["x1"]) - float(line["x0"])) > page.width * 0.5: + structure["lines"].append({ + "page": page_num, + "y": round(float(line["top"]), 1), + "x0": round(float(line["x0"]), 1), + "x1": round(float(line["x1"]), 1) + }) + + for rect in page.rects: + width = float(rect["x1"]) - float(rect["x0"]) + height = float(rect["bottom"]) - float(rect["top"]) + if 5 <= width <= 15 and 5 <= height <= 15 and abs(width - height) < 2: + structure["checkboxes"].append({ + "page": page_num, + "x0": round(float(rect["x0"]), 1), + "top": round(float(rect["top"]), 1), + "x1": round(float(rect["x1"]), 1), + "bottom": round(float(rect["bottom"]), 1), + "center_x": round((float(rect["x0"]) + float(rect["x1"])) / 2, 1), + "center_y": round((float(rect["top"]) + float(rect["bottom"])) / 2, 1) + }) + + lines_by_page = {} + for line in structure["lines"]: + page = line["page"] + if page not in lines_by_page: + lines_by_page[page] = [] + lines_by_page[page].append(line["y"]) + + for page, y_coords in lines_by_page.items(): + y_coords = sorted(set(y_coords)) + for i in range(len(y_coords) - 1): + structure["row_boundaries"].append({ + "page": page, + "row_top": y_coords[i], + "row_bottom": y_coords[i + 1], + "row_height": round(y_coords[i + 1] - y_coords[i], 1) + }) + + return structure + + +def main(): + if len(sys.argv) != 3: + print("Usage: extract_form_structure.py ") + sys.exit(1) + + pdf_path = sys.argv[1] + output_path = sys.argv[2] + + print(f"Extracting structure from {pdf_path}...") + structure = extract_form_structure(pdf_path) + + with open(output_path, "w") as f: + json.dump(structure, f, indent=2) + + print(f"Found:") + print(f" - {len(structure['pages'])} pages") + print(f" - {len(structure['labels'])} text labels") + print(f" - {len(structure['lines'])} horizontal lines") + print(f" - {len(structure['checkboxes'])} checkboxes") + print(f" - {len(structure['row_boundaries'])} row boundaries") + print(f"Saved to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/.claude/skills/pdf/scripts/fill_fillable_fields.py b/.claude/skills/pdf/scripts/fill_fillable_fields.py new file mode 100644 index 0000000..a706cd2 --- /dev/null +++ b/.claude/skills/pdf/scripts/fill_fillable_fields.py @@ -0,0 +1,96 @@ +import json +import sys + +from pypdf import PdfReader, PdfWriter + +from extract_form_field_info import get_field_info + + +def fill_pdf_fields(input_pdf_path: str, fields_json_path: str, output_pdf_path: str): + with open(fields_json_path) as f: + fields = json.load(f) + fields_by_page = {} + for field in fields: + if "value" in field: + field_id = field["field_id"] + page = field["page"] + if page not in fields_by_page: + fields_by_page[page] = {} + fields_by_page[page][field_id] = field["value"] + + reader = PdfReader(input_pdf_path) + + has_error = False + field_info = get_field_info(reader) + fields_by_ids = {f["field_id"]: f for f in field_info} + for field in fields: + existing_field = fields_by_ids.get(field["field_id"]) + if not existing_field: + has_error = True + print(f"ERROR: `{field['field_id']}` is not a valid field ID") + elif field["page"] != existing_field["page"]: + has_error = True + print(f"ERROR: Incorrect page number for `{field['field_id']}` (got {field['page']}, expected {existing_field['page']})") + else: + if "value" in field: + err = validation_error_for_field_value(existing_field, field["value"]) + if err: + print(err) + has_error = True + if has_error: + sys.exit(1) + + writer = PdfWriter(clone_from=reader) + for page, field_values in fields_by_page.items(): + writer.update_page_form_field_values(writer.pages[page - 1], field_values, auto_regenerate=False) + + writer.set_need_appearances_writer(True) + + with open(output_pdf_path, "wb") as f: + writer.write(f) + + +def validation_error_for_field_value(field_info, field_value): + field_type = field_info["type"] + field_id = field_info["field_id"] + if field_type == "checkbox": + checked_val = field_info["checked_value"] + unchecked_val = field_info["unchecked_value"] + if field_value != checked_val and field_value != unchecked_val: + return f'ERROR: Invalid value "{field_value}" for checkbox field "{field_id}". The checked value is "{checked_val}" and the unchecked value is "{unchecked_val}"' + elif field_type == "radio_group": + option_values = [opt["value"] for opt in field_info["radio_options"]] + if field_value not in option_values: + return f'ERROR: Invalid value "{field_value}" for radio group field "{field_id}". Valid values are: {option_values}' + elif field_type == "choice": + choice_values = [opt["value"] for opt in field_info["choice_options"]] + if field_value not in choice_values: + return f'ERROR: Invalid value "{field_value}" for choice field "{field_id}". Valid values are: {choice_values}' + return None + + +def monkeypatch_pypdf_method(): + from pypdf.generic import DictionaryObject + from pypdf.constants import FieldDictionaryAttributes + + original_get_inherited = DictionaryObject.get_inherited + + def patched_get_inherited(self, key: str, default=None): + result = original_get_inherited(self, key, default) + if key == FieldDictionaryAttributes.Opt: + if isinstance(result, list) and all(isinstance(v, list) and len(v) == 2 for v in result): + result = [r[0] for r in result] + return result + + DictionaryObject.get_inherited = patched_get_inherited + + +if __name__ == "__main__": + if len(sys.argv) != 4: + print("Usage: fill_fillable_fields.py [input pdf] [field_values.json] [output pdf]") + sys.exit(1) + monkeypatch_pypdf_method() + input_pdf = sys.argv[1] + fields_json = sys.argv[2] + output_pdf = sys.argv[3] + fill_pdf_fields(input_pdf, fields_json, output_pdf) diff --git a/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py b/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py new file mode 100644 index 0000000..a2ac423 --- /dev/null +++ b/.claude/skills/pdf/scripts/fill_pdf_form_with_annotations.py @@ -0,0 +1,104 @@ +import json +import sys + +from pypdf import PdfReader, PdfWriter +from pypdf.annotations import FreeText + + +def transform_from_image_coords(bbox, image_width, image_height, pdf_width, pdf_height): + x_scale = pdf_width / image_width + y_scale = pdf_height / image_height + + left = bbox[0] * x_scale + right = bbox[2] * x_scale + + top = pdf_height - (bbox[1] * y_scale) + bottom = pdf_height - (bbox[3] * y_scale) + + return left, bottom, right, top + + +def transform_from_pdf_coords(bbox, pdf_height): + left = bbox[0] + right = bbox[2] + + pypdf_top = pdf_height - bbox[1] + pypdf_bottom = pdf_height - bbox[3] + + return left, pypdf_bottom, right, pypdf_top + + +def fill_pdf_form(input_pdf_path, fields_json_path, output_pdf_path): + with open(fields_json_path, "r") as f: + fields_data = json.load(f) + + reader = PdfReader(input_pdf_path) + writer = PdfWriter() + + writer.append(reader) + + pdf_dimensions = {} + for i, page in enumerate(reader.pages): + mediabox = page.mediabox + pdf_dimensions[i + 1] = [mediabox.width, mediabox.height] + + annotations = [] + for field in fields_data["form_fields"]: + page_num = field["page_number"] + + page_info = next(p for p in fields_data["pages"] if p["page_number"] == page_num) + pdf_width, pdf_height = pdf_dimensions[page_num] + + if "pdf_width" in page_info: + transformed_entry_box = transform_from_pdf_coords( + field["entry_bounding_box"], + float(pdf_height) + ) + else: + image_width = page_info["image_width"] + image_height = page_info["image_height"] + transformed_entry_box = transform_from_image_coords( + field["entry_bounding_box"], + image_width, image_height, + float(pdf_width), float(pdf_height) + ) + + if "entry_text" not in field or "text" not in field["entry_text"]: + continue + entry_text = field["entry_text"] + text = entry_text["text"] + if not text: + continue + + font_name = entry_text.get("font", "Arial") + font_size = str(entry_text.get("font_size", 14)) + "pt" + font_color = entry_text.get("font_color", "000000") + + annotation = FreeText( + text=text, + rect=transformed_entry_box, + font=font_name, + font_size=font_size, + font_color=font_color, + border_color=None, + background_color=None, + ) + annotations.append(annotation) + writer.add_annotation(page_number=page_num - 1, annotation=annotation) + + with open(output_pdf_path, "wb") as output: + writer.write(output) + + print(f"Successfully filled PDF form and saved to {output_pdf_path}") + print(f"Added {len(annotations)} text annotations") + + +if __name__ == "__main__": + if len(sys.argv) != 4: + print("Usage: fill_pdf_form_with_annotations.py [input pdf] [fields.json] [output pdf]") + sys.exit(1) + input_pdf = sys.argv[1] + fields_json = sys.argv[2] + output_pdf = sys.argv[3] + + fill_pdf_form(input_pdf, fields_json, output_pdf) diff --git a/book/myst.yml b/book/myst.yml index 617cab2..8ba2efa 100644 --- a/book/myst.yml +++ b/book/myst.yml @@ -23,6 +23,10 @@ project: file: why_causal_inference/why_causal_inference_en.ipynb - file: why_causal_inference/why_causal_inference_ko.ipynb hidden: true + - title: Policy Learning + file: who_should_be_treated/who_should_be_treated_en.ipynb + - file: who_should_be_treated/who_should_be_treated_ko.ipynb + hidden: true site: template: book-theme diff --git a/book/who_should_be_treated/assets/AJStyles&Undertaker.jpg b/book/who_should_be_treated/assets/AJStyles&Undertaker.jpg new file mode 100644 index 0000000..4782de9 Binary files /dev/null and b/book/who_should_be_treated/assets/AJStyles&Undertaker.jpg differ diff --git a/book/who_should_be_treated/data/multi_attribution_sample.csv b/book/who_should_be_treated/data/multi_attribution_sample.csv new file mode 100644 index 0000000..a629466 --- /dev/null +++ b/book/who_should_be_treated/data/multi_attribution_sample.csv @@ -0,0 +1,2001 @@ +Global Flag,Major Flag,SMC Flag,Commercial Flag,IT Spend,Employee Count,PC Count,Size,Tech Support,Discount,Revenue +1,0,1,0,45537,26,26,152205,0,1,17688.363 +0,0,1,1,20842,107,70,159038,0,1,14981.43559 +0,0,0,1,82171,10,7,264935,1,1,32917.13894 +0,0,0,0,30288,40,39,77522,1,1,14773.76855 +0,0,1,0,25930,37,43,91446,1,1,17098.69823 +0,0,1,0,34597,44,51,218703,1,0,17280.70918 +1,0,0,0,40199,21,14,126342,0,0,9153.974376 +0,0,1,1,30454,11,8,96784,0,0,5760.075096 +0,0,0,1,23428,50,55,77298,1,1,17265.11146 +0,0,1,1,7970,131,153,77888,1,1,21953.37179 +0,0,0,1,151143,84,58,417289,1,1,50875.56035 +1,0,1,1,30759,169,136,116016,1,0,22153.82215 +0,0,1,1,19899,279,195,66555,0,0,11972.18818 +0,0,1,1,8420,29,26,36973,1,0,10063.85753 +0,0,1,1,34577,20,15,91689,1,0,10521.9988 +0,0,0,1,1294,18,15,11292,0,1,2296.739654 +1,0,1,1,80629,13,9,466694,0,1,41743.72206 +1,0,1,0,144013,119,124,413240,1,1,56307.34264 +1,0,1,1,17143,24,24,49673,0,1,9673.221778 +0,0,1,1,22729,110,110,117138,0,1,15397.70575 +0,0,1,1,28856,34,34,136831,1,1,21267.51393 +0,0,0,0,14057,24,18,67434,0,1,6321.956387 +0,0,1,0,14682,13,16,45908,0,0,2694.216001 +0,0,0,0,8321,82,50,26999,0,0,2531.466231 +0,0,1,0,22051,170,140,56512,0,0,9116.230185 +0,0,1,0,15052,156,154,41571,0,1,12387.81871 +1,0,0,0,21432,106,122,148865,1,1,27860.93773 +0,0,0,0,21232,221,146,84567,0,0,9072.07955 +0,0,1,1,41352,15,11,126934,1,1,19867.56935 +0,0,1,1,12744,10,10,54468,0,0,2868.606553 +0,0,1,0,13980,46,32,58046,0,1,7126.30626 +1,1,0,0,35303,108,136,120173,0,1,20237.79168 +1,0,1,1,10923,26,21,74202,0,1,11610.05857 +0,0,0,1,16662,11,10,110110,1,1,18647.91666 +0,0,0,1,88422,34,27,295436,1,1,37025.88584 +0,0,0,0,3971,41,30,29867,0,0,1038.615975 +0,0,1,0,22692,17,15,133197,1,1,19821.22882 +0,0,1,1,31488,17,10,112661,0,1,13903.71762 +1,1,0,0,45422,45,56,193432,0,0,15182.77493 +0,0,0,1,62739,108,131,250338,1,0,25477.56325 +1,0,1,0,2168,48,47,11009,1,1,13173.24059 +0,1,0,1,20901,85,80,115902,1,0,15864.39188 +1,0,1,1,4857,44,48,16491,0,1,8488.73425 +0,1,0,0,25002,34,22,99114,1,0,13303.01519 +0,0,0,1,23904,32,20,176769,1,0,15658.03599 +0,0,1,0,5972,28,26,16210,0,1,1866.574021 +0,0,0,1,19486,22,22,116783,0,1,11262.22719 +0,0,1,0,16858,63,45,99214,1,1,18338.09893 +0,0,1,1,37432,98,76,272286,1,1,36192.4073 +0,1,0,1,32794,76,47,101073,0,0,8797.224912 +0,0,0,1,21666,13,14,59452,0,0,3346.04696 +0,0,0,1,2272,33,34,13700,0,0,2984.716846 +0,0,0,1,23677,58,65,93466,1,1,18338.7401 +1,0,1,0,7813,15,15,27740,0,0,5350.908641 +0,0,1,1,117098,13,10,535638,1,1,60479.55167 +0,0,0,1,6590,34,24,19900,0,0,3312.640665 +0,0,0,1,17049,61,62,60369,1,0,12021.49536 +0,1,0,0,88501,127,152,417564,1,1,54047.43047 +1,0,1,1,2937,57,68,10629,0,0,8835.047329 +1,1,0,0,28651,30,28,106147,1,1,23904.30481 +0,1,0,1,7587,50,32,69779,1,1,15366.9466 +0,1,0,1,80706,141,175,244713,0,0,18149.13843 +0,0,0,1,4672,33,24,11762,0,1,3087.227865 +0,0,1,0,41677,26,32,205525,0,1,19301.61921 +0,1,0,0,61286,31,36,179022,1,0,18214.01619 +0,0,1,0,157655,15,18,429946,1,0,28356.19404 +0,1,0,1,24376,20,12,165690,0,1,17103.73349 +0,1,0,1,78105,31,28,429628,1,1,52180.4918 +1,1,0,0,10468,13,14,54499,1,1,17418.02118 +0,0,0,1,14156,59,63,40721,0,1,6480.237874 +1,0,1,0,11328,37,37,56684,1,0,14732.56658 +0,0,1,0,9291,27,33,39421,0,1,6212.661339 +0,0,1,0,47794,51,63,310950,1,1,37274.32798 +0,0,1,1,25131,34,35,129251,0,1,13546.97187 +0,0,0,0,17376,78,60,72051,0,1,6934.633026 +0,0,0,1,45245,34,23,242353,0,1,22886.89951 +0,0,1,1,50999,48,59,128288,1,0,16289.16582 +0,0,0,0,2867,20,16,12868,0,0,427.6482735 +0,0,1,1,67890,15,13,406365,1,1,47587.29878 +1,1,0,1,160244,98,124,454291,1,1,63162.5783 +1,0,0,1,7278,103,93,43359,0,0,8583.56821 +0,0,1,0,61684,14,11,315315,1,1,37902.30016 +0,0,1,1,23501,101,74,59631,0,0,5172.847451 +0,0,1,1,8771,317,268,27460,1,0,20175.58992 +0,0,1,0,10076,27,29,84398,1,1,13690.74117 +0,1,0,1,6669,33,25,34115,1,1,11546.04042 +0,0,1,1,43708,10,7,126892,1,0,13310.68181 +0,0,1,1,24160,32,25,181818,1,0,15695.07674 +0,0,1,1,22691,57,52,149726,1,1,22908.60641 +0,0,1,1,150289,53,56,377721,1,0,28330.76253 +0,1,0,1,24836,120,92,145100,1,0,17501.09664 +0,0,1,0,66191,84,58,176803,1,1,26121.34126 +1,0,1,0,24416,17,18,102243,1,1,18963.73396 +1,0,0,1,6203,38,47,31973,0,1,8998.929994 +0,0,1,1,3176,17,13,21184,0,1,4208.953344 +1,0,0,0,20620,61,68,64891,0,1,11729.14754 +0,0,1,1,10130,42,45,35605,0,1,5698.071425 +1,0,1,0,2082,23,24,11269,1,0,10750.36634 +1,1,0,1,32247,21,24,86154,0,0,10332.25678 +0,0,0,0,4531,83,84,12688,0,0,5205.635952 +0,0,1,1,54076,64,76,211136,1,1,29938.98853 +0,0,1,1,8030,51,50,22089,0,0,3892.89282 +0,1,0,0,22105,58,48,92998,0,1,10775.65117 +0,0,1,0,2446,70,87,12772,1,1,8732.876532 +0,0,0,1,68678,17,18,235152,1,1,30804.12152 +0,0,1,1,28402,143,164,110430,0,1,18359.92468 +0,0,1,1,33525,86,65,98371,1,1,19446.96031 +1,0,1,1,32296,60,51,134989,0,0,10618.8936 +0,1,0,1,25297,27,24,227115,1,0,20154.21729 +0,0,1,1,10143,31,24,81870,1,0,11441.82429 +0,0,0,0,2517,27,17,21453,1,1,8533.242525 +0,0,1,1,8405,73,52,26417,0,0,3883.200484 +1,0,0,1,71141,28,24,210928,0,0,13785.74983 +0,0,1,1,75907,50,42,208770,0,1,20463.75335 +0,0,0,0,2337,159,183,17870,0,0,7309.382264 +1,1,0,1,16756,35,31,70823,1,0,16577.22067 +0,0,0,1,4359,55,48,30794,1,1,11868.75457 +0,1,0,1,22628,14,16,99704,0,1,11800.72088 +0,1,0,1,52238,14,10,150085,1,0,16800.52139 +0,0,0,1,31620,125,146,108026,0,0,11742.24034 +1,0,1,1,20553,174,178,58863,1,0,20339.32765 +0,0,1,1,46828,21,14,121378,1,1,19690.81724 +0,1,0,1,10302,103,98,36116,0,0,8061.36729 +0,0,0,1,11896,20,12,58290,0,1,7112.372385 +0,0,1,0,27461,88,86,77141,1,1,16303.77806 +1,1,0,1,138371,154,95,391156,1,1,55238.15934 +0,1,0,0,17406,14,8,80062,0,1,8856.093234 +0,0,0,1,6672,175,205,17437,0,0,10980.00749 +0,0,0,1,5733,24,20,15552,0,0,1263.065792 +0,1,0,1,4927,71,68,37194,1,1,14424.33442 +0,0,0,0,7712,113,143,35022,0,1,7429.766914 +0,0,0,1,23011,95,68,61522,1,0,10737.71349 +0,0,1,1,37491,17,18,272112,0,1,23602.45932 +0,0,1,0,6444,22,27,46013,1,1,9932.041465 +0,0,1,1,3984,16,10,15835,0,1,4004.123217 +0,1,0,1,94564,59,69,288104,1,0,24503.26207 +0,0,1,0,43904,23,21,199942,0,1,17650.28582 +1,0,1,1,18953,71,53,68308,0,0,9877.285199 +0,0,1,1,22096,30,34,79186,1,1,15465.93262 +0,0,1,1,8555,31,39,31220,1,0,10514.12972 +0,0,0,0,34368,78,52,118289,1,0,14608.44238 +0,0,1,1,15294,120,75,40569,0,0,5958.755283 +0,1,0,0,34126,69,61,187211,0,1,20432.53813 +0,0,1,0,16562,77,66,85917,1,0,12823.25034 +0,1,0,1,9700,109,141,24838,0,1,12434.5908 +1,0,0,0,70265,34,27,330589,1,1,43556.70504 +0,0,0,1,2040,57,68,12825,1,0,10518.12191 +0,0,0,1,8710,105,68,23341,0,1,7931.258914 +0,1,0,1,12776,90,62,41423,1,1,15312.82009 +0,0,0,1,156713,14,13,677214,1,0,40166.67407 +1,1,0,1,3954,20,12,21408,0,0,8699.634473 +0,0,1,1,38797,19,17,326943,0,0,9399.076282 +0,0,1,1,9867,18,11,27353,0,0,2588.025236 +0,1,0,1,11356,11,13,75715,1,1,16257.93618 +0,0,0,1,18931,145,164,50727,0,1,12850.98872 +1,0,0,1,7671,57,35,21975,1,0,11839.8829 +1,0,1,1,17200,150,110,114140,0,1,18577.31238 +1,0,1,1,60359,20,24,459345,1,1,55748.82324 +0,1,0,1,25135,55,45,131549,0,0,8435.037353 +0,0,1,0,2298,19,12,11375,1,0,8415.235112 +0,0,0,0,48536,37,33,335333,1,1,38288.29422 +0,0,0,1,19084,18,19,55362,0,1,6461.177574 +0,0,1,1,12220,66,57,63430,0,0,6489.251169 +0,0,1,0,33933,60,70,157628,0,0,10567.21174 +0,0,1,1,14702,17,16,123893,0,0,5898.268951 +0,0,0,1,27897,35,25,73351,0,0,3352.079607 +1,0,1,1,52982,22,19,159086,0,1,19184.64834 +0,0,1,1,13605,206,156,62175,1,1,19216.95099 +0,0,1,1,5764,53,43,19948,0,1,5020.076872 +0,1,0,1,2244,27,17,15691,0,0,5593.089558 +1,0,0,1,5038,46,32,17721,0,0,6922.064446 +0,0,0,0,68096,13,11,244989,1,1,28475.95454 +1,1,0,1,4849,50,59,14330,0,1,11182.43115 +1,0,0,1,46944,60,72,177787,0,1,22913.23627 +0,0,0,0,24935,80,103,81077,0,1,10502.37863 +0,0,1,1,13657,21,16,44931,1,0,9730.116239 +1,0,1,1,2161,30,37,13652,1,0,12702.69822 +1,0,0,0,12526,89,112,72595,0,1,15586.68329 +1,0,1,0,68732,237,183,224090,1,1,42906.04591 +0,1,0,1,23802,89,70,126710,1,0,14178.61795 +0,0,1,0,117322,41,38,315544,1,1,38604.23011 +0,1,0,1,10745,37,35,28092,1,0,10258.59208 +1,0,1,1,4068,18,13,38388,1,0,13535.50829 +0,0,1,1,3937,61,71,33293,1,0,9309.372487 +0,0,1,1,27937,77,56,155237,0,1,15265.72861 +0,0,1,1,118244,70,71,315199,1,0,24561.74379 +0,0,1,0,22615,14,16,212549,1,0,16571.64054 +0,0,0,1,21515,48,53,75989,0,1,10118.85221 +0,0,0,1,5980,15,14,19643,0,1,2838.325206 +0,0,1,1,3639,56,52,14509,0,0,4098.491438 +0,1,0,0,88237,118,150,229779,1,0,25590.53569 +0,0,1,0,17314,19,18,106862,1,1,18383.28659 +0,1,0,1,8387,175,197,45477,1,0,20337.33835 +0,0,0,1,27267,96,76,104636,0,0,6272.606914 +0,0,1,0,20605,33,33,72497,0,1,5924.766859 +0,0,1,1,12890,29,35,101666,0,0,6657.532861 +0,0,1,1,57910,20,24,268205,1,0,21539.93028 +0,0,0,0,5402,125,149,25775,0,0,7405.369309 +0,0,1,1,69176,48,61,199564,1,1,29080.77548 +0,0,1,1,26470,36,32,94398,1,1,18038.439 +1,0,1,1,10428,18,17,34956,1,1,12826.48382 +0,0,0,0,8917,21,24,24997,0,0,740.2041154 +0,0,1,1,91861,38,23,232176,1,1,30069.56041 +1,0,1,1,5560,14,17,47658,1,1,14984.83452 +0,0,1,1,20889,36,22,64873,0,1,9138.845406 +0,0,0,1,62209,79,97,180677,0,1,21423.36985 +0,0,0,1,40967,64,74,232979,1,1,33710.50043 +0,0,1,1,17317,94,88,77496,0,0,5267.233703 +0,0,0,0,32250,21,22,188642,1,1,25062.01954 +0,1,0,1,5259,27,28,26085,1,0,9904.940439 +1,0,1,1,17184,95,65,120086,1,1,24304.85393 +0,0,1,1,21320,63,57,82308,0,0,6406.755284 +0,0,0,1,2905,41,47,26025,0,1,5116.346422 +0,0,1,1,26657,64,58,161527,0,0,9800.742942 +0,1,0,1,4178,135,107,19275,0,1,9646.977542 +0,0,1,0,1854,33,23,14154,0,0,2925.00813 +0,0,0,1,4586,38,45,21698,1,1,8043.890063 +0,0,1,1,33402,17,13,89124,0,1,10193.08059 +0,0,0,1,15479,90,84,56697,1,0,14903.83914 +0,0,0,0,17982,73,51,52065,1,0,9625.035979 +0,1,0,0,49673,23,28,239833,1,0,19319.62226 +0,0,1,1,18700,30,34,78490,0,1,8096.444424 +0,0,0,1,21059,42,52,115642,1,1,20582.73153 +0,0,0,0,19225,79,75,71811,1,0,11738.91702 +0,0,1,1,76818,13,14,246340,1,0,18364.88524 +0,1,0,1,12616,80,62,57031,0,0,8819.660959 +0,1,0,0,35916,17,21,132169,1,1,22732.21829 +0,0,1,1,33999,95,99,245949,1,1,35096.32977 +0,0,1,0,38213,42,29,338043,0,1,27467.93103 +0,0,1,1,35147,19,18,106306,0,1,10597.60992 +0,0,1,1,6744,12,9,26371,0,1,3115.42718 +0,0,1,1,16928,10,10,85080,1,1,15044.39546 +0,0,1,1,62920,27,30,216075,1,1,29230.15641 +0,0,0,0,4594,76,79,25081,0,1,6347.182107 +0,0,1,1,29370,23,27,143831,1,0,14397.39567 +0,0,1,0,21590,34,21,77756,0,1,6808.504874 +0,0,1,1,18171,33,34,128257,1,1,21128.28362 +0,0,1,1,11026,142,165,54051,0,1,12749.13256 +0,1,0,1,27952,13,16,107788,1,0,14240.70685 +1,1,0,1,21526,11,12,58430,0,1,13134.84644 +0,0,1,1,15825,11,13,59235,0,1,7629.090292 +0,1,0,0,24708,68,64,179604,0,0,9458.421442 +0,0,1,0,6608,35,34,28620,1,0,7657.136936 +0,1,0,1,36287,16,19,112196,1,1,19856.81149 +0,0,0,1,33094,66,55,100932,0,0,5185.256598 +1,0,1,1,4383,83,96,30708,1,1,17481.04576 +0,0,0,1,3176,38,22,13208,1,1,7630.788895 +1,0,1,1,7792,48,49,65744,1,1,21119.4915 +0,0,1,0,5292,15,13,21678,0,0,1678.447341 +0,0,0,1,9937,56,50,55617,0,1,6276.100271 +0,1,0,0,24133,40,50,93204,0,0,6579.419624 +0,1,0,1,26263,20,25,70868,1,1,15935.48222 +1,0,1,0,38842,16,12,126003,1,1,23245.99828 +1,0,0,1,5995,46,57,41312,0,1,11022.39004 +0,0,1,1,20999,84,64,65775,0,1,8633.419379 +0,0,1,0,7930,47,37,41473,1,0,8780.115203 +0,0,1,0,32635,19,20,107181,0,1,11260.84271 +0,1,0,1,24506,14,9,82940,0,0,6434.636636 +0,0,0,1,23291,27,19,84287,1,0,10648.04763 +0,0,1,1,8595,126,113,23124,0,1,8794.347788 +0,0,1,1,11123,51,32,41222,1,1,12292.45826 +0,0,0,1,21949,56,62,59551,1,0,11770.64662 +0,0,1,1,65556,143,119,181708,1,0,20077.4196 +0,0,0,1,3153,34,36,21218,0,0,1691.743608 +0,0,1,0,40992,43,29,120675,0,0,5962.412434 +0,0,1,1,14999,38,41,117742,0,1,14147.67247 +0,0,1,1,7797,31,26,20878,0,0,1364.936583 +0,0,0,0,4365,170,203,22037,1,0,16981.36868 +0,0,0,1,3718,24,20,21031,1,0,6464.915122 +1,0,1,0,47892,41,45,208753,0,1,22672.5671 +1,0,1,1,41496,19,16,188159,1,1,32056.56452 +0,0,1,1,11782,42,40,36314,0,1,5456.59679 +0,0,1,1,15526,73,67,46628,0,0,4740.708498 +0,0,1,1,9601,10,12,27481,0,1,2577.495205 +0,0,1,1,22798,41,25,66720,1,1,13955.58479 +0,0,1,1,5365,22,27,26066,0,1,4995.809424 +0,1,0,1,3045,131,101,21883,0,0,8470.103969 +1,0,0,1,142325,47,49,396317,1,1,51419.71823 +1,1,0,1,9976,18,20,51091,0,1,11592.21297 +0,0,1,0,15861,17,21,104855,0,0,4686.019904 +0,0,1,0,18104,36,27,164427,1,0,15162.89897 +0,0,0,1,106731,103,84,387171,1,0,28861.64052 +0,0,1,0,17323,52,46,65568,0,1,7747.679224 +1,0,0,1,15095,105,124,54340,0,1,15752.30457 +0,0,1,0,19925,19,15,156070,0,0,4339.93969 +0,1,0,1,28974,92,72,110336,1,0,17146.50535 +0,1,0,1,6453,156,187,17014,1,0,15899.73864 +0,0,0,0,1647,29,34,14242,0,1,2675.457403 +0,0,1,1,29419,19,13,146931,0,0,6868.147956 +0,1,0,1,5531,38,40,14398,0,1,7639.256995 +0,0,1,1,55573,31,29,165848,1,1,26572.05176 +0,0,1,1,6886,129,106,17220,0,1,6543.552993 +0,0,0,1,21029,128,93,91299,0,1,12639.67366 +0,1,0,1,2810,535,407,15503,0,0,21445.05937 +0,0,1,1,10824,25,23,42483,0,0,2736.140569 +0,0,1,1,89014,109,140,269738,0,1,29168.27255 +1,0,0,1,132855,77,59,335574,1,0,28664.11676 +0,0,1,0,6807,32,27,25433,1,1,9820.295287 +0,1,0,1,23915,38,30,158725,1,1,23856.34284 +0,1,0,1,13930,131,112,65562,1,0,16241.99888 +0,0,1,1,107773,40,42,346802,1,0,25373.13155 +0,0,0,1,36663,32,33,112622,1,1,17939.21897 +0,1,0,1,8902,104,130,43286,1,1,18184.09885 +0,0,1,0,97203,39,42,288126,1,1,36123.32499 +0,0,1,1,55789,95,60,362323,1,1,43607.018 +1,0,0,1,6370,68,63,51541,1,0,14975.50384 +0,1,0,1,34325,69,69,116687,1,0,18127.12363 +1,1,0,0,5632,34,40,30658,1,0,13068.91705 +1,0,1,1,47377,34,42,129626,1,0,18042.88613 +0,0,0,1,21329,12,12,54067,0,1,4810.106068 +0,0,0,1,43377,106,67,166282,1,0,16043.1834 +0,0,1,0,6150,75,54,47342,0,0,4809.24011 +1,0,1,0,6274,25,31,48622,0,1,9585.262451 +0,1,0,1,12858,18,11,119528,1,1,20842.74918 +0,0,0,1,27159,17,15,134303,0,0,4998.686659 +0,0,0,1,61431,16,14,241590,1,1,30281.73375 +1,0,0,1,12073,32,40,67400,0,0,9195.50682 +0,0,1,0,21109,25,26,147722,0,1,12990.02835 +0,0,1,1,5826,231,270,22805,1,1,18310.04886 +0,0,0,1,4372,43,46,23236,1,0,8068.479679 +0,0,1,0,4162,62,66,25960,0,0,2139.823082 +0,0,0,0,16024,13,8,52378,1,1,10904.29261 +0,1,0,0,4397,55,36,34740,0,1,5827.637873 +0,1,0,1,55877,40,35,246428,1,0,20483.00682 +0,1,0,0,74493,27,21,299245,1,1,37353.16765 +0,0,1,1,20592,20,15,144860,1,0,13779.91158 +0,0,1,1,17654,37,27,161052,0,1,14308.17281 +0,1,0,1,18912,49,53,89464,1,0,15859.34335 +0,0,1,0,28673,113,82,242562,1,1,32562.24665 +0,1,0,0,51176,62,52,140044,1,0,16471.73165 +1,1,0,1,59946,74,74,244004,1,1,40497.96124 +0,0,1,1,13579,180,211,38702,1,0,16523.06401 +1,0,1,1,28764,71,66,88237,0,0,10473.37958 +0,0,1,1,7113,51,45,24939,0,1,5621.495511 +0,0,0,0,44746,82,86,143547,0,1,16116.64649 +1,0,1,1,13932,20,17,80535,1,1,18253.09563 +0,1,0,1,13314,79,82,82226,0,1,12864.58506 +0,0,1,1,2698,10,11,22466,1,0,9765.044709 +0,0,1,1,6696,18,15,16756,0,0,1610.197453 +0,0,1,1,18693,77,91,85722,0,0,8291.163787 +0,1,0,0,79104,150,98,277467,1,0,26538.01097 +0,0,1,1,3610,171,207,28817,0,1,12939.36505 +0,0,1,0,56810,78,69,250355,1,0,20643.79915 +0,0,0,1,13651,35,43,92141,0,1,10595.26695 +0,0,0,0,181671,14,13,456529,1,1,51924.83565 +0,0,1,1,67999,16,16,251152,1,1,31213.60214 +0,1,0,0,25672,16,14,93188,1,0,12642.9538 +0,0,1,1,36456,40,48,117070,0,0,6927.236082 +0,0,1,0,14293,26,31,57400,0,1,5814.425667 +0,0,1,1,44035,12,8,180498,0,0,7340.06039 +0,0,1,1,18056,44,36,70973,1,0,12523.29614 +1,0,1,0,7446,57,54,62155,0,0,8267.670198 +0,0,0,1,60483,67,65,311253,1,1,40723.91115 +0,0,1,1,7166,39,47,20130,1,1,9371.622527 +0,0,1,1,41540,35,36,119009,0,1,14220.32201 +0,0,1,1,5214,27,31,20180,0,0,2267.005255 +0,1,0,1,28456,111,77,84648,1,0,14936.89896 +0,0,0,1,63025,244,184,244034,0,1,29132.85827 +0,0,1,1,26422,42,50,78664,0,1,9850.210724 +0,0,1,1,7722,106,69,31439,1,1,13160.67895 +0,0,1,1,6694,47,33,17661,0,1,5902.711888 +0,0,1,0,16144,49,63,130026,1,0,14066.81751 +0,0,1,0,47156,125,108,203387,1,1,30241.19053 +0,0,1,1,8336,25,22,26640,0,0,3632.90825 +0,0,0,1,17564,121,75,136194,0,0,8156.040297 +0,1,0,1,15949,33,23,105290,0,0,7177.14562 +1,0,1,1,10062,35,33,52179,1,0,14583.99348 +0,1,0,1,9108,92,77,34421,0,0,8403.453102 +0,0,1,1,26039,66,71,81208,0,1,11195.85353 +0,0,1,0,9249,43,51,46714,1,0,11168.71204 +1,0,0,1,17134,83,59,80182,1,1,20948.36407 +0,0,1,1,180249,53,45,523072,1,1,59799.16955 +0,0,1,1,34393,11,10,127796,0,0,5156.378765 +0,0,0,1,34886,36,40,267997,0,1,23392.26768 +0,0,0,1,29790,64,76,102901,1,0,14147.29512 +1,0,1,1,10440,10,11,30476,0,0,6164.337526 +0,1,0,0,5732,101,111,19706,0,1,9219.297591 +0,0,0,0,13071,103,113,41131,0,0,5684.753484 +0,0,0,1,10572,16,15,53019,1,0,9111.931717 +0,0,1,0,8155,63,56,35718,1,0,10277.30621 +0,0,0,0,37613,79,65,111708,1,0,13816.50717 +0,0,0,1,21993,144,98,71301,1,1,17817.12326 +1,0,1,1,2708,92,92,12849,1,0,16609.08389 +0,1,0,1,26275,34,30,67361,0,0,5067.939335 +0,1,0,1,1805,40,49,10101,0,0,4233.988521 +1,0,0,0,4465,23,25,39431,1,0,12541.37395 +0,0,1,1,19104,185,148,63023,0,0,8468.373517 +0,0,1,1,15114,14,16,118715,0,1,10159.10407 +0,0,1,1,5579,131,85,40869,1,0,11627.39836 +1,0,0,0,7080,25,32,35339,1,0,12927.24394 +0,0,0,1,24857,45,38,124626,0,0,7304.186619 +0,1,0,1,62020,13,15,186338,1,0,19648.6002 +1,0,1,1,36893,15,16,285473,1,1,41386.39792 +0,0,1,1,39103,82,97,139197,1,1,25824.9784 +0,0,0,1,45474,58,73,150931,1,0,16145.67606 +0,0,1,1,2976,66,80,10166,0,1,5360.752999 +0,0,1,0,1556,10,11,11288,0,1,3080.489274 +0,0,0,1,11294,30,33,112288,1,0,11639.04193 +0,0,1,1,88673,59,68,354861,0,1,32723.36488 +0,0,1,0,24787,30,20,116506,0,1,11147.73518 +1,0,0,1,31224,160,200,144355,1,1,32341.84295 +0,0,1,1,70994,84,73,278699,1,1,38448.55502 +0,0,1,1,17925,11,7,73279,0,1,7830.856999 +0,0,0,1,11086,47,39,39742,1,0,7762.919592 +0,1,0,1,22586,37,47,67962,0,0,6274.782362 +0,0,1,1,9337,49,36,24599,0,1,6195.65709 +0,0,1,1,4106,14,15,13447,0,1,3287.837466 +0,0,0,1,2820,11,13,11436,0,1,3141.438092 +0,1,0,0,3473,28,19,15797,0,0,2367.974069 +0,0,1,1,62198,18,12,162238,0,0,8267.460899 +0,1,0,1,12529,136,129,100303,1,1,23611.31726 +0,0,1,1,17934,213,223,48109,0,0,13133.45761 +1,0,0,0,26517,27,33,68723,0,0,9594.085757 +1,0,1,1,84183,47,55,211681,1,0,21632.58762 +1,0,0,1,6616,23,14,30771,0,0,4529.899662 +0,0,0,0,19834,57,43,71494,1,1,15139.6409 +0,1,0,0,2920,16,13,10914,1,0,7605.428215 +0,1,0,1,71548,95,66,246262,1,1,34952.13563 +0,0,1,1,7136,30,29,29883,0,1,5157.582288 +0,0,0,1,30996,28,25,77894,0,1,9597.463865 +1,0,1,1,4381,16,15,23762,0,1,7237.045021 +0,0,1,0,48086,13,11,134061,1,1,20640.76421 +1,0,1,1,13047,43,44,50236,0,0,9437.606288 +1,0,0,0,20779,30,29,58572,0,0,6832.944493 +0,0,0,1,3701,100,82,14786,0,0,5994.344433 +1,0,1,0,105657,23,16,365657,1,0,27892.79878 +0,0,1,1,51015,186,172,130256,1,1,26391.25123 +0,1,0,0,9247,92,114,75889,0,1,13929.84848 +0,1,0,1,16894,58,46,98446,0,0,7199.232341 +0,0,0,1,77983,106,105,240548,1,1,34452.27461 +0,0,1,1,43922,34,24,317542,1,1,38424.33029 +0,0,0,1,101715,58,52,441327,1,1,52058.55769 +0,1,0,1,65186,66,54,181351,1,0,21550.5332 +0,0,1,1,34484,12,13,133730,1,1,22621.54606 +1,0,1,0,2263,10,6,22627,0,0,5749.223418 +0,0,1,1,36360,81,78,162718,1,0,17983.01013 +0,0,0,1,42258,83,59,226327,1,1,32179.11067 +0,0,1,1,23536,36,28,107698,0,0,4986.290485 +0,0,0,0,4772,93,74,31472,1,1,12786.3002 +0,0,0,1,8391,107,77,21257,1,0,11037.04497 +0,0,1,1,19917,69,48,111687,0,0,5879.647635 +0,0,1,1,53873,39,37,164836,1,0,17266.07837 +0,0,1,0,2276,10,10,11649,1,0,4619.491361 +1,0,1,0,7709,53,61,37189,0,1,9916.962461 +0,0,1,1,2692,14,16,14203,0,1,2161.660276 +0,0,1,1,90742,97,107,579829,1,1,70405.60593 +0,1,0,1,14130,45,51,59812,0,1,10011.99584 +0,1,0,0,119865,52,31,450829,1,1,54908.52665 +1,0,1,1,24101,35,42,77767,0,1,13610.06466 +0,0,1,1,6533,65,56,32288,0,1,5449.408473 +0,0,0,0,103019,32,37,336370,1,1,39979.63889 +0,0,1,1,84538,50,50,261351,0,1,23226.09424 +0,0,0,0,20640,14,10,55757,0,1,4643.135404 +1,0,1,1,11230,14,11,104708,0,0,8611.12893 +0,0,0,0,16160,10,9,89259,0,0,3531.240563 +0,0,0,1,19215,18,13,148376,1,1,21817.54653 +0,0,1,1,4843,25,26,31145,0,1,5595.403592 +0,0,1,1,20410,29,27,133682,1,1,21018.41974 +0,0,1,1,49747,11,10,150840,0,0,7639.384949 +0,0,1,1,14819,15,12,47163,1,0,9710.63789 +0,0,1,1,21792,211,136,131719,0,1,16740.27343 +0,0,0,0,35220,27,21,184873,0,0,6516.061032 +0,0,1,1,6192,41,43,17203,1,1,10457.70207 +0,0,1,0,27843,184,160,77525,0,0,10263.97187 +0,0,0,1,19419,103,100,65629,0,0,8372.933098 +0,1,0,0,4127,10,8,12863,0,1,5745.206084 +0,0,1,1,47825,23,15,174200,1,0,16693.90473 +1,0,1,0,7680,111,89,61748,1,0,16859.57223 +0,0,1,1,95504,82,60,303089,1,1,39591.00952 +0,0,0,0,17615,16,9,102904,1,1,15570.84483 +0,1,0,1,12249,97,107,39792,0,0,10507.14384 +0,1,0,1,14642,57,63,74178,0,0,9045.136014 +1,0,0,1,9785,14,9,26468,0,0,4921.146726 +0,0,0,0,5737,16,14,46649,0,1,3290.437078 +0,0,1,1,23504,67,40,83551,1,0,11067.72266 +0,0,1,1,20753,46,28,53095,0,0,4100.066835 +0,0,1,1,8840,29,36,72334,0,0,4716.348468 +0,0,1,1,39863,58,67,210389,1,1,31346.13915 +0,0,0,0,9049,68,63,35945,0,0,2342.322272 +0,1,0,1,17463,66,47,140023,1,0,16650.33293 +1,0,1,0,8275,19,16,21935,0,0,6043.553795 +0,0,1,0,37609,76,93,186672,1,0,18216.06353 +0,0,1,1,10049,100,68,53619,1,1,15951.4408 +0,0,1,1,10942,125,154,33710,1,0,15178.07679 +0,0,0,0,72978,46,39,189662,1,1,25200.70828 +0,0,1,0,12363,27,22,31227,1,1,9139.886332 +1,0,1,0,27289,36,31,121945,1,0,16779.91344 +0,0,1,1,7667,44,47,46684,0,1,6629.553622 +0,0,1,0,8679,18,11,28879,0,0,2725.168602 +1,0,1,1,22840,11,6,126504,0,0,9502.755081 +0,1,0,1,9708,89,59,45745,0,0,5006.419232 +1,0,0,0,26739,81,87,120447,0,0,10819.49251 +0,0,0,1,56503,53,48,146965,1,1,21664.35844 +0,0,1,1,1995,112,104,15256,0,1,6100.597739 +0,0,0,1,35938,19,20,95950,0,0,4656.715179 +1,0,0,1,4312,38,41,30816,0,0,8202.154684 +1,0,1,1,11240,13,14,61523,1,0,14287.6097 +1,0,1,1,12051,95,114,32497,1,0,17091.29017 +0,1,0,1,14072,25,16,134313,1,0,13281.92333 +1,1,0,0,10772,30,20,43592,0,0,8978.875867 +0,0,1,0,29398,71,72,103500,0,1,12218.00156 +0,0,1,1,2009,82,83,13299,0,1,6398.359921 +0,0,1,0,55019,75,65,151204,0,1,15393.43583 +0,0,1,1,8024,117,117,24001,1,0,12216.88022 +0,0,1,1,3163,16,10,29186,0,0,3243.766223 +0,0,0,0,9121,17,18,84477,0,0,3859.295896 +0,0,1,1,4566,56,40,13178,0,0,4549.874946 +0,0,1,1,49437,46,44,228672,1,0,18526.7485 +0,0,1,1,52486,15,10,140954,0,0,5858.523325 +0,0,1,1,69370,114,103,184808,0,1,20807.66651 +0,0,0,1,9789,156,197,28010,0,1,13530.65248 +0,0,1,1,8656,80,95,43736,1,0,12710.58625 +0,0,1,1,21175,48,48,102315,1,0,12091.04998 +0,1,0,1,7702,292,312,26266,1,0,22859.58487 +1,0,1,0,60376,124,104,157177,1,1,31904.76107 +0,0,0,0,58435,60,41,191337,0,1,17286.22888 +1,0,1,1,3496,125,161,16201,0,0,12469.58864 +0,0,1,1,7388,40,48,20111,0,1,5416.301687 +0,0,1,0,10081,104,76,25349,1,0,7577.97488 +0,1,0,1,59263,45,33,159316,1,1,26015.1458 +0,0,1,1,39850,117,126,266732,1,1,38457.41874 +0,0,1,1,124422,30,24,426440,0,1,37135.08325 +1,0,1,1,23914,30,35,130900,0,1,16918.90171 +1,0,1,1,7699,27,25,33783,1,0,14934.28956 +1,0,1,1,78624,80,97,213882,1,1,36116.57596 +0,0,1,0,17187,53,60,96134,0,1,10323.35981 +0,1,0,1,54043,18,22,213215,1,1,31239.19889 +0,0,0,0,26325,47,33,152157,1,1,21319.23994 +1,0,1,1,42241,21,25,253465,1,1,36720.15919 +0,0,0,1,5858,15,10,17547,0,0,866.5027041 +1,0,0,1,6384,102,72,35227,1,0,14702.80333 +0,0,1,1,2786,21,17,12121,0,1,2258.131151 +0,0,0,1,82116,31,37,456050,1,1,51880.29928 +0,0,1,1,24232,142,88,157885,1,0,18343.63045 +1,0,1,1,2620,45,30,26001,0,0,5512.498855 +0,0,1,1,3921,31,24,14241,0,0,3313.33739 +1,0,0,1,118363,143,147,304508,1,1,47852.08547 +0,1,0,1,21732,11,12,66918,0,1,8795.355976 +0,0,1,0,8770,50,42,82729,1,1,15661.76372 +1,0,1,1,3432,28,20,17932,1,0,11402.19819 +1,0,0,1,6756,45,34,34062,0,0,8351.665632 +0,0,1,1,30137,74,73,117713,0,0,7571.36749 +0,0,0,0,48923,25,27,131711,0,1,12597.96129 +0,0,0,1,10364,36,33,35162,0,0,2499.167204 +0,0,1,0,15403,11,8,47092,1,1,11240.48073 +0,0,1,1,5730,213,267,47487,0,0,12059.02577 +0,0,0,0,27791,50,47,117492,0,0,5134.198316 +1,0,1,1,11571,23,21,53433,0,1,10629.10275 +0,0,1,1,6523,43,36,18816,1,0,9464.520369 +0,1,0,0,37292,42,42,96920,0,0,7979.394504 +0,0,0,1,91266,90,57,287031,1,1,36334.68281 +0,0,1,1,25181,241,297,70897,1,1,27973.15607 +0,0,1,1,15019,50,55,38278,0,1,6338.094915 +0,1,0,1,5511,12,14,48863,1,1,11006.59102 +1,0,1,1,29488,58,47,125085,1,1,26038.79802 +0,0,1,1,32206,38,36,284796,1,1,36645.63815 +0,0,0,1,124635,35,41,368727,1,1,45373.57403 +0,0,1,0,12544,20,20,91212,0,1,8654.983774 +0,1,0,1,12907,12,13,71272,0,0,5779.293269 +0,1,0,1,20808,156,121,58423,0,1,11890.29075 +0,0,0,0,15182,96,94,91407,1,1,19539.92132 +0,1,0,1,4417,18,20,15004,0,0,3937.913319 +0,0,1,1,27261,39,35,89449,0,0,5573.486266 +0,0,1,0,94970,138,145,270429,0,1,28266.38094 +0,0,1,1,4418,21,16,13274,0,0,3095.427966 +1,0,0,1,8841,37,22,33257,0,0,5563.593837 +0,0,0,0,6372,51,36,25215,1,0,8733.23786 +1,1,0,0,2144,43,38,14802,0,0,8901.799611 +0,0,0,1,4515,23,23,38466,1,1,12135.37366 +0,1,0,0,2454,39,33,16107,0,1,3831.361007 +1,0,0,0,14352,102,88,45394,0,1,11339.28766 +0,1,0,1,19537,106,102,87840,0,1,14775.65201 +0,0,1,0,5145,45,50,25495,0,1,4170.850008 +1,1,0,1,19614,69,56,57081,1,0,17970.53412 +0,0,0,1,8633,49,62,74423,0,0,7596.227578 +0,0,1,1,11846,78,90,54722,1,1,15920.2787 +0,0,1,1,10003,78,67,26486,0,1,4266.673288 +0,1,0,1,9625,29,27,31766,0,1,6432.659745 +0,0,1,1,16926,13,12,99886,1,0,12412.85596 +0,0,1,1,10846,12,13,62702,1,0,8862.558253 +0,1,0,1,30862,11,10,141853,1,1,22100.80708 +0,0,0,1,37431,17,14,100785,1,1,16670.66491 +0,1,0,1,23443,63,61,150607,0,0,10265.69667 +0,1,0,1,5254,45,39,16314,0,0,3674.955232 +0,0,0,1,1484,74,83,10356,0,1,3749.068364 +0,1,0,0,32211,61,61,169634,1,0,17965.99838 +1,0,0,1,24262,18,22,114054,1,0,14901.1801 +0,0,1,1,23411,52,34,61017,1,1,13705.96307 +0,0,1,1,14557,31,29,48400,1,0,10045.32635 +0,0,1,1,3612,31,39,15993,0,1,4451.110194 +0,0,1,0,6048,74,91,26755,0,0,4823.024566 +0,0,0,0,41713,38,31,127759,0,0,3579.628507 +0,0,0,1,3755,110,81,31828,0,0,5080.331732 +0,0,0,1,58730,11,13,156161,1,0,16838.31593 +1,0,1,1,9946,23,18,25537,0,1,8450.373565 +0,0,1,1,62638,29,34,201956,1,0,16353.64152 +0,0,1,0,24886,35,31,140824,1,1,21353.98798 +0,0,0,0,59675,79,49,172544,1,1,23784.014 +1,0,1,1,22479,149,152,57507,0,0,14855.23627 +0,0,1,1,6765,64,55,36235,0,0,3924.85708 +0,0,1,1,4370,163,113,17863,1,0,13067.51613 +1,1,0,0,32908,63,69,182030,1,1,31603.40384 +1,0,0,0,44768,72,78,288131,1,1,41805.45738 +0,0,1,0,51501,19,14,160081,0,1,13253.74293 +0,1,0,1,37973,12,9,118758,0,0,9247.633942 +0,0,0,1,41362,21,26,126915,0,1,12458.48419 +0,0,1,1,100158,397,323,274719,0,1,37514.29356 +0,0,1,1,13531,68,68,41268,0,0,7088.813884 +0,1,0,1,10693,39,38,52841,1,1,15146.54356 +0,0,1,0,9508,55,39,66334,0,1,5978.721145 +0,0,0,1,6476,64,80,23459,0,1,7550.86325 +0,0,1,0,11469,29,35,54717,0,0,3341.823071 +0,0,1,1,50873,17,12,193027,1,1,26327.42469 +0,0,0,1,19255,198,216,48928,0,0,9853.256899 +0,0,0,0,68221,157,133,184800,1,0,18208.87536 +0,0,0,0,6760,42,49,52894,1,1,10408.71595 +1,0,1,1,10816,85,53,27988,0,1,9306.015641 +0,0,1,1,13410,17,19,60339,0,0,2884.160011 +0,1,0,1,20812,47,43,65449,0,1,9734.050914 +0,0,1,0,10132,12,14,75928,0,0,2883.081314 +0,0,0,0,59470,41,27,292631,1,1,36257.64003 +0,0,1,1,112782,38,39,315258,0,1,27193.64001 +0,0,1,1,20229,84,53,58026,0,0,5205.690894 +0,1,0,1,17655,142,98,96204,1,0,15345.31194 +0,0,0,0,18655,23,17,48163,1,1,10107.28003 +0,0,1,1,55725,25,23,166714,1,0,17594.85795 +0,0,0,1,8346,30,32,30487,0,0,3175.214083 +1,0,0,1,24125,92,77,102457,0,0,11334.54085 +0,0,1,0,6244,90,75,51526,0,0,4258.853967 +1,0,1,0,13683,44,56,67320,0,0,8328.713237 +1,0,0,1,6983,18,12,55021,1,1,14420.92728 +1,0,0,1,24811,15,18,102213,0,1,14348.56177 +0,0,1,0,44913,63,44,417163,1,1,47465.3412 +0,0,1,0,4186,12,10,15928,1,1,8648.999344 +0,0,1,1,3885,96,86,17580,1,1,10898.43799 +1,0,1,0,10182,18,20,74347,1,0,12159.90168 +0,0,0,1,11623,16,19,87554,0,1,7233.63393 +0,0,0,1,37849,24,19,108781,1,1,17732.64553 +0,0,1,0,26267,19,24,111482,0,0,5042.940727 +0,0,0,0,36834,84,99,100638,1,0,15057.14152 +0,0,1,1,11158,34,22,32180,0,0,4486.460688 +0,0,1,1,41565,13,9,108630,1,1,17150.95795 +0,0,1,1,8298,73,93,69506,1,0,11789.74401 +0,0,1,1,10833,115,107,33468,0,0,5959.569767 +0,0,0,0,13728,28,26,52748,0,0,2417.57566 +0,0,1,0,43005,16,15,243471,0,1,19579.10068 +0,0,0,1,23209,51,59,64592,0,0,6723.133299 +1,0,1,1,24414,54,36,89895,1,0,16628.32527 +0,0,0,1,12277,22,26,34947,0,0,4656.094392 +1,0,1,0,38269,12,13,107397,1,0,15324.51276 +0,0,1,1,20574,39,40,78478,1,0,9484.149855 +0,1,0,1,8841,10,9,34104,0,0,5098.284531 +0,0,1,0,9260,41,34,87200,0,0,3820.739798 +0,0,1,1,21805,59,54,106506,0,1,11924.61423 +0,1,0,0,27969,36,24,155298,0,1,15583.72657 +0,0,0,1,16583,92,65,58958,1,0,12828.23697 +1,0,1,1,72348,51,35,241828,0,1,26127.9543 +1,0,0,1,14403,27,20,55228,0,0,6713.853191 +0,0,0,1,22024,49,46,118816,1,0,14502.08795 +1,1,0,1,62831,102,115,180594,1,1,35018.90603 +0,1,0,0,82642,25,22,271759,1,0,20509.34615 +0,0,1,1,37274,35,36,140684,0,0,7800.921691 +0,1,0,0,52187,12,9,147369,1,1,20957.4076 +1,0,0,1,2582,97,120,14821,1,0,15673.91672 +0,0,1,1,11106,102,80,49998,0,0,5621.031559 +0,0,0,1,11587,125,114,69017,0,0,9156.330752 +0,1,0,1,41123,62,71,225071,1,0,21751.17177 +0,0,1,1,4741,100,74,39316,1,0,11367.81931 +0,0,1,1,2983,24,18,12027,1,0,7367.999154 +0,0,1,1,67185,19,23,592031,1,1,66294.40273 +0,0,1,1,13886,111,107,58209,1,1,15133.085 +0,0,1,1,4282,70,47,10741,0,1,5618.644444 +0,0,1,1,14278,49,47,50781,1,1,13386.88583 +0,1,0,0,33723,16,20,230303,1,1,31614.4348 +1,0,0,1,108490,49,36,285019,1,1,40717.37495 +1,1,0,1,12425,101,107,31355,1,0,18790.94691 +1,0,1,1,37843,13,10,159448,0,0,10230.59352 +0,0,1,1,4886,22,23,18308,0,0,3440.503781 +0,0,0,1,24118,22,23,85655,0,1,9020.869609 +0,0,0,1,41197,71,85,106057,1,1,19384.67619 +0,0,0,1,36730,86,57,118542,0,1,12495.46075 +1,0,1,1,6270,39,38,35737,0,0,8370.509775 +0,0,0,1,48090,39,41,194165,1,0,17499.98951 +0,0,1,1,4196,46,37,28286,1,1,9859.146847 +0,1,0,0,7210,35,44,49912,0,0,5967.841137 +0,0,1,1,4610,18,22,24152,1,0,7881.626501 +0,0,1,1,82500,149,125,207800,1,0,21643.22548 +0,0,1,1,3152,68,82,15647,0,0,5102.757612 +0,0,1,0,3001,113,104,12047,0,1,4998.714087 +0,0,0,1,36290,11,13,189870,0,1,18641.16406 +0,0,1,1,13968,257,311,66876,0,1,19960.93409 +0,0,0,1,7859,46,43,39429,0,1,7089.908942 +0,1,0,1,51940,178,174,292856,0,1,31379.32219 +1,0,1,1,40464,26,24,185851,1,0,19645.23798 +1,0,0,1,5855,22,15,41749,1,1,13650.70235 +1,0,0,0,20478,70,55,73767,0,0,7603.842548 +0,0,0,0,12950,20,14,63673,1,1,11390.60223 +0,0,1,1,50706,185,191,259323,0,1,29831.33311 +0,0,1,0,12336,66,47,65794,0,0,5295.01662 +0,0,1,1,23842,37,33,92404,1,0,12156.37941 +0,0,0,0,5618,19,22,14347,1,1,5903.90688 +0,0,0,1,28132,93,117,74672,0,0,6671.775593 +1,0,1,1,12270,17,22,31254,0,1,8959.680159 +0,0,0,1,35356,124,107,100331,0,1,12764.66444 +0,1,0,1,39636,23,23,139884,0,1,13465.12537 +0,1,0,1,14221,143,151,51248,1,0,19010.06087 +1,0,0,0,4411,17,17,14701,1,0,10443.93574 +0,0,1,1,35540,19,23,111631,1,0,14659.38464 +0,0,0,1,2436,78,53,11520,0,0,3432.258329 +0,0,1,1,10278,119,122,64211,0,0,9013.157714 +0,0,1,1,23473,30,28,60043,1,1,13186.55818 +0,1,0,1,32605,62,53,130204,1,0,17086.88052 +1,0,1,0,5443,51,53,29779,1,1,12891.60928 +0,1,0,1,12287,14,11,32485,1,0,10804.32922 +1,1,0,1,16551,14,13,72707,0,0,10831.10914 +0,0,0,0,112485,37,33,294377,1,0,20969.14879 +1,1,0,0,43867,15,15,178062,1,1,29001.08064 +0,0,1,0,74769,18,20,211482,1,1,27140.0278 +1,0,1,1,41708,229,147,125470,0,1,21229.99095 +0,1,0,1,14132,32,23,73589,0,1,9436.539748 +0,0,1,1,1645,42,29,10561,1,0,7917.72614 +0,0,1,0,12549,76,57,38509,0,0,4186.247555 +0,0,1,1,41952,132,123,159724,1,0,19038.30905 +0,1,0,1,57496,28,30,180825,1,0,18473.04575 +0,0,0,0,57708,43,55,188921,0,0,8200.044385 +0,0,1,1,26207,63,72,105094,1,1,19052.52686 +0,0,1,0,87395,14,11,265685,1,1,31667.2133 +0,0,0,0,31288,62,66,128180,0,0,7876.537537 +0,0,1,1,22486,28,32,186502,0,1,17617.60425 +0,1,0,1,62431,23,29,170763,1,1,26957.69292 +0,0,1,1,16166,22,28,47458,0,0,4414.542295 +1,0,0,0,10481,66,84,34409,0,1,9644.67258 +0,0,1,1,11046,68,54,39023,0,0,4836.424456 +1,0,1,0,5716,33,27,18758,1,0,10680.46847 +0,0,1,1,31073,177,135,120287,0,0,10224.81206 +0,0,0,1,18683,128,151,93074,1,0,18355.78078 +0,1,0,1,36027,50,32,153666,0,1,17033.61914 +1,0,0,1,57356,59,69,270515,1,1,38720.5649 +1,0,1,1,17026,72,65,68146,0,1,12469.49981 +0,0,0,1,10509,25,27,35371,0,0,3850.495537 +0,0,1,1,71571,18,20,243258,1,1,32101.21364 +0,0,1,1,18663,33,38,111100,0,0,7038.626683 +0,0,0,1,3083,69,54,16495,1,0,9018.927033 +0,0,0,0,8970,14,11,84745,1,1,13071.80872 +0,0,0,1,31243,68,54,136917,1,1,23749.7002 +0,0,1,1,17250,29,24,60243,1,1,13029.4313 +0,0,1,1,4554,18,17,13039,0,0,4193.71714 +0,0,0,0,13831,69,76,90289,0,0,4992.113273 +0,1,0,0,44217,42,51,130825,1,0,16737.69932 +0,0,1,0,10513,29,34,27477,1,1,10363.02502 +0,0,0,0,15967,39,35,89785,1,0,10216.49236 +0,0,1,0,3019,13,8,15849,0,1,1918.472293 +0,0,1,1,7839,127,112,66145,1,1,17110.84006 +1,0,0,1,46790,28,29,202190,0,1,23053.75214 +0,0,0,1,25059,84,95,101515,0,0,8605.208523 +0,0,0,1,60204,67,83,290129,1,1,38259.78067 +0,0,0,0,5553,37,31,24958,0,1,3215.696532 +1,1,0,0,2635,176,170,10540,1,0,18151.19212 +0,1,0,1,11919,73,94,116933,0,1,16588.51885 +0,0,1,1,44548,78,49,139936,1,0,16430.5856 +0,1,0,1,12196,72,58,54559,0,1,10381.80894 +0,0,1,0,30518,58,48,85608,1,1,14940.5702 +1,1,0,1,11235,15,16,33761,0,1,8618.757569 +0,0,1,0,16918,24,20,49967,0,0,2205.347793 +0,0,1,1,51551,19,23,228340,0,1,19487.9771 +0,0,0,1,1161,137,126,10110,0,0,6438.150315 +0,0,1,1,23339,10,6,121433,1,0,11475.91936 +0,0,1,1,11747,13,14,30759,0,0,3952.418228 +0,0,0,1,38292,17,19,276460,1,1,32961.26813 +0,0,0,0,9385,14,12,24120,0,1,2150.764164 +1,1,0,1,22095,90,88,186400,0,1,24938.68313 +0,0,1,0,4524,52,57,11477,1,1,8481.162147 +0,0,1,0,3590,84,65,17743,0,0,3901.467895 +0,0,0,0,57173,69,81,194397,1,0,18470.39534 +0,0,1,1,11691,12,10,30587,0,0,2445.971539 +1,0,0,1,43341,132,82,119059,0,0,12205.08601 +1,0,1,1,39893,27,23,118939,0,1,15217.28724 +0,0,0,1,43130,25,25,155603,1,1,22927.8039 +0,0,1,1,2432,20,22,10707,0,1,3579.826911 +0,0,0,1,4616,99,94,14943,1,0,11813.63627 +0,0,0,1,43744,20,19,123561,0,1,12900.7674 +0,0,1,0,19166,84,96,164619,1,1,24497.47256 +0,0,0,1,12244,106,114,47774,0,1,9585.584733 +0,0,1,1,48686,60,76,335624,1,1,41627.81473 +0,0,0,1,14376,59,75,39382,0,1,6177.235708 +0,0,0,1,23822,21,17,70596,0,1,7828.339295 +1,1,0,1,5162,132,119,51026,1,1,22679.74804 +1,0,1,1,97206,70,85,288765,1,0,28145.2671 +0,0,0,1,7972,56,71,20187,0,0,4358.590607 +0,0,1,0,119282,111,73,381223,1,1,48825.15791 +1,1,0,1,48188,36,24,166317,0,0,13475.01085 +1,0,1,1,21309,75,93,96090,0,0,12753.54049 +0,1,0,1,59161,10,9,177549,0,1,17060.57066 +0,0,1,0,18220,34,28,95970,0,1,8836.230462 +0,0,0,1,58011,81,84,164011,0,1,19483.50593 +0,0,1,1,14562,27,31,50186,0,0,4372.063859 +0,0,0,1,30014,276,295,205223,0,1,29953.98746 +0,0,1,0,25956,43,36,207341,1,0,17356.96793 +0,1,0,1,23191,55,62,115530,1,0,17237.24459 +0,0,0,1,63723,18,17,277788,1,1,33984.33364 +0,0,1,1,34485,23,26,94661,1,0,12052.66583 +0,0,0,1,10832,89,80,36405,1,0,11457.6029 +1,1,0,1,3425,11,7,20511,1,1,14623.94369 +0,0,1,1,48257,25,16,138281,1,1,21028.4986 +0,0,1,1,13363,39,44,44636,0,0,6198.280838 +0,0,1,1,11948,48,45,78276,0,0,8591.515014 +0,0,1,1,24625,80,81,154873,0,1,17730.1473 +0,0,0,0,20477,22,24,150152,0,1,12872.69707 +0,0,1,1,54552,52,58,178123,0,0,9448.094145 +0,0,1,1,12774,91,103,38037,0,0,6335.50074 +0,0,1,1,4302,23,18,37911,1,1,11050.47634 +0,0,1,1,25692,110,93,107027,1,0,15720.36657 +0,0,0,1,12288,91,66,85794,1,1,17085.61706 +1,0,0,0,31423,61,69,149848,1,1,26623.66442 +0,1,0,0,47336,48,50,199343,1,0,20241.82221 +0,1,0,1,56979,15,13,223319,1,1,30246.84776 +0,0,1,0,42895,11,8,409816,1,0,27934.82979 +0,0,1,1,27340,60,45,199973,1,1,28643.98619 +0,1,0,1,18050,96,73,46960,0,0,7386.644608 +0,0,1,1,17590,117,111,64199,1,1,16801.29799 +0,0,1,1,73270,51,35,210451,1,0,17615.61941 +0,0,1,0,19700,60,40,52203,0,0,3702.353016 +1,0,0,1,62574,15,17,211830,0,0,10518.47624 +0,0,1,0,14416,124,114,61017,0,1,10537.92488 +0,0,0,1,3721,61,71,18761,0,0,4274.035841 +0,1,0,0,32014,39,34,168779,1,1,22833.47745 +0,0,1,1,3085,10,11,17143,0,0,1725.572935 +0,0,1,1,7698,67,67,21786,0,1,4493.399538 +1,0,0,1,25514,16,18,79330,0,0,8614.599349 +0,0,0,0,80766,13,8,409216,0,1,32385.27363 +0,0,1,1,9472,23,15,70972,0,1,7159.863106 +0,0,1,1,14786,173,120,51263,0,1,9207.940664 +0,0,1,1,12143,16,10,84849,1,1,15010.17268 +0,0,1,1,34725,35,31,151778,0,1,15802.91663 +0,1,0,1,38681,25,17,234451,1,1,30319.0011 +1,0,0,1,89769,32,22,267633,1,1,38036.22259 +0,0,1,0,25641,13,11,69995,1,0,11522.14837 +1,1,0,1,44697,79,56,237676,1,0,25230.62232 +1,0,1,1,32141,13,7,126438,1,0,16778.87418 +1,1,0,1,22923,268,312,194055,1,1,43769.61503 +0,0,0,0,2684,77,58,17673,0,0,3511.944554 +0,0,0,1,9460,68,52,68510,0,0,5695.145297 +0,0,0,1,12360,14,14,67594,0,0,3685.758896 +1,1,0,1,53037,21,15,211044,1,0,24571.00018 +0,0,0,1,34956,153,147,142812,0,1,19931.75744 +1,0,1,1,20072,111,98,108150,0,1,19529.78425 +0,1,0,1,8014,109,120,52152,1,1,19136.5617 +0,0,1,1,18900,11,7,74524,0,0,4241.815778 +1,0,0,1,41220,50,44,275648,1,1,39463.25931 +0,0,0,1,12064,114,141,58648,0,0,7356.327876 +0,0,0,1,2348,66,61,11965,0,0,3747.519848 +0,0,1,1,50879,130,115,278253,1,0,25501.66844 +0,0,1,1,106257,60,42,273111,1,1,34219.61616 +0,0,1,1,19632,77,82,130469,1,0,15473.29918 +0,1,0,0,11690,19,15,111676,0,0,6369.082518 +0,1,0,1,32570,45,34,110579,1,0,14653.6345 +0,0,1,1,8791,34,27,22367,1,1,9900.519725 +0,1,0,0,16224,40,27,40810,1,0,10836.31695 +1,0,1,1,70877,37,38,182144,0,0,10354.31599 +0,1,0,0,43168,11,12,164172,1,0,17868.72587 +0,0,0,1,5976,33,30,22015,0,0,2909.544956 +0,1,0,0,37102,21,20,102313,0,1,13090.73802 +0,1,0,1,23036,43,49,80175,1,1,17671.33507 +1,1,0,1,23049,99,122,63075,1,1,23058.83592 +1,0,0,1,4835,60,44,21741,0,1,8089.685727 +1,0,1,1,12372,18,20,73617,0,0,7871.632576 +0,0,0,0,167714,32,19,532991,1,1,59494.49477 +0,0,0,0,23597,18,20,111714,0,1,10550.17236 +0,0,0,1,3226,66,47,29406,0,0,3692.980006 +0,0,1,0,32170,156,162,206448,0,0,13529.61913 +1,0,0,1,95361,130,121,325754,1,1,48460.80509 +0,0,0,0,64591,109,98,284399,1,0,23513.40276 +0,1,0,0,4407,72,49,20032,1,0,11115.66682 +1,1,0,1,18638,97,73,114884,1,1,26747.56274 +0,1,0,1,82656,32,27,226035,1,1,31697.45921 +0,0,1,0,9507,26,15,92186,1,0,11675.94125 +0,0,1,0,32765,53,56,91646,1,0,12127.16554 +0,1,0,1,9417,41,38,24161,1,1,13055.80096 +0,0,1,1,9982,115,131,96959,0,1,13698.22229 +0,0,1,0,38505,33,27,134791,0,0,4399.388273 +0,0,0,0,63185,43,28,213695,0,1,17689.83084 +0,1,0,0,16589,56,34,73505,1,0,12810.15442 +0,0,0,1,27012,20,24,73207,0,0,4864.281094 +0,0,1,1,9818,33,38,77656,0,0,6199.903732 +0,0,0,1,14712,31,35,40750,0,0,2730.122799 +0,0,1,1,21903,40,46,81450,1,0,13859.8355 +0,0,1,0,7400,69,60,28434,1,0,9762.730488 +1,1,0,0,11898,10,12,92882,1,0,15522.97777 +0,0,1,1,12334,115,91,84071,0,1,11690.24518 +1,0,1,1,25406,64,56,66919,0,1,14219.43138 +1,0,1,1,7389,14,11,53856,1,0,12486.7371 +1,1,0,0,86368,62,43,224519,1,1,35715.53248 +0,0,0,0,117187,20,19,324495,0,1,25494.5016 +0,1,0,0,33745,72,47,173644,1,0,18047.44692 +0,0,1,1,18768,104,108,78648,1,1,15877.81596 +0,1,0,0,2701,43,44,17103,1,0,10857.14235 +1,1,0,1,12999,20,23,98378,0,0,11892.40261 +0,1,0,1,19686,179,231,156726,1,1,33337.30767 +0,0,0,0,43841,77,49,161258,0,1,14596.43392 +0,0,0,1,69194,208,216,178354,1,0,23894.58049 +0,0,0,1,2474,33,34,24532,0,0,2858.632027 +0,1,0,1,14448,34,21,86961,0,0,5878.004417 +0,0,1,1,26173,42,25,99311,0,1,9894.79667 +0,0,1,0,2137,161,157,16364,1,0,11303.72537 +0,0,0,1,26067,135,131,155046,1,1,28146.75714 +1,0,1,1,11034,10,12,46612,0,1,8520.844793 +0,0,0,1,13843,132,135,92344,1,0,15489.22167 +0,0,1,0,13819,34,25,35881,1,1,8913.420934 +0,0,1,0,24105,23,27,65307,0,1,5669.662516 +0,0,1,0,54221,63,55,222019,1,1,28354.79066 +1,0,0,1,84700,200,190,377756,1,1,53778.42969 +0,1,0,0,2368,105,81,21188,0,1,9076.91555 +0,0,0,1,2363,27,20,10165,1,0,6417.365157 +0,1,0,1,70745,72,73,199052,1,1,30427.48377 +0,1,0,1,23133,17,17,116491,1,0,13790.5221 +0,1,0,1,8661,55,60,67170,0,0,8687.583676 +0,0,1,1,21920,55,51,82313,1,0,12226.4228 +0,0,1,1,7778,97,114,61796,1,0,14462.92373 +0,0,1,0,18925,52,41,105305,0,1,10454.9325 +0,0,1,1,4115,68,73,16836,0,0,5430.179286 +1,0,1,1,23519,71,43,126031,1,1,23700.03716 +0,0,1,1,63336,67,83,268036,1,1,37758.74083 +0,0,1,1,19677,50,34,102561,0,0,5443.590796 +1,1,0,1,16814,61,49,63214,1,1,20121.49264 +0,0,0,1,33617,21,22,135596,0,0,5190.790215 +0,0,1,1,42113,127,76,212139,1,1,27746.21581 +1,0,1,1,4631,317,250,25033,1,1,22180.12798 +1,0,1,1,86415,34,39,315105,1,1,44069.65938 +1,0,0,1,5281,86,104,33488,1,1,16338.14719 +1,0,0,0,3797,10,8,10273,0,0,5414.094946 +0,0,1,1,5677,17,15,23129,0,0,4098.004662 +0,1,0,1,7971,178,123,25390,0,0,8156.575502 +0,0,1,1,3632,69,56,24876,0,1,5957.777274 +0,0,1,0,27567,51,50,118976,1,0,15984.9995 +0,0,1,0,55529,35,35,175141,1,0,14314.20721 +1,0,1,0,27218,46,38,74856,1,0,13268.24686 +1,1,0,0,5909,127,115,21842,1,0,16346.18441 +1,0,1,1,23480,26,16,98484,1,0,16978.09629 +0,1,0,0,41426,120,110,150732,1,0,20274.3054 +1,0,1,1,3100,28,33,21758,0,0,9822.09884 +0,0,0,0,4836,50,53,19446,0,1,3234.454299 +0,0,0,0,1829,33,37,11692,0,0,374.4919543 +0,1,0,1,97609,16,16,251115,1,1,34324.18839 +0,0,1,1,57828,62,55,253448,1,1,34590.33593 +0,1,0,1,14845,42,28,109708,0,0,6290.856127 +0,0,1,0,24978,27,28,93186,0,1,8775.30422 +0,0,0,0,11430,96,72,83583,1,0,14556.84843 +0,0,1,0,8300,111,75,32700,0,1,5379.372413 +0,0,0,0,6110,29,22,48991,1,1,11711.37175 +0,1,0,0,12882,35,37,96742,0,1,11139.01919 +0,1,0,0,13966,132,136,47141,1,1,16030.57305 +1,0,1,1,47068,72,53,179251,0,1,20728.17565 +0,0,1,1,15723,11,8,61958,0,1,6943.252728 +1,0,1,1,49000,69,64,132441,0,0,11586.90069 +0,0,1,1,7533,68,73,31732,0,0,5844.905558 +0,0,1,0,36929,41,33,187615,1,0,15022.90035 +0,0,1,0,32258,129,104,101546,1,1,19915.63252 +0,0,0,0,30929,47,40,79995,0,1,7886.598072 +0,0,1,1,19383,144,183,95841,0,1,17049.23992 +0,0,1,1,44333,11,10,186906,0,0,6885.897147 +0,0,1,0,4121,24,23,26389,1,0,6905.691011 +0,0,1,0,10302,93,112,86224,1,1,17494.19729 +0,0,1,1,23366,117,111,126904,0,0,8984.496102 +1,0,0,1,5885,72,51,33055,0,0,8370.312256 +0,0,0,0,3411,61,56,17393,0,0,927.7440511 +0,0,1,1,37285,142,146,180782,1,1,29339.67577 +1,0,1,1,55002,22,15,319537,1,1,42297.84965 +1,0,1,0,26594,17,19,140264,1,1,24339.567 +0,0,1,0,3672,100,120,16333,1,0,10969.97368 +0,1,0,1,33596,23,23,104648,0,0,6289.555494 +0,0,1,1,7898,15,18,26614,0,1,3894.353505 +0,0,1,0,120053,16,20,348026,1,0,25019.12665 +1,0,0,1,26374,155,101,156262,1,0,21733.72894 +0,0,1,1,11169,74,59,39394,0,0,4933.718505 +0,0,1,0,10343,117,101,29140,0,0,4516.709131 +0,0,0,0,119811,74,79,338780,1,1,41893.04823 +0,0,1,1,55155,41,28,209808,1,1,28148.2893 +0,0,1,0,25486,43,37,200461,0,1,18717.08297 +0,0,0,1,17133,115,114,43800,1,0,13719.34886 +0,0,1,0,10986,22,25,55203,0,0,3877.036058 +0,0,1,1,3720,120,118,29719,1,1,14819.01866 +0,0,0,0,37833,220,204,101318,1,0,19501.87294 +1,1,0,0,47449,11,12,251510,1,1,34882.6487 +1,0,1,1,8760,131,84,81243,1,0,19114.5469 +1,0,1,0,35447,135,124,110438,0,0,12688.82126 +0,0,1,1,13150,56,43,70848,0,1,8498.405233 +0,0,0,1,47170,32,24,163505,1,1,23199.82368 +0,0,1,1,16848,30,24,131324,1,0,13032.75978 +1,0,0,1,27695,11,7,237921,0,1,25148.51695 +0,0,1,0,77625,74,94,512799,1,1,61842.53886 +0,0,0,1,134562,15,18,343625,1,1,40063.98894 +0,0,1,1,47989,152,179,255746,1,1,38496.75612 +0,0,1,1,4747,14,11,13521,0,0,2955.875518 +1,0,1,1,29087,61,45,97119,1,0,17603.9525 +1,0,1,0,59544,54,45,218979,0,1,25110.63315 +0,0,1,1,8135,85,74,58753,0,0,8419.202725 +0,0,0,1,85468,22,25,314297,1,1,38741.92019 +0,0,1,1,12137,62,59,101757,1,1,19272.74509 +0,0,1,0,33471,17,17,205233,1,1,27122.56479 +0,1,0,1,4448,22,24,13398,0,0,3398.11952 +0,0,0,1,41467,31,29,264171,1,1,32723.72263 +0,0,0,1,12207,99,59,60497,0,0,4879.220052 +1,0,0,1,63941,32,29,235279,1,1,34810.55066 +1,1,0,1,11578,41,33,32790,0,1,11304.1594 +1,0,1,0,169712,22,23,426030,0,0,18095.77891 +0,0,1,1,20110,49,42,105859,1,0,13418.95409 +0,0,0,1,84131,83,59,247807,0,0,11316.10913 +0,0,1,0,2335,79,102,15524,0,1,6788.539469 +0,0,1,1,22064,106,64,161425,1,1,26358.09252 +1,0,0,0,64113,46,48,204894,1,0,20667.64091 +0,0,1,0,50248,171,140,208390,1,1,32069.98303 +1,0,1,1,8338,63,79,48841,1,1,16647.35755 +0,0,0,1,19899,38,45,73329,0,0,5064.05926 +1,1,0,1,110002,264,226,275491,1,1,50307.33259 +0,0,1,1,95362,33,25,256642,0,1,23432.22518 +0,0,0,0,11397,26,31,30444,0,1,5145.141759 +1,0,0,1,43817,213,271,379290,1,1,60203.89043 +0,0,1,0,16580,88,56,99898,0,0,4183.562699 +0,0,0,0,12933,51,47,53317,1,1,11588.65023 +0,0,1,1,5449,20,19,15266,0,0,3658.278053 +1,1,0,1,100167,48,51,358940,1,1,52019.16027 +1,0,1,0,74555,22,17,226384,1,1,32896.6055 +0,0,0,0,47194,152,155,126111,1,0,17573.86443 +0,0,1,0,3926,55,54,10454,0,1,3504.503079 +1,1,0,1,104055,18,13,305435,1,1,43612.60606 +1,0,0,0,16056,28,28,49332,1,1,15206.90912 +0,0,1,1,54424,67,60,253921,1,0,21686.95205 +0,0,1,1,18775,26,27,124740,1,1,18199.69751 +0,0,1,1,14302,43,42,84235,0,0,6303.212166 +0,1,0,1,16525,50,41,91894,0,1,13907.25597 +0,1,0,0,9501,53,50,38192,1,0,10861.04743 +1,0,1,0,28947,146,134,89687,1,1,23753.70897 +0,0,0,0,38258,338,327,227586,1,0,30043.81505 +1,1,0,1,5411,110,101,30244,0,0,10369.50698 +0,0,0,1,26835,243,187,99838,1,0,19400.48493 +0,1,0,1,12232,75,49,38683,0,1,8939.53286 +1,0,1,0,3209,120,145,15965,0,0,11643.57355 +0,0,0,0,31661,127,157,117083,1,1,24038.8003 +0,0,0,1,11598,19,12,33497,1,1,10677.20017 +0,0,0,1,83791,18,22,273756,0,1,25331.78145 +1,0,0,0,50206,12,15,149350,1,0,17083.90622 +1,0,0,1,45543,10,10,140943,0,0,10211.61067 +0,0,0,1,27402,54,44,79984,0,0,3774.022142 +0,0,0,1,67216,13,13,369854,1,1,42810.83364 +0,0,1,1,35233,30,18,90039,1,0,10666.32347 +0,1,0,1,10080,21,18,79275,0,0,6123.187067 +1,0,0,0,15824,138,84,86526,1,0,17595.12246 +0,0,1,1,7156,44,55,24417,1,0,10982.72842 +0,0,1,1,22383,30,33,133443,1,0,13957.71172 +0,1,0,0,31957,21,26,151588,1,0,14916.32438 +0,0,1,1,7142,76,90,38019,0,1,8745.555866 +1,0,0,0,25718,135,137,69886,0,0,12375.25549 +0,0,1,1,25393,70,61,81306,0,1,9984.021432 +0,0,1,1,4272,244,278,18389,0,0,12912.11771 +1,0,0,0,14482,30,21,89187,1,1,19112.70034 +1,1,0,1,36515,27,31,91536,0,1,15703.48185 +0,0,1,1,83954,22,13,414028,1,1,48411.73584 +1,0,1,1,5359,24,18,17539,1,0,12403.82279 +0,0,0,0,25396,93,81,169520,1,1,24682.17914 +0,0,1,1,8218,93,103,21545,0,1,8013.702893 +1,0,1,1,20775,24,14,71600,0,0,8119.431132 +0,0,0,1,17762,243,237,70104,1,1,25916.50473 +0,0,0,0,49517,11,11,189560,1,1,23457.60884 +0,0,1,1,15628,25,15,71621,1,0,10537.24554 +0,0,1,1,13660,38,24,46070,0,0,5594.256812 +0,1,0,1,5732,36,46,27540,1,1,12872.95036 +1,1,0,1,10033,74,65,56750,1,0,16952.31794 +0,1,0,1,3970,12,10,21244,0,1,7616.587121 +1,0,0,1,54925,102,85,171154,1,0,22461.02533 +0,0,0,1,21284,37,22,103826,0,1,11795.08376 +0,0,0,1,12314,37,43,33933,0,0,4730.494451 +0,0,1,1,26324,170,133,74431,1,1,18602.41447 +0,1,0,0,77559,71,57,290795,1,1,37418.4234 +1,0,1,1,3976,42,25,14692,1,1,12010.44682 +0,0,1,1,65118,14,9,214925,1,1,29424.86975 +0,0,1,0,73434,11,10,254975,1,0,19171.56902 +0,0,1,1,52217,21,18,171420,1,1,24924.6775 +0,0,1,1,20795,15,14,61856,1,0,10738.01935 +0,0,1,1,10102,247,306,55864,0,0,16188.5893 +0,0,1,1,53540,99,80,184557,1,0,19937.30604 +0,0,1,1,3516,134,132,26005,1,0,12624.47883 +0,0,0,1,31130,75,52,138529,1,1,22232.06116 +0,0,1,1,49152,98,126,161032,1,1,29057.13155 +1,0,1,1,6429,34,42,47778,1,0,15575.59092 +0,0,0,1,24143,19,17,150038,1,1,22069.98835 +0,0,1,0,10347,177,154,29299,1,0,12110.95703 +0,0,1,1,25178,31,20,90695,0,1,10792.43108 +0,0,1,0,14148,33,23,48945,0,0,3046.58905 +0,1,0,0,113561,59,67,291874,1,1,38101.29937 +0,0,1,1,17240,17,16,62576,1,0,10924.6916 +0,0,1,1,3641,74,56,15051,1,0,12652.01364 +0,0,1,0,12569,47,60,61120,1,0,11758.11622 +0,0,1,1,74494,28,22,224557,1,0,21570.27881 +1,0,1,0,75145,13,13,436643,1,1,51481.47076 +1,0,1,1,31558,25,28,128626,1,1,25264.31538 +0,0,0,1,18540,30,27,72997,0,1,9055.466622 +0,1,0,0,34325,41,40,120679,0,0,8536.313565 +0,0,1,1,5720,57,61,26848,0,0,6769.439448 +0,1,0,1,12020,203,234,30600,0,0,12671.6971 +0,0,0,1,20576,66,50,152654,1,0,16145.62882 +0,1,0,1,16597,34,33,73011,0,0,6803.452299 +0,0,0,0,25021,39,33,159410,0,0,5687.746612 +1,0,1,1,41644,146,93,244406,1,1,37938.17821 +0,1,0,0,12911,49,62,39582,1,1,14715.84123 +0,0,1,0,24486,84,108,91734,1,0,13786.72061 +0,0,1,1,24412,80,51,124649,1,0,13916.59201 +0,0,1,1,35015,40,30,89385,1,1,17680.56379 +0,0,1,1,81286,11,13,308051,1,1,36980.83711 +0,1,0,1,36647,155,108,196940,0,1,23306.00006 +0,0,1,1,26933,101,76,123624,1,0,16293.66391 +0,0,0,0,32174,45,31,240885,1,1,31262.08172 +1,0,1,1,41344,41,49,115047,0,0,9719.121829 +0,0,1,1,4077,13,12,19584,1,0,9121.758337 +0,0,0,0,2000,27,24,13828,0,0,1950.003882 +0,0,0,1,19511,42,47,147592,1,1,21766.20787 +0,1,0,1,15166,203,125,42304,0,0,9369.386154 +1,0,1,0,33239,13,13,99772,1,1,19402.91401 +0,1,0,1,68209,50,59,180279,0,1,20803.41705 +0,0,1,0,4131,103,83,20340,1,1,10261.71063 +0,0,1,1,20910,134,170,143777,0,1,19449.43461 +0,1,0,1,4243,16,13,21518,1,0,12566.93159 +0,1,0,1,3108,110,70,17153,0,0,6432.405158 +0,1,0,0,31160,20,21,146114,0,0,6955.52316 +0,0,1,1,2924,71,56,21425,1,0,11287.25064 +0,0,1,1,6267,130,160,22855,0,0,8529.479435 +0,0,1,1,2398,49,34,23887,0,0,3459.079997 +0,0,1,1,38047,15,15,180869,1,0,14293.74411 +0,0,1,1,125515,34,42,621723,1,1,69502.99747 +1,1,0,1,47333,118,137,363714,1,0,35334.1679 +0,0,1,0,32360,50,50,178707,1,0,15949.85578 +1,1,0,0,49230,19,11,185925,0,0,13743.69156 +0,0,0,0,86318,27,24,415986,1,1,47684.93267 +0,0,0,1,23830,330,232,83006,0,1,17790.00111 +0,1,0,1,14024,49,31,111457,1,1,19360.29741 +0,0,1,0,45845,159,110,157802,1,0,18662.28483 +1,0,1,0,19093,29,27,189251,0,1,21412.61012 +0,0,1,1,4333,16,17,11813,1,0,7057.716994 +0,0,0,0,8334,113,110,59012,0,0,5815.313267 +0,0,1,0,7503,77,86,39893,0,1,6041.691655 +0,0,1,1,3344,37,35,20632,0,0,2487.28966 +0,0,1,1,8425,43,39,25626,0,1,4957.736644 +0,1,0,0,19158,18,14,80909,1,0,12110.20072 +0,0,1,0,19452,52,65,56500,1,0,9610.462044 +1,0,1,0,21621,213,133,68031,1,1,22110.9188 +0,0,1,1,27904,18,13,160297,0,0,8022.861962 +1,0,1,0,19168,31,30,146996,1,1,23680.3172 +0,0,0,1,19530,51,52,133215,0,1,12813.65007 +0,0,0,0,2295,87,85,11652,1,1,12866.98374 +0,0,0,1,10431,111,109,43425,1,1,14834.34432 +0,1,0,0,1914,130,129,18960,1,0,14030.81194 +0,1,0,1,11769,10,12,101790,1,0,13709.73817 +0,1,0,1,40599,11,13,391334,1,1,47007.10273 +0,0,1,1,3673,93,66,29982,0,1,5465.561091 +0,0,1,0,43813,300,291,117866,0,1,22034.16676 +0,0,0,1,24922,28,36,238370,1,1,30232.00281 +0,1,0,1,29650,75,58,129825,1,1,26170.72392 +0,0,1,1,57779,117,100,155279,0,1,16874.24697 +0,0,1,1,8710,18,19,37352,0,0,4343.695185 +0,0,1,0,12372,31,37,87831,1,0,10074.31003 +1,0,1,1,25210,152,93,82789,0,1,16246.96505 +1,0,0,1,35232,95,93,88361,0,0,11747.05758 +0,0,1,1,2910,28,22,19665,1,0,7535.963856 +0,0,0,1,4697,11,13,19975,0,0,3153.999581 +1,0,1,0,22028,139,142,60867,1,0,18548.92683 +1,0,1,1,20085,42,48,55217,0,0,8837.617126 +0,1,0,1,25176,16,11,75388,1,1,15671.0671 +0,0,0,1,55601,128,152,364693,1,1,48422.49158 +0,0,1,0,60042,34,42,180787,0,0,7457.534234 +0,0,0,1,43814,44,28,139225,0,1,14923.19864 +1,0,1,1,8175,175,174,57797,1,0,20795.30641 +1,0,1,1,20911,23,27,157897,1,0,18476.45679 +1,0,1,1,7020,92,57,36048,1,1,16736.85308 +0,1,0,0,24809,16,13,82969,1,1,18157.45591 +0,0,1,1,5037,27,22,44426,0,1,5106.006203 +1,1,0,1,8911,37,25,36052,1,0,15058.3902 +0,0,0,1,6184,66,52,34816,0,1,6351.385349 +0,0,1,0,54124,26,24,360237,1,1,41718.57662 +0,0,1,0,6964,12,8,25839,0,1,2562.555572 +0,0,1,1,37921,11,6,112185,0,1,9782.389138 +0,0,1,1,36046,94,91,123950,0,1,14914.87993 +1,0,1,0,20439,34,32,53451,1,0,13411.55529 +0,0,0,1,34471,70,77,112570,0,0,8116.201911 +0,0,1,1,26216,80,75,83602,1,0,14465.35069 +0,0,1,1,72410,113,98,213094,0,1,23650.45983 +0,0,0,1,24612,11,6,83804,1,1,14458.94926 +0,0,0,1,20156,50,44,105410,0,0,5360.756553 +0,0,0,1,65471,131,86,312961,1,1,41559.2825 +0,0,1,1,5085,52,64,19455,0,1,5690.371584 +0,0,0,1,26726,35,35,80233,1,1,15856.81648 +0,0,0,1,28145,100,91,75364,0,0,8782.850133 +1,0,0,1,6326,88,106,19067,0,0,11346.83188 +1,1,0,1,5595,200,168,32218,0,0,16131.02992 +0,0,1,1,8670,175,151,63222,0,1,13452.202 +0,0,0,1,48104,110,136,121870,0,1,16826.19827 +0,0,0,1,2715,50,52,18984,0,0,3276.441955 +0,0,1,1,7555,81,62,25721,0,0,5182.933638 +0,0,0,0,8202,52,50,37711,1,0,10393.92094 +1,0,1,0,60229,16,10,169389,0,1,17528.55139 +1,0,1,1,76500,16,11,303731,1,1,39653.45191 +0,0,1,1,187785,125,132,486587,1,1,60619.11284 +0,0,1,1,18389,15,14,84984,0,0,4554.40667 +0,1,0,1,6423,16,14,61674,0,0,7472.361427 +0,0,0,1,2961,42,34,10950,0,0,3248.018934 +0,0,1,0,44693,189,159,138383,0,1,20930.42881 +1,0,1,0,25553,146,118,72761,1,1,22416.93314 +0,0,1,0,12754,29,31,47655,0,1,5814.054048 +0,0,1,1,5905,15,16,26206,1,0,8830.422136 +0,0,1,1,16602,179,219,78992,0,0,12512.88604 +0,1,0,1,9423,39,35,64041,0,1,9039.266551 +0,0,0,1,18287,55,61,76185,1,1,15442.85173 +0,1,0,0,7816,18,15,25347,1,0,9855.571089 +1,1,0,1,2889,103,73,23204,0,0,10374.1152 +1,0,0,0,2238,31,25,17128,0,1,6439.1138 +0,0,1,0,17184,13,9,45179,0,0,3240.677761 +0,1,0,1,71207,22,15,324583,1,1,41789.21678 +1,1,0,1,95864,126,100,313896,1,1,48722.19318 +0,0,0,0,61565,88,82,155731,1,1,23979.2456 +0,0,1,0,52056,84,78,249586,0,1,24583.70491 +0,0,1,0,42209,287,368,132812,0,1,26763.89677 +0,0,1,1,30881,350,346,78447,0,0,19044.33861 +0,0,1,1,32098,76,47,94511,0,1,10933.44821 +0,0,0,0,125643,17,15,353921,1,1,40255.44235 +0,0,0,1,15553,40,39,102679,0,0,6468.444106 +0,1,0,1,27005,129,130,116103,1,0,18377.2377 +0,1,0,0,42988,167,172,206798,0,0,14047.61075 +0,0,0,0,7672,32,39,33964,0,1,3792.469079 +0,0,0,0,8166,36,33,35400,0,1,4509.107352 +1,0,1,0,31533,158,166,160557,1,1,31782.88533 +0,0,1,1,12678,82,51,51160,0,1,6702.151585 +0,1,0,1,10525,28,27,90436,0,1,11455.40518 +0,1,0,1,14493,111,83,47701,0,0,8361.661382 +0,0,1,0,9689,23,20,30535,1,0,7362.893813 +0,1,0,1,42165,35,43,182719,0,1,20467.30031 +0,0,1,1,37048,86,64,128094,0,0,8159.382761 +0,0,1,1,148792,28,31,387643,1,1,46328.9537 +0,1,0,1,22286,30,26,64435,1,1,17184.19603 +0,0,0,1,17740,58,55,60088,1,1,14089.96524 +0,0,0,1,9428,26,16,35205,1,1,11627.70748 +0,0,1,1,62294,142,179,236867,1,1,36651.53787 +0,0,1,1,26526,151,195,85307,1,0,16851.32486 +0,1,0,0,9974,36,35,26139,0,1,6011.221209 +0,0,1,1,16751,12,8,60287,0,0,2161.067162 +0,0,1,0,2017,16,11,10767,0,1,1075.306142 +0,0,1,0,29482,40,51,287594,0,1,26263.23327 +1,0,0,1,66115,14,11,175283,0,1,20758.60175 +0,0,0,0,18516,54,56,108860,1,0,12850.2833 +0,0,0,1,10060,158,145,35440,0,0,8283.110327 +0,0,1,1,3280,71,54,16764,0,0,5008.528603 +0,0,1,1,20957,178,196,70511,1,1,23263.80294 +1,0,1,1,11821,48,35,58965,0,0,8584.94953 +0,0,1,1,14031,12,14,62941,1,0,10127.11694 +0,0,1,1,52153,41,27,204358,0,0,9614.969945 +0,0,1,1,40238,184,122,355100,1,1,46075.22695 +1,0,1,1,5342,44,44,18610,0,1,8958.329522 +1,1,0,0,49513,35,34,273202,1,0,26057.50382 +0,0,1,1,36169,154,184,171836,0,1,22141.74528 +0,0,0,1,5712,10,12,30801,1,1,9849.800909 +0,0,1,1,14457,51,63,101818,0,0,7633.370285 +1,0,0,1,11786,24,28,69099,0,0,8669.574323 +0,0,1,0,9982,117,95,27981,0,1,7712.577363 +0,0,1,1,28616,32,36,111547,1,1,18512.21463 +0,0,0,1,8003,149,89,33333,0,1,5532.672742 +0,0,1,1,13539,24,22,65113,0,1,5200.339559 +0,0,1,1,21443,13,9,93531,0,0,4438.57358 +1,0,1,0,55566,71,51,380317,1,1,50858.10683 +0,0,1,0,2780,74,45,14419,0,0,2272.127562 +0,0,0,1,37114,36,43,157622,1,1,23926.54642 +0,0,1,0,6722,67,82,29573,1,1,11457.61484 +0,0,1,1,6531,56,47,36447,1,1,12740.23798 +0,0,1,0,11264,36,32,28892,0,1,4429.202694 +0,0,1,1,43847,19,19,157285,0,0,8283.264431 +0,0,1,1,5582,59,62,17240,0,0,6302.147101 +1,0,1,0,4154,37,26,14490,0,1,7030.601816 +0,0,0,1,85025,13,12,227981,1,1,28421.08444 +0,0,1,1,12019,58,68,44029,1,0,12317.583 +0,0,1,1,27506,188,135,219199,0,1,25131.74074 +0,0,1,1,64011,155,108,191203,1,1,31791.75098 +0,0,1,0,10387,130,123,60886,0,1,11196.20622 +0,0,1,1,54804,104,90,230590,1,0,22851.47202 +0,1,0,1,3739,136,100,10517,0,1,7861.118021 +0,0,1,0,43218,90,59,240968,1,1,31185.63017 +1,0,0,1,133727,58,57,343435,1,0,29186.81641 +0,0,0,1,2145,80,74,19592,0,1,7154.496475 +0,0,1,0,18340,19,19,46709,0,0,1788.257783 +0,0,1,1,40388,23,28,163068,1,1,22514.99273 +0,0,1,0,27683,14,8,79956,0,1,8201.242114 +1,0,0,0,37653,10,10,169230,0,0,9536.348658 +1,0,1,1,43133,88,89,126433,1,0,22077.31349 +0,0,0,0,4783,32,23,14964,0,1,2205.170846 +0,1,0,1,10860,46,53,31509,0,1,8237.521679 +0,0,1,1,14286,73,47,39297,0,0,2805.656965 +0,0,1,1,7035,24,28,27539,0,0,3451.888589 +0,0,1,1,185820,45,36,561249,1,0,38349.62419 +0,0,0,1,65137,122,147,237129,1,0,24103.33576 +0,0,0,0,11679,50,41,99386,1,1,16866.80194 +0,0,1,0,6399,25,21,16146,1,1,7812.085994 +0,0,1,1,72852,71,83,227646,1,1,32424.35195 +0,1,0,1,5047,24,29,28839,1,0,10910.47314 +0,0,0,1,6566,21,17,19778,0,0,1526.97737 +0,0,1,1,63185,282,292,163772,1,1,33763.74732 +0,0,1,1,1923,15,18,17196,0,0,3599.09488 +1,0,0,0,21061,23,24,134540,0,1,14828.91803 +0,0,0,1,6541,20,15,39541,0,0,2689.880991 +0,0,1,0,24886,34,35,98687,1,0,12058.38907 +0,0,0,1,7147,22,14,39237,0,0,3793.687727 +0,0,0,1,20846,64,56,112843,0,1,12762.26916 +1,0,1,1,23796,48,49,60288,1,0,14779.28191 +0,0,1,1,17231,58,48,74124,1,1,15862.94307 +0,1,0,1,17530,28,20,115727,0,1,13850.06656 +0,0,1,1,6434,20,20,36841,0,0,2466.731959 +0,0,1,0,12337,75,45,53062,1,0,10506.62169 +0,0,1,1,15110,22,15,44091,0,1,5042.730342 +0,0,1,0,14587,82,83,46146,0,1,8148.839687 +0,0,1,1,9466,51,64,44456,0,1,6819.859368 +0,0,0,1,10197,105,108,37298,0,1,8699.677047 +0,0,1,1,88324,20,21,248728,0,0,10088.00987 +0,0,0,1,12919,16,17,89955,0,1,9269.945628 +0,1,0,1,20893,47,50,66235,1,1,15915.97157 +0,0,0,1,34937,27,18,204745,1,1,27843.71872 +1,0,0,1,21028,64,55,118109,1,0,16707.76303 +1,0,1,1,35716,176,145,258150,1,1,43287.31095 +0,1,0,1,26653,10,12,196556,1,0,17609.17247 +0,0,1,1,21425,81,69,93682,0,0,5730.848545 +0,1,0,1,132165,128,117,369686,1,1,50130.92151 +0,1,0,1,10282,12,8,93054,1,0,12674.01825 +0,0,1,0,7932,68,61,64418,0,0,4660.333545 +0,0,1,1,41186,31,31,133654,0,1,13759.22737 +1,0,1,0,3082,33,27,30297,1,0,12782.91284 +0,1,0,0,44056,23,23,152713,1,1,24418.0332 +0,0,0,1,34057,38,27,119427,1,1,19079.72898 +0,0,1,1,3888,54,43,29769,0,1,6113.80667 +0,0,0,1,13275,80,54,41946,0,1,6875.8485 +0,0,1,0,73103,39,42,224032,1,1,28756.2661 +0,0,1,0,8505,62,55,29014,1,0,7046.27083 +0,0,1,1,18993,82,96,153617,1,0,17746.91304 +1,0,0,1,25259,50,47,105468,0,1,15388.45275 +1,0,0,1,5900,67,83,25413,0,0,9931.152373 +0,0,1,1,4799,15,13,25828,0,0,1808.608641 +1,0,1,0,6233,93,97,55279,0,0,8637.988017 +1,0,0,1,1855,82,76,13145,1,0,14090.36477 +0,0,0,1,16861,55,34,153278,0,0,6254.98617 +0,0,0,1,43118,32,35,127226,0,0,6983.619267 +0,0,1,0,23597,264,220,73249,1,1,22659.50419 +0,0,0,0,22245,12,10,96594,0,1,8108.028387 +0,0,0,0,12420,90,78,37446,0,0,4271.401033 +0,0,1,1,78109,13,12,232974,1,0,18953.51594 +0,0,1,1,24163,24,17,162345,1,1,21509.95807 +0,1,0,0,17457,23,19,57875,0,0,4471.087288 +1,0,1,1,16434,37,25,82730,1,0,16182.27909 +0,0,1,0,5115,19,12,17494,0,1,2799.743004 +0,0,0,0,13959,40,49,101399,0,0,4744.473057 +1,0,0,1,9149,75,71,53781,1,1,18086.028 +1,0,1,1,5180,105,90,13802,1,0,13681.09963 +0,0,1,1,14921,136,132,47549,0,0,9496.221711 +0,0,0,0,19206,22,22,67538,1,1,12374.67423 +0,0,1,1,4865,120,118,32082,0,0,7445.272025 +1,0,0,1,7157,57,68,17917,0,1,9655.977756 +0,0,1,1,55763,72,70,274217,1,1,36554.17464 +0,0,0,1,45926,32,25,121688,1,0,13102.90052 +0,0,0,0,12303,53,59,83824,1,1,15267.41038 +0,0,1,1,12864,15,17,64482,1,1,14042.46102 +0,0,0,0,15689,21,15,41964,0,0,1113.172781 +0,0,0,1,49295,85,58,131982,1,0,15754.24544 +1,0,0,1,10933,56,38,57713,0,1,10785.29754 +0,0,0,1,42488,74,55,143637,1,1,24193.11289 +0,0,1,1,17395,105,117,48496,0,1,9994.092275 +0,0,1,1,50126,34,43,125504,1,1,20932.81831 +1,0,0,1,19693,73,65,75585,1,0,16648.60247 +0,0,0,1,44522,88,104,216508,1,1,30794.48969 +0,0,1,1,71691,12,11,329923,1,1,38545.90173 +1,0,0,0,27011,34,29,96403,1,1,19490.37588 +1,0,1,1,41441,29,20,105035,0,0,10722.2157 +0,1,0,1,32052,51,44,101540,0,1,13513.02957 +0,0,1,0,10763,39,26,29301,0,1,4918.077477 +1,0,1,1,17253,62,72,48319,1,1,18683.07929 +0,0,1,1,48633,37,34,126911,0,0,5982.366767 +0,1,0,1,4028,46,31,22089,1,1,11764.24383 +0,1,0,1,30229,92,113,248850,1,1,37524.42834 +0,0,1,1,2029,13,12,18641,0,1,5965.460187 +1,0,1,0,19544,14,17,60230,1,0,12723.99488 +1,0,1,0,3438,110,108,31135,1,1,17993.10381 +0,0,0,1,38621,99,107,293510,1,1,39756.50128 +1,1,0,1,38283,73,48,121796,0,1,20599.22185 +1,0,1,1,4328,125,119,16090,0,0,9928.621027 +0,0,1,1,11517,33,22,55005,0,0,3714.651945 +0,1,0,1,2778,80,65,13682,0,0,5325.555068 +0,0,0,1,72346,14,10,194938,0,0,7866.984969 +0,0,1,1,37139,227,221,134594,1,1,29605.25558 +0,0,0,1,34975,16,15,140578,0,1,12305.625 +0,0,0,1,25050,34,36,70904,1,1,15090.02796 +1,0,0,1,17714,16,11,48556,0,0,6507.064194 +0,0,1,1,16104,16,12,47197,0,0,4627.528321 +0,0,1,1,26560,46,49,87562,0,0,6624.822918 +1,0,1,1,13485,16,11,69563,1,0,15812.20706 +0,1,0,1,71203,21,26,245123,0,1,25313.38013 +1,1,0,1,8016,61,46,25613,0,1,10625.00185 +0,1,0,1,7958,38,42,77649,1,1,17474.30546 +0,0,1,1,9141,65,40,59573,1,0,11450.96151 +0,0,1,0,73924,58,65,316663,0,0,13905.29862 +1,0,1,1,3798,11,11,15615,0,0,6929.75123 +0,0,0,1,47051,55,61,198478,1,1,28593.84247 +0,0,0,1,30457,39,39,77466,1,1,17704.23462 +1,0,1,1,12036,22,18,47870,1,0,12267.90934 +0,1,0,1,6063,45,31,17352,0,0,4157.376807 +0,1,0,0,16212,170,140,63835,0,0,11058.92666 +0,0,0,1,25627,68,64,73479,0,0,4728.372297 +0,0,1,1,2595,134,169,12752,0,1,9363.243188 +1,1,0,0,10994,19,21,44245,1,1,15004.20719 +0,0,1,1,11023,93,92,100300,1,0,12888.18501 +1,0,0,0,23399,82,87,63825,0,1,10876.15546 +0,1,0,1,66950,78,72,229300,0,1,26308.99261 +0,0,0,0,9442,41,45,35149,0,0,3897.27888 +1,0,1,1,7350,59,43,56526,0,1,11354.20289 +1,1,0,1,22699,21,25,89220,1,1,21847.66321 +0,0,0,1,15888,53,40,134756,1,1,20548.47139 +0,1,0,0,90030,71,56,227082,1,0,21740.35477 +1,0,0,1,15860,29,28,65533,1,0,15546.20046 +1,0,1,1,9837,87,99,49832,1,1,18958.14154 +0,0,0,1,16449,122,96,52638,1,1,16276.81724 +0,0,1,1,52276,23,15,175190,1,0,15262.2085 +0,0,0,1,30669,24,17,125355,1,1,20924.00185 +0,0,1,1,49274,40,41,146268,1,1,24373.30679 +0,1,0,0,9733,70,60,34800,1,0,11246.90037 +0,0,1,1,9333,71,65,29377,0,1,5236.862906 +0,0,1,1,91066,20,12,239951,1,1,31190.52552 +0,0,1,0,6268,210,222,25450,0,0,9725.253749 +0,1,0,1,31784,31,27,118649,1,1,23041.97953 +0,0,1,1,12565,49,59,101747,1,1,18578.82007 +0,0,0,1,5898,39,37,22433,0,1,4665.17618 +0,0,0,1,12432,21,17,68448,1,1,13605.75904 +0,0,1,1,24080,15,14,99400,1,0,13929.53007 +0,0,1,0,50785,23,16,170686,1,1,21878.81944 +1,0,1,1,46151,40,24,228243,1,1,34461.63062 +0,0,1,1,29833,19,16,200222,1,1,25888.90355 +0,0,0,1,6223,141,155,47986,1,0,15543.72503 +1,0,0,1,5389,21,22,46579,1,0,13139.07023 +0,1,0,1,9687,10,12,73454,0,0,5929.673647 +0,0,0,1,13982,181,186,68710,0,1,13725.05944 +0,0,1,0,28634,27,19,86259,1,1,14586.45649 +0,0,0,1,19698,223,194,68434,0,0,12146.46301 +1,1,0,1,48400,46,52,152151,1,1,29080.33788 +1,0,0,0,36848,95,89,154038,1,1,29094.56132 +1,1,0,1,18391,25,19,48419,0,0,9525.496591 +0,1,0,1,11356,26,25,33958,0,1,6615.064745 +0,0,0,0,4571,28,22,12955,0,1,889.9756528 +0,0,1,1,12171,12,13,42237,0,0,1408.456289 +0,0,1,1,3613,35,30,30750,0,1,6131.616399 +0,0,0,1,20584,41,49,52063,1,0,10898.83007 +0,0,1,0,5651,22,15,14223,0,0,1608.868027 +0,0,0,1,7610,14,16,19559,0,0,2836.787536 +0,0,1,1,19834,21,22,141636,1,1,21384.87729 +0,0,1,0,32146,78,61,88939,1,1,17804.1785 +0,1,0,1,12634,25,15,89284,1,1,18148.86182 +0,0,1,1,216847,58,59,576931,1,0,39189.79226 +1,0,1,1,8892,18,13,79262,0,0,5216.817245 +1,0,0,1,36354,47,34,334533,0,1,34620.77186 +0,0,0,0,15505,236,154,78785,0,0,10416.76761 +0,0,1,1,43928,14,9,142408,0,0,6604.038947 +0,0,1,0,5047,34,38,21189,0,0,1496.571961 +0,0,1,0,33099,32,21,159814,1,1,20189.74348 +0,0,0,0,9390,27,19,51588,0,1,5638.55887 +1,0,0,1,26849,82,89,100878,1,0,18010.79571 +0,0,1,0,5514,39,31,53677,0,1,7323.304046 +0,0,1,1,9182,25,26,25319,1,1,7832.328389 +0,1,0,1,90715,215,134,252573,0,1,30418.1706 +0,0,1,0,29291,22,25,219386,1,1,28408.8204 +1,0,1,1,2769,22,20,12621,0,1,7731.261504 +1,0,0,1,24130,39,39,238170,0,1,23439.61324 +0,0,1,0,22121,206,242,94992,1,0,21355.66519 +0,1,0,0,15892,79,49,48896,0,0,5184.767479 +0,0,0,0,14824,23,18,49678,1,0,5816.155015 +1,1,0,1,55185,114,146,195651,1,0,25214.32095 +0,1,0,1,4890,32,27,19932,0,0,4906.737437 +0,0,0,0,4574,45,56,11464,1,0,7700.722688 +0,0,1,0,32713,42,36,89221,0,1,6824.258074 +0,0,1,0,84172,83,82,289225,1,1,37057.45653 +0,1,0,1,18823,22,25,144786,1,0,16952.15638 +0,0,1,1,26311,26,15,106429,0,1,11563.57478 +0,0,1,1,14319,49,62,49910,0,0,4846.826271 +0,1,0,0,8243,53,67,20861,0,0,6473.5128 +0,0,1,1,2457,24,17,12147,0,0,1816.05151 +0,0,0,0,32519,23,14,121554,0,0,3944.826915 +0,0,1,1,14614,53,34,46570,1,0,9455.624525 +0,1,0,1,31770,123,117,79885,1,0,17573.13979 +0,0,1,1,26622,65,61,76943,0,1,10611.93383 +0,0,0,0,43574,26,25,122852,0,0,5382.789214 +0,0,1,1,20046,72,55,107275,1,0,12982.07293 +0,0,1,1,22681,56,44,71702,0,1,9683.228815 +0,0,1,1,33502,48,42,100847,0,1,12161.26982 +0,0,1,0,7014,12,7,48094,1,0,8736.408395 +0,0,1,1,31031,68,55,87512,0,0,5908.813014 +0,0,0,1,55835,47,31,339298,1,1,39957.12295 +0,1,0,1,26338,56,34,91201,1,0,13443.9427 +0,0,0,1,54085,18,15,164706,1,1,23309.57527 +0,0,1,0,4195,78,51,11390,1,0,7885.634278 +0,0,0,1,7331,11,11,21523,0,1,3581.785919 +1,1,0,1,7216,39,33,45321,1,0,15447.03202 +0,0,1,0,23982,64,47,60092,1,1,12370.46019 +0,0,1,1,6898,77,86,58994,0,1,10328.88638 +0,0,1,1,10665,55,69,64120,1,0,11766.00932 +0,0,1,0,63286,38,42,160582,0,1,16046.82812 +0,0,0,1,14194,34,26,54669,0,1,7219.732899 +0,0,0,1,12785,77,98,38887,1,1,14129.6421 +1,1,0,1,13980,26,18,38892,0,0,9816.213152 +0,0,1,1,36150,12,12,115412,0,1,10856.31406 +0,0,1,1,17826,63,52,50404,1,0,12470.55267 +1,0,1,0,16838,59,56,64514,0,0,9496.053381 +0,0,1,0,18778,35,31,55095,1,1,12093.38079 +0,0,1,0,4459,55,63,25729,1,0,9576.578823 +0,0,0,0,135692,178,213,618047,1,1,77528.81537 +0,0,1,1,3675,26,17,25096,1,1,9942.700669 +0,0,1,0,42914,14,15,254051,1,1,31201.35727 +0,0,0,0,108334,218,192,291538,1,1,43629.75392 +0,0,0,0,2444,27,28,15688,1,0,7456.745373 +0,1,0,1,24130,26,31,86989,0,0,7454.659858 +1,0,0,1,42664,41,35,134974,1,0,18862.37313 +0,0,0,0,9113,59,61,52406,0,0,5423.927958 +0,0,0,1,11283,55,50,70135,0,0,6679.766799 +0,0,1,1,30243,169,203,247768,1,0,28086.67604 +0,1,0,1,15694,24,30,84643,1,0,12468.53816 +0,0,1,1,31333,42,36,94666,0,0,5333.651027 +1,0,1,1,11357,59,55,75101,0,0,10147.14027 +1,0,0,0,10117,20,22,31883,1,0,11395.4986 +0,0,1,1,11262,17,18,39822,0,1,6271.528516 +1,0,1,0,33583,85,77,161560,1,1,29537.65233 +0,0,0,1,8268,121,142,29476,1,0,13640.71751 +0,0,0,1,9502,15,17,92715,1,1,15155.16726 +1,1,0,1,41557,114,98,332003,1,1,48728.22763 +0,0,0,1,3933,24,27,10768,1,0,8371.741955 +0,0,1,0,18772,52,43,51145,1,0,10488.15887 +0,0,0,1,50921,20,21,188000,0,1,15941.99873 +0,1,0,1,8786,40,37,68130,0,0,5894.35398 +0,0,0,0,59529,24,15,181704,1,0,14601.54169 +0,0,1,0,6277,20,12,30417,0,0,1835.451631 +0,0,0,1,60133,129,160,172913,1,1,32519.54847 +0,1,0,1,34596,42,42,105828,0,1,13968.27771 +0,0,0,1,12412,26,26,44954,0,0,4081.900584 +0,0,1,0,22916,77,58,62639,0,0,4171.2487 +1,1,0,1,32039,11,12,166808,1,0,21418.21206 +0,1,0,1,7566,103,74,64992,1,1,16998.38856 +0,0,1,1,4319,24,16,22989,1,1,7655.750618 +0,0,1,1,31472,51,38,125245,1,1,21036.03817 +0,1,0,0,41368,70,79,188113,0,0,10174.99537 +0,0,1,1,59887,24,14,176089,1,1,24340.71476 +0,0,0,1,11400,36,27,96026,0,0,5364.934704 +1,1,0,0,5190,18,22,23923,0,0,7416.942714 +0,0,1,0,21753,12,10,57036,0,1,5541.2102 +0,1,0,1,31244,33,28,161287,1,0,17158.78318 +0,1,0,1,14349,57,42,70116,1,1,16646.46136 +0,0,1,0,11225,72,57,45233,0,1,6225.88462 +0,0,0,0,58172,32,22,256127,1,1,32063.72955 +1,1,0,1,8119,71,75,47949,0,0,10124.27551 +0,0,1,1,8935,75,84,34192,0,0,6418.173838 +0,0,1,0,5955,11,13,56655,1,0,9543.824782 +0,1,0,1,21769,25,25,115008,0,0,7619.890571 +0,1,0,0,54710,57,37,145035,1,1,23480.37139 +1,0,1,0,20201,15,17,62075,0,0,5279.11638 +0,1,0,1,13168,24,16,70591,1,1,14673.046 +0,0,1,1,40516,43,45,142076,1,0,15228.48508 +1,0,1,0,10601,125,95,30445,1,0,15968.83095 +0,1,0,1,13643,63,44,89773,1,1,19043.65555 +0,0,0,1,17353,14,8,46146,0,1,6127.903977 +0,0,0,1,35304,15,9,123038,0,1,11212.04662 +0,1,0,1,29430,12,7,77690,0,0,5465.409643 +0,0,1,1,3181,66,52,23386,0,1,4155.17803 +0,0,0,0,58779,87,54,298788,1,1,39230.68034 +0,0,1,0,13745,29,32,52031,1,0,8031.738977 +1,0,1,1,12862,30,21,45223,0,1,10476.25359 +1,0,0,0,33526,15,17,112292,0,1,14707.84229 +0,0,0,1,22332,68,66,70057,1,0,13970.97992 +0,1,0,1,10233,51,65,26333,0,0,5677.481518 +1,0,1,0,92769,37,35,266060,1,0,22936.35093 +0,0,1,0,16204,56,68,44187,1,1,13024.00882 +0,0,0,1,7781,67,53,20585,1,0,10420.06826 +0,0,0,1,77157,66,47,260624,0,0,11088.22641 +0,0,1,1,2020,114,125,11821,0,0,7004.24437 +1,0,1,0,4208,23,14,25922,0,0,6203.790479 +0,0,1,1,38290,121,105,193140,1,0,20883.98191 +1,0,1,1,33872,26,17,85308,0,0,8423.4853 +0,0,0,1,72987,54,48,257270,1,1,33902.75033 +0,0,1,0,6755,42,35,48614,1,0,9096.476076 +0,1,0,1,22843,14,16,127307,1,0,15132.06439 +0,0,0,0,24694,49,53,63961,0,0,4710.660484 +0,0,0,1,7651,38,46,55372,0,1,7548.081392 +0,0,1,1,19961,146,128,87993,0,0,10474.25199 +0,0,1,1,19379,127,84,68897,1,1,17654.38141 +1,0,1,0,92517,107,89,525030,1,1,65050.49043 +0,0,1,1,24728,210,247,143155,1,1,32194.46318 +0,0,1,0,43604,20,15,193262,0,0,6513.837374 +0,0,1,1,3718,87,69,12453,1,0,10546.46439 +0,0,1,1,2656,79,100,15633,0,0,6646.573074 +0,0,1,1,13651,65,59,43059,0,0,4597.490173 +0,0,0,1,17527,20,20,115576,0,1,11554.45132 +0,1,0,0,62843,110,133,313708,0,1,34085.18712 +1,1,0,1,35866,28,32,125668,1,1,25430.41466 +0,0,1,0,47793,35,28,149714,0,1,13584.77301 +0,0,1,1,11016,58,55,35876,1,1,11025.45128 +0,0,1,0,4853,73,46,19814,1,0,9097.568257 +0,0,0,1,56856,25,16,143099,1,0,15055.79704 +1,0,1,0,22924,18,19,140933,1,0,17543.84502 +0,0,0,1,16492,57,52,105671,0,1,13203.11481 +0,1,0,1,3837,14,17,14864,0,1,4378.016683 +0,0,1,0,5218,15,10,42069,0,0,1439.392273 +0,0,1,1,2236,13,14,15777,0,0,1983.398267 +0,0,1,1,14765,57,65,62142,0,1,9307.400678 +0,0,0,0,16950,101,93,131327,1,0,15252.16749 +0,0,1,1,19100,134,97,80943,0,0,5675.826269 +0,0,1,1,110805,38,25,478518,0,1,41818.39213 +0,1,0,1,2410,27,23,19804,0,0,3395.495012 +1,0,0,1,43058,83,94,175626,1,1,32215.16098 +1,0,0,1,10751,17,10,37642,0,0,5915.114035 +0,0,1,0,15375,25,32,58423,1,0,9731.674473 +1,0,0,1,9136,39,24,77650,1,1,18608.01124 +0,0,0,1,45636,190,214,138040,0,1,21710.83319 +0,0,0,0,17132,22,13,56766,1,0,7688.134548 +0,0,1,0,4451,19,22,15751,0,0,2342.172593 +0,1,0,1,21171,24,18,68592,1,1,16261.05254 +0,0,0,1,53513,35,21,171389,1,0,15341.2549 +0,0,0,1,65868,20,17,202041,1,1,27864.98293 +0,0,0,1,12421,100,81,94980,1,1,17726.35317 +0,0,1,1,44950,183,233,120480,0,1,19661.73817 +0,0,1,0,23056,34,29,191204,1,0,17610.66107 +0,0,0,1,12015,104,131,74728,0,1,12023.65435 +0,1,0,1,39637,17,21,309604,1,1,37891.172 +0,1,0,0,32529,49,37,90631,0,0,6647.296616 +0,0,0,1,77350,15,12,303470,1,1,35467.78765 +0,0,1,1,61039,66,43,196930,1,1,28597.51471 +0,0,0,1,16226,90,72,54075,0,0,7220.87191 +0,0,1,0,12813,158,188,97736,1,1,22883.8854 +0,0,1,1,52575,153,155,143587,0,1,21617.33461 +1,0,0,1,9349,34,20,39847,0,1,9099.395522 +0,1,0,1,32920,10,11,192001,1,1,27934.92302 +0,0,1,1,7853,94,92,52369,1,1,14899.93712 +0,0,0,1,2740,44,55,10867,1,0,9150.068397 +0,0,0,0,115796,13,8,418788,1,1,46668.59688 +0,0,1,1,40637,85,100,243944,1,1,33519.77889 +0,0,1,1,2964,43,42,26090,0,1,6237.467069 +0,0,1,1,29107,13,12,90923,1,0,12351.10675 +0,1,0,1,8140,16,11,70111,0,0,6673.257513 +0,0,0,0,18938,35,29,54633,0,1,5766.333262 +0,0,1,1,1755,51,65,12679,1,0,8678.13295 +0,0,0,1,86106,52,35,267114,0,0,8834.50154 +0,0,1,0,94521,72,92,301844,0,1,28800.33804 +0,1,0,1,5344,147,137,17498,0,0,9284.071213 +1,0,1,1,11215,33,22,37339,0,1,9379.400485 +0,0,0,0,8397,76,86,78647,1,1,16291.33259 +0,0,1,1,51343,32,36,159946,0,0,7940.947136 +0,1,0,0,13017,118,84,54033,1,1,15732.86598 +0,0,1,0,13512,16,16,34524,0,0,1657.60029 +0,0,0,0,5153,86,53,24055,0,0,1768.879168 +1,1,0,1,32540,14,12,200125,0,1,21874.94337 +0,0,1,1,87157,33,36,236683,1,0,20887.12542 +0,1,0,1,95183,29,33,281831,0,0,15365.64843 +0,0,1,0,35818,55,70,286403,1,1,35776.06122 +0,0,0,1,40342,53,60,124441,0,1,14019.02331 +0,0,1,1,7565,17,17,21557,0,0,2572.29145 +0,0,1,1,39299,30,25,121658,1,0,13440.23615 +0,1,0,0,3695,52,57,27252,0,0,6427.628148 +0,0,1,1,34997,76,82,124451,0,0,6864.032046 +1,0,1,1,2228,10,10,22187,0,0,6828.300545 +0,0,1,0,34935,37,24,312042,1,1,37676.723 +0,0,1,0,11982,52,40,71221,0,1,8315.512754 +1,0,1,1,15945,114,109,52711,0,1,14559.37835 +0,0,0,1,22829,19,21,122986,0,0,5520.08532 +0,0,0,1,41241,53,61,218244,0,0,10153.0231 +0,0,0,1,93644,18,20,369755,1,1,45167.61109 +0,0,1,1,26096,31,40,112556,0,0,7420.574514 +0,0,1,1,33283,37,47,113427,0,0,8036.636786 +0,0,0,0,12263,64,53,76953,0,1,9011.419677 +0,0,1,1,44908,38,49,171011,1,1,25780.19642 +0,0,1,1,63404,35,25,186296,1,1,26795.14017 +0,1,0,1,29286,40,37,80408,1,1,17264.45423 +0,0,1,0,50350,17,17,170110,1,1,22058.75821 +1,1,0,1,18843,35,41,112058,1,0,18925.58012 +0,0,1,0,41753,21,22,174845,1,0,16353.77671 +0,0,1,1,12170,26,16,34745,1,0,9797.931115 +0,0,1,1,8425,21,24,69801,1,0,11471.60871 +0,0,1,1,89351,42,49,235865,1,0,20195.00107 +0,0,1,1,40103,34,41,262309,1,0,19585.5975 +0,0,1,1,3153,57,43,10245,0,0,4387.135137 +0,1,0,1,11462,204,204,35991,0,0,13619.82974 +0,0,1,1,10851,72,44,28921,1,1,12724.2847 +0,1,0,1,3446,77,52,31509,0,0,5947.278689 +0,0,0,1,5816,25,31,25646,1,0,9107.229617 +0,0,1,1,3757,72,62,21188,0,0,6550.153588 +1,0,1,1,12841,158,149,86687,0,0,14162.78827 +0,1,0,0,26798,30,24,98948,0,0,7180.914803 +0,0,1,1,66632,74,76,193061,0,1,20889.74858 +1,0,0,1,7182,27,23,23792,1,1,13954.74776 +0,0,0,1,4857,240,196,27719,1,1,17028.09659 +1,0,1,1,26045,92,95,72144,1,0,17047.86739 +0,1,0,0,13861,35,23,34800,1,0,9965.380853 +1,0,1,0,8576,84,94,27701,1,0,16472.20159 +0,0,0,0,11033,26,30,60672,0,1,5748.774662 +1,0,1,0,35919,24,29,97257,1,1,21762.75624 +0,0,1,0,3726,173,106,31027,0,1,7139.476089 +0,0,1,1,14114,29,30,56959,0,1,7991.801988 +0,0,0,1,4919,51,44,38379,0,0,3672.918732 +0,0,0,1,30702,153,145,95179,1,1,21795.06614 +0,0,0,1,16510,184,219,46455,0,1,13878.99218 +0,0,1,1,7157,16,14,69576,1,1,14189.7407 +0,0,1,1,2284,122,158,12521,0,1,10381.90741 +0,0,1,0,47012,38,39,143772,0,0,6887.731045 +0,0,1,0,73855,141,139,188319,1,1,30681.06136 +0,1,0,1,9381,83,85,42495,1,0,13716.10988 +0,0,0,1,38673,28,30,142276,1,1,21574.47709 +0,0,0,0,15243,26,32,38690,1,1,10110.00069 +0,1,0,1,8698,22,23,46078,1,1,14498.91261 +0,0,1,1,7373,33,40,24073,0,0,2680.364289 +0,0,1,1,4251,33,29,38335,0,0,5480.749596 +0,1,0,1,34672,129,160,126165,1,0,19823.71177 +1,0,0,1,103613,74,52,259317,0,1,29737.78514 +0,0,0,1,23857,30,23,73164,1,1,14328.8149 +0,0,0,1,8915,38,27,59774,0,0,5781.273289 +0,0,1,1,19374,39,34,66436,1,0,10420.16974 +0,0,1,1,18630,266,325,100278,0,1,23166.06052 +0,0,1,1,4127,32,31,15569,0,1,2921.20266 +0,1,0,1,8279,38,30,52969,0,1,10113.77289 +1,0,1,1,6508,33,39,38371,0,1,10330.24255 +0,1,0,0,43287,26,33,207662,1,0,19666.70339 +0,0,0,0,65671,13,14,171025,1,0,15897.04316 +0,0,1,0,62591,57,59,205256,1,1,27906.4413 +0,1,0,1,21605,70,52,59139,0,0,5977.569192 +0,0,1,1,4809,21,26,34871,0,1,5239.444732 +0,0,1,1,75639,30,19,210431,1,0,19023.10679 +0,1,0,1,20232,29,27,92846,0,0,6904.359815 +0,1,0,1,3315,67,66,11681,0,0,5434.443462 +0,1,0,0,42932,23,15,125437,1,0,15123.5866 +0,0,0,0,1640,15,11,11267,0,0,-616.5724513 +0,0,0,1,56765,54,53,160784,1,1,23112.51345 +0,0,1,1,4644,56,49,45005,0,0,5500.479797 +0,0,0,1,6217,56,41,36316,1,0,7328.944312 +0,1,0,1,10443,60,68,99629,1,1,19656.17731 +0,0,1,1,17254,49,62,46979,0,1,8632.529171 +0,0,1,1,10801,80,98,32666,1,0,12627.10734 +0,0,1,1,19955,30,26,60593,0,1,7854.669318 +1,0,0,1,7690,20,20,22101,1,0,13912.19876 +0,0,0,0,35600,66,76,171952,0,1,17500.87214 +1,0,0,0,63483,142,92,204411,1,1,33250.97377 +1,1,0,1,17853,94,94,84566,0,1,17705.53082 +0,0,1,0,4721,24,14,17807,1,0,6322.44198 +0,0,1,0,11592,59,65,77087,0,0,4565.842911 +0,1,0,1,133052,36,40,399228,1,0,28104.52976 +0,0,0,0,49286,22,14,150010,1,1,20132.08548 +1,0,1,1,29684,21,15,160862,1,1,26783.82687 +0,0,1,1,8922,92,65,62289,1,1,15612.3162 +0,0,1,1,64836,112,127,197133,1,1,31799.31029 +1,1,0,1,46758,91,96,170928,1,1,33713.93212 +0,0,1,1,50599,107,67,229859,1,1,33989.91842 +0,0,0,1,5394,13,12,30169,1,0,7777.676957 +0,0,1,0,2115,16,17,11709,0,1,2117.546395 +0,0,1,0,19769,197,231,55240,1,0,17419.95201 +0,0,0,0,54806,123,145,283472,0,1,28592.42148 +0,0,1,0,59798,178,203,558513,1,1,70110.41759 +1,1,0,0,24172,70,72,81282,1,1,22154.12055 +0,0,1,1,10226,10,7,92253,0,1,8899.070061 +0,1,0,1,3121,16,16,14226,1,1,11642.06011 +0,0,0,1,4568,21,14,14755,1,0,5933.833852 +0,1,0,1,15788,42,44,97815,0,1,13636.95272 +0,1,0,1,13014,45,37,93611,0,1,12127.64035 +0,0,1,1,6018,27,20,19427,1,0,8389.650104 +0,0,0,1,84149,71,88,310718,1,1,38438.5399 +0,0,1,0,32655,36,44,135278,1,1,21340.90633 +1,0,1,0,18844,70,90,55184,0,1,13191.2218 +0,1,0,1,58594,55,62,490270,1,1,58714.84971 +0,0,0,1,10630,23,27,55771,0,0,3980.851169 +0,0,0,1,8220,19,15,72357,1,0,10492.48497 +0,0,1,1,9317,211,254,39983,0,0,14542.64742 +1,0,1,0,28734,48,38,269252,1,1,37507.32502 +1,0,0,0,12037,42,42,61609,0,0,6937.60052 +0,0,0,1,49427,28,33,288358,1,1,35979.81746 +1,0,0,1,5752,37,46,54873,0,0,6987.138356 +1,0,0,1,7096,91,86,65357,0,0,11234.4588 +0,1,0,1,13359,18,12,77553,1,0,11368.25645 +1,0,0,0,21722,59,48,136034,0,1,15218.78818 +0,0,1,0,24113,27,27,160121,1,0,13816.97789 +0,0,1,1,17293,29,37,80727,0,0,7133.681372 +0,0,0,0,12688,11,10,42969,0,1,2549.01298 +0,0,1,0,28207,125,95,76872,1,1,18576.01452 +0,0,1,1,71943,18,14,248566,0,1,20998.0432 +1,0,0,0,84320,54,65,225946,0,1,23284.55072 +0,0,1,1,22042,14,14,68408,1,0,9544.957874 +0,1,0,0,18815,80,81,61704,0,0,7972.853529 +0,0,1,0,10897,24,28,74510,0,1,7692.030915 +0,0,1,0,8332,21,13,48657,1,0,7872.254385 +1,0,1,0,60860,227,214,165733,1,0,26875.78401 +0,0,0,0,23524,92,111,216948,1,1,30042.24231 +0,0,1,0,1501,22,18,12243,0,0,3118.114431 +0,0,1,1,12010,31,30,42780,0,1,7446.547831 +0,0,0,1,59331,323,338,260230,1,1,46530.42113 +0,0,1,0,31294,17,14,100087,1,0,9617.575449 +0,0,0,1,43525,79,101,201749,1,1,32638.07335 +1,0,1,0,21105,34,21,146090,1,1,25425.5671 +0,1,0,1,42867,48,56,191941,1,1,29134.66939 +0,1,0,0,12838,68,41,48972,1,0,14081.51429 +0,1,0,0,21339,20,25,98120,1,0,12868.98743 +0,1,0,1,36471,16,13,101443,1,1,18642.82558 +0,1,0,1,38793,139,165,192679,1,1,34058.60358 +0,0,0,1,112566,89,97,306079,1,1,38693.56767 +0,1,0,1,1727,65,42,14266,0,1,4852.886305 +0,0,1,1,15814,32,35,79801,0,0,6464.14138 +0,1,0,1,54198,21,22,276950,1,0,24003.60504 +1,0,1,0,6176,77,88,24603,1,1,16244.06651 +0,0,1,1,75071,71,89,369362,1,1,49636.20396 +1,1,0,1,17200,29,24,163971,1,0,21486.33935 +1,0,0,1,8656,28,22,43930,1,0,13369.71389 +0,1,0,0,1883,30,34,11621,1,1,10157.94009 +0,0,0,1,45309,39,43,137828,1,1,20308.17965 +0,0,0,1,15369,26,16,61966,0,1,7223.827695 +0,0,0,1,107765,163,155,357482,0,1,36079.39157 +0,0,1,1,28942,109,125,146101,0,0,9826.859963 +0,0,0,1,4176,76,72,24504,0,0,4875.567785 +0,0,1,1,11363,49,52,105082,0,1,11544.48893 +0,0,1,1,12749,22,26,75798,0,0,4021.204818 +1,0,1,1,37759,20,15,136223,0,1,16741.71293 +1,1,0,1,45201,74,78,158032,1,1,31342.41506 +1,0,1,1,23587,152,104,103485,0,0,13710.65014 +1,1,0,1,12588,234,271,41540,0,0,21326.34189 +0,0,0,0,76824,88,99,556966,1,1,64641.33112 +1,0,1,1,8236,14,15,26162,0,1,8053.329576 +0,0,0,0,4874,22,15,32888,0,1,3085.171282 +0,0,1,1,16975,17,13,104309,1,0,10897.16033 +0,0,1,1,27236,10,7,90537,1,0,11655.52611 +0,0,1,0,12979,14,16,43358,0,1,3744.097328 +0,0,0,1,6638,21,21,29166,0,1,3814.303434 +0,0,1,0,38213,40,28,100002,0,1,9744.287477 +1,0,1,1,52165,78,53,316043,1,1,43156.33691 +0,1,0,1,4837,74,80,45004,1,1,16722.52173 +0,1,0,1,46825,37,35,239934,1,0,20709.49016 +0,1,0,1,62909,15,19,185341,0,0,11098.19468 +0,0,0,0,34929,25,23,98476,0,1,9266.628165 +0,0,1,0,49604,18,14,216230,1,1,28216.15072 +0,0,0,1,9678,73,47,24449,0,0,3398.862946 +0,0,1,0,38592,226,198,115829,1,0,19909.6354 +0,0,0,1,60156,30,18,273062,0,1,23779.99628 +0,0,0,1,10476,24,23,47249,0,1,7022.488822 +0,0,0,1,86093,65,75,285143,1,1,38203.11784 +0,0,0,1,7414,32,33,34819,1,1,9752.831101 +0,0,1,1,45546,41,50,128179,0,0,10259.69591 +0,0,1,1,16846,84,87,49484,0,1,10842.52317 +0,0,1,1,11467,100,121,31288,0,0,7444.036432 +0,0,1,1,11169,11,9,42955,1,0,8119.356813 +0,0,0,1,14193,38,35,75949,0,0,5396.306639 +0,0,0,1,20864,54,66,61250,0,0,5077.933824 +0,1,0,0,53006,26,33,262468,1,1,34569.82493 +0,0,0,0,19280,59,59,56606,0,0,4302.232812 +0,0,1,1,15826,148,97,45361,1,1,16062.76019 +1,0,1,1,4897,55,37,12435,0,0,8105.305773 +0,0,0,1,17992,57,36,68079,0,0,3393.358658 +0,0,0,1,55622,57,73,278418,1,1,36875.60652 +0,0,1,0,43553,36,26,114094,1,1,16561.71637 +0,0,0,0,1959,43,40,14795,1,0,7434.790067 +0,0,1,1,28795,23,27,162999,0,1,16569.3359 +0,0,1,1,21700,51,56,182905,0,0,6948.191324 +0,0,1,1,15220,283,188,43339,1,0,16128.40668 +0,1,0,1,6288,12,7,35941,1,0,9764.999051 +0,0,0,1,4131,60,69,28178,0,0,4651.985014 +0,1,0,1,12338,14,16,60982,1,1,14309.01002 +0,0,1,1,9858,13,9,65645,1,1,11808.9937 +0,1,0,0,49639,34,39,236428,1,0,22720.78752 +0,0,0,1,46007,38,34,151678,0,1,15213.31743 +0,1,0,1,8458,56,63,29407,0,1,8268.107679 +1,0,1,0,31635,88,100,96045,0,0,11546.25512 +0,0,1,0,20130,35,34,180710,1,0,17637.81626 +0,0,0,1,22448,108,65,85971,1,0,12121.85281 +0,0,1,0,9400,214,225,38115,0,1,13017.22591 +1,0,1,1,125862,30,35,349699,0,1,34921.74153 +0,0,1,0,3029,75,69,13965,1,0,9034.125983 +0,0,1,0,3899,141,150,14010,1,0,10357.27798 +0,0,1,0,7801,76,50,51789,1,1,12466.48763 +0,0,1,1,8357,34,26,24866,1,0,8358.920998 +0,0,1,0,12245,16,16,70499,1,1,13222.14717 +0,1,0,1,37314,44,56,189686,0,0,13143.71507 +0,0,0,1,6231,10,9,16430,0,0,1299.102236 +0,0,1,0,17341,17,17,47528,0,0,3814.032543 +0,0,1,1,7188,53,34,26282,0,1,4400.598975 +1,0,1,1,2161,109,75,15424,0,0,7953.444885 +1,0,1,1,17351,16,12,50651,0,1,10928.05785 +1,0,1,1,50693,75,74,221486,1,1,36120.18731 +0,0,1,1,57128,150,99,158373,1,1,28399.87285 +0,0,0,1,27799,45,41,97169,1,1,18611.64529 +0,1,0,1,15352,50,60,136254,0,0,9350.540121 +0,0,0,1,1903,39,30,13700,0,1,5861.649012 +1,1,0,0,34179,21,18,143612,0,1,17312.76386 +0,0,1,1,11925,99,83,58553,0,1,9550.739313 +0,1,0,0,15724,20,17,39611,1,0,9581.700883 +0,1,0,0,73832,60,38,287082,1,1,37591.60621 +0,0,0,0,53577,16,14,152576,0,0,4783.476068 +0,0,1,1,18461,25,28,52829,1,0,9443.426405 +0,0,1,1,20166,62,70,59649,1,0,12419.66799 +0,0,1,1,94161,41,24,276626,1,0,21897.14378 +0,1,0,1,259808,58,38,766485,1,1,86006.92445 +0,0,0,1,28400,58,52,82686,1,0,10331.16381 +0,0,1,1,8755,47,43,47511,0,0,3946.520963 +0,0,1,0,3319,90,106,21859,0,0,5554.865993 +0,1,0,1,12435,37,42,108507,0,1,13941.19144 +1,0,0,1,8712,74,60,41868,1,0,14081.63434 +0,1,0,1,19731,10,6,60657,0,1,7952.221689 +0,0,1,0,5900,186,170,23567,1,1,14249.44051 +0,0,1,0,7499,13,9,21854,0,0,1880.998166 +0,0,1,0,3231,12,11,23226,0,0,869.5679452 +0,0,1,1,23285,101,125,82213,0,0,9610.946665 +1,0,0,0,11851,20,13,94601,1,1,18687.84542 +1,0,0,1,44676,44,49,140724,1,1,26882.98389 +0,0,0,1,18777,65,62,60298,1,0,10622.92178 +1,0,1,0,27383,25,29,127925,0,1,15879.43813 +0,0,0,1,60902,16,19,161836,1,0,14719.98451 +0,0,0,1,13097,30,20,47851,0,0,1680.262467 +0,0,0,1,75199,93,100,193057,1,0,21270.02348 +0,0,1,1,47956,71,87,150646,0,1,17077.46962 +0,1,0,1,30668,12,14,76685,0,1,11054.46492 +0,1,0,1,24828,32,34,66207,0,1,9464.745128 +0,0,1,0,9588,24,23,64208,0,0,1576.904992 +0,0,1,1,15014,25,18,46293,1,1,13356.46203 +0,0,0,1,55478,32,31,150647,1,1,21544.90395 +0,0,0,0,26110,61,65,88044,1,1,16860.49338 +0,0,0,0,58097,14,11,200479,0,1,15998.36376 +0,0,1,1,1981,87,105,13886,0,1,6807.419547 +1,1,0,0,19786,105,80,61173,1,1,21710.26054 +0,1,0,1,4494,15,12,41218,0,1,6891.681471 +0,1,0,0,11548,194,178,32745,1,0,15415.53276 +1,1,0,1,33526,21,26,98231,0,0,12487.33731 +0,0,1,1,6034,74,79,17817,1,0,11138.53115 +0,0,0,1,37957,30,36,109245,0,1,12192.21526 +1,0,0,1,19390,29,37,74918,0,1,12960.35675 +1,0,1,0,8551,45,53,40703,0,0,8123.101703 +0,1,0,1,13988,49,33,50219,0,0,8672.851562 +1,0,1,1,42616,107,94,122328,1,1,28184.29894 +0,1,0,1,78554,56,71,228506,1,0,23682.07355 +0,1,0,1,52583,106,88,137381,0,1,20940.13031 +1,0,1,1,42105,38,41,229669,1,1,34305.23927 +0,0,1,1,18524,65,62,137759,1,1,23509.18852 +1,0,0,1,92410,41,35,422327,0,1,40789.41791 +0,0,0,1,69328,11,10,209910,0,1,18452.77644 +1,0,1,1,5864,71,86,41264,0,0,11360.06959 +0,0,0,1,34784,26,25,221889,0,1,18096.93978 +0,0,1,1,36554,184,127,138290,1,0,20023.38286 +0,0,1,1,34041,87,56,201086,1,1,28382.05576 +0,0,0,1,4290,60,69,21262,0,1,5242.13016 +0,0,0,1,34241,84,76,130518,1,1,22971.45448 +0,0,1,0,18514,91,58,57277,0,0,5125.167803 +1,0,1,0,76835,68,63,244594,1,0,24399.42021 +0,0,0,1,7216,11,10,33670,1,0,6363.877963 +0,0,0,1,19414,59,76,137979,1,1,23419.19662 +0,1,0,0,43270,119,99,117234,1,1,23452.52164 +0,0,0,0,32414,48,52,107806,1,0,11894.77167 +0,0,1,1,7683,37,24,20041,1,0,6912.385715 +0,0,1,1,36966,10,11,156425,0,0,6046.072857 +0,0,1,0,7598,41,26,22896,0,0,3173.607208 +0,0,0,0,11723,79,87,43580,0,1,6342.437748 +1,0,0,1,16234,202,183,77772,0,1,18668.96923 +0,1,0,1,34980,23,20,130407,1,1,20730.45615 +0,0,0,1,74432,12,8,278883,1,0,20267.32063 +0,0,0,1,15286,21,13,68312,0,1,7316.069738 +0,1,0,1,1864,15,9,15110,0,0,3960.120835 +0,0,1,0,4402,18,13,14696,1,0,5994.52877 +0,0,1,1,44773,15,14,125674,1,1,19955.65869 +1,0,0,0,73881,49,43,190863,1,0,22145.54476 +1,0,0,0,54554,248,312,470588,1,1,68429.09507 +0,1,0,1,11124,70,71,43253,1,0,14168.48187 +0,0,0,1,16223,61,59,57955,0,1,9284.721324 +1,0,1,1,27346,59,55,151249,1,1,27932.65921 +0,0,1,1,30256,54,67,85065,0,0,5937.476304 +1,0,1,1,21063,62,40,65840,0,1,12962.64014 +0,0,0,1,23500,11,7,91670,1,0,13413.1494 +1,0,0,1,7939,16,11,65674,0,0,7322.936783 +0,0,0,1,73202,24,16,252970,0,0,6949.114958 +0,1,0,1,13139,14,16,73949,0,1,10114.24582 +1,0,1,1,20919,28,23,160371,0,0,11384.83831 +0,0,0,0,47645,104,76,163568,0,0,7956.801304 +0,0,1,1,41860,28,16,106287,0,1,8605.681492 +0,0,0,1,103125,55,45,318677,1,1,39303.1489 +0,0,0,1,59409,50,58,211780,0,1,19458.78897 +0,1,0,1,38481,30,34,104335,1,0,15149.97842 +1,0,1,0,30814,70,54,88879,1,1,19835.77323 +0,0,0,1,11045,47,50,38137,0,0,6500.022999 +1,0,1,1,69117,11,10,215342,1,1,33756.74093 +0,0,1,1,3348,20,18,30282,1,0,8886.536678 +0,0,1,1,2327,66,70,15085,0,0,7924.059399 +0,0,1,0,2982,68,43,16262,0,0,2613.223994 +0,0,1,1,10988,106,102,109237,0,1,13443.79982 +0,0,1,0,57973,69,63,153445,0,0,9036.567702 +0,0,1,1,14793,32,40,42943,0,1,8199.699674 +0,0,1,0,36920,204,143,96848,0,0,8235.537343 +0,0,1,1,9682,224,286,84905,0,1,21328.54713 +0,0,0,1,4440,64,47,20368,0,0,2290.876869 +0,0,0,0,2346,45,52,17580,1,0,7918.8815 +1,0,0,1,14772,63,74,54855,0,0,8930.495022 +0,0,0,1,3504,321,334,15802,1,0,21081.75578 +0,0,0,1,25058,161,139,127963,1,0,18045.75122 +1,0,1,1,105538,97,117,267677,0,1,32454.81798 +0,1,0,1,20893,70,47,112880,1,0,15249.06291 +0,0,1,1,4716,27,32,18150,0,0,4607.795027 +0,0,0,1,17504,28,30,124231,1,0,13802.46645 +1,0,1,1,22440,33,29,99953,1,0,17706.83971 +0,0,1,1,46186,74,48,141579,0,1,13930.12862 +0,0,1,0,39683,12,13,111848,0,0,4753.072214 +0,1,0,0,4195,14,17,11924,0,0,2161.745939 +1,0,0,1,10664,68,47,40037,1,1,17694.82079 +0,1,0,1,21618,30,20,152869,0,0,9412.468409 diff --git a/book/who_should_be_treated/who_should_be_treated_en.ipynb b/book/who_should_be_treated/who_should_be_treated_en.ipynb new file mode 100644 index 0000000..5d1176a --- /dev/null +++ b/book/who_should_be_treated/who_should_be_treated_en.ipynb @@ -0,0 +1,2597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ae5a6c4", + "metadata": {}, + "source": [ + "**🌐 Language:** **English** | [한국어 →](/who-should-be-treated-ko)\n", + "\n", + "# Who Should Receive Which Promotion?\n", + "\n", + "Many problems in causal inference focus on the Average Treatment Effect (ATE). For example, the question \"should we run a promotion or not?\" compares a policy that offers the same promotion to all customers against one that offers it to no one.\n", + "\n", + "But if the budget is limited and you need to maximize impact within it, the story changes. If promotion effects vary significantly across customers, a one-size-fits-all strategy may not be optimal.\n", + "\n", + "- You waste marketing budget on always-converters who would have purchased regardless, and on lost causes who won't buy no matter what incentive they receive.\n", + "- sleeping dogs who react negatively to marketing actually cause losses, and\n", + "- you miss potential revenue by failing to prioritize high-responders.\n", + "\n", + "The core question is therefore:\n", + "\n", + "_\"Which customers, when targeted, yield the greatest net profit?\"_\n", + "\n", + "Policy learning explores possible treatment rules and learns to maximize the overall average net profit. In other words, it answers the question: \"Who should be treated?\"" + ] + }, + { + "cell_type": "markdown", + "id": "2b06ptb53eu", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fc2741f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:39.293018Z", + "iopub.status.busy": "2026-05-19T10:22:39.292944Z", + "iopub.status.idle": "2026-05-19T10:22:41.041435Z", + "shell.execute_reply": "2026-05-19T10:22:41.040951Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import tempfile\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "os.environ.setdefault(\"MPLCONFIGDIR\", str(Path(tempfile.gettempdir()) / \"matplotlib\"))\n", + "os.environ.setdefault(\"MPLBACKEND\", \"Agg\")\n", + "os.environ.setdefault(\"LOKY_MAX_CPU_COUNT\", \"8\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from econml.dr import DRLearner\n", + "from econml.policy import DRPolicyForest, DRPolicyTree\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 1\n", + "rng = np.random.default_rng(SEED)\n", + "np.random.seed(SEED)\n", + "sns.set_theme(style='whitegrid')" + ] + }, + { + "cell_type": "markdown", + "id": "8b741c26", + "metadata": {}, + "source": [ + "## Problem Setup\n", + "\n", + "A software sales company wants to know whether incentives such as discounts or technical support actually increase software purchases, and which customers respond most effectively.\n", + "\n", + "Ideally, one would run a randomized controlled trial (RCT) assigning different incentives randomly to each customer. In practice, however, factors such as cost, sales strategy, and the risk of losing major clients make randomized experiments difficult. This data is therefore observational, not from a randomized experiment.\n", + "\n", + "The dataset contains information on approximately 2,000 customers, described by the following variables.\n", + "\n", + "- Customer characteristics: detailed information on each customer's industry, size, revenue, and technology profile.\n", + "- Intervention: information on the incentive provided to each customer.\n", + "- Outcome: software purchase revenue over the year following the incentive.\n", + "\n", + "| Variable | Type | Description |\n", + "| --------------- | ---- | ---------------------------------------------------------------------------- |\n", + "| Global Flag | X | Whether the customer has a global office (overseas branch) |\n", + "| Major Flag | X | Whether the customer is a large consumer in its industry (vs. SMC or SMB) |\n", + "| SMC Flag | X | Whether the customer is a small-medium corporation (vs. Major or SMB) |\n", + "| Commercial Flag | X | Whether the customer's business type is commercial (vs. public sector) |\n", + "| IT Spend | X | Amount spent on IT-related purchases |\n", + "| Employee Count | X | Number of employees |\n", + "| PC Count | X | Number of PCs used by the customer |\n", + "| Size | X | Customer size measured by annual total revenue |\n", + "| Tech Support | T | Whether the customer received technical support (binary) |\n", + "| Discount | T | Whether the customer received a discount (binary) |\n", + "| Revenue | Y | Customer revenue from software purchases |\n", + "\n", + "\n", + "Since both `Tech Support` and `Discount` are interventions, we combine them to define four treatments:\n", + "\n", + "$$\n", + "A_i \\in \\{0, 1, 2, 3\\}\n", + "$$\n", + "\n", + "Each treatment means:\n", + "- $A_i = 0$: no intervention\n", + "- $A_i = 1$: technical support only\n", + "- $A_i = 2$: discount only\n", + "- $A_i = 3$: both technical support and discount" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "477aed7a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.045288Z", + "iopub.status.busy": "2026-05-19T10:22:41.045118Z", + "iopub.status.idle": "2026-05-19T10:22:41.055755Z", + "shell.execute_reply": "2026-05-19T10:22:41.055291Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2000, 13)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Global FlagMajor FlagSMC FlagCommercial FlagIT SpendEmployee CountPC CountSizeTech SupportDiscountRevenuetreatmenttreatment_name
010104553726261522050117688.363002discount_only
1001120842107701590380114981.435592discount_only
20001821711072649351132917.138943discount_plus_support
30000302884039775221114773.768553discount_plus_support
40010259303743914461117098.698233discount_plus_support
\n", + "
" + ], + "text/plain": [ + " Global Flag Major Flag SMC Flag Commercial Flag IT Spend \\\n", + "0 1 0 1 0 45537 \n", + "1 0 0 1 1 20842 \n", + "2 0 0 0 1 82171 \n", + "3 0 0 0 0 30288 \n", + "4 0 0 1 0 25930 \n", + "\n", + " Employee Count PC Count Size Tech Support Discount Revenue \\\n", + "0 26 26 152205 0 1 17688.36300 \n", + "1 107 70 159038 0 1 14981.43559 \n", + "2 10 7 264935 1 1 32917.13894 \n", + "3 40 39 77522 1 1 14773.76855 \n", + "4 37 43 91446 1 1 17098.69823 \n", + "\n", + " treatment treatment_name \n", + "0 2 discount_only \n", + "1 2 discount_only \n", + "2 3 discount_plus_support \n", + "3 3 discount_plus_support \n", + "4 3 discount_plus_support " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('./data/multi_attribution_sample.csv')\n", + "data.columns = data.columns.str.strip()\n", + "\n", + "covariates = [\n", + " 'Global Flag',\n", + " 'Major Flag',\n", + " 'SMC Flag',\n", + " 'Commercial Flag',\n", + " 'IT Spend',\n", + " 'Employee Count',\n", + " 'PC Count',\n", + " 'Size',\n", + "]\n", + "outcome = 'Revenue'\n", + "\n", + "TREATMENT_NAMES = {\n", + " 0: 'none',\n", + " 1: 'tech_support_only',\n", + " 2: 'discount_only',\n", + " 3: 'discount_plus_support',\n", + "}\n", + "TREATMENT_LABELS = [TREATMENT_NAMES[i] for i in range(4)]\n", + "\n", + "required_columns = covariates + ['Tech Support', 'Discount', outcome]\n", + "for col in required_columns:\n", + " data[col] = pd.to_numeric(data[col], errors='coerce')\n", + "\n", + "policy_df = data[required_columns].dropna().copy()\n", + "policy_df['treatment'] = (\n", + " 2 * policy_df['Discount'].astype(int)\n", + " + policy_df['Tech Support'].astype(int)\n", + ").astype(int)\n", + "policy_df['treatment_name'] = policy_df['treatment'].map(TREATMENT_NAMES)\n", + "\n", + "n = len(policy_df)\n", + "X = policy_df[covariates].to_numpy()\n", + "Y = policy_df[outcome].to_numpy(dtype=float)\n", + "A = policy_df['treatment'].to_numpy(dtype=int)\n", + "\n", + "print(policy_df.shape)\n", + "policy_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8bfa27b6", + "metadata": {}, + "source": [ + "### Cost Setup\n", + "\n", + "In real-world operations, costs must be considered alongside incentive effects. Furthermore, the cost of the same incentive can vary across customers depending on their characteristics.\n", + "\n", + "For example, technical support may require more support hours for larger companies, while discounts may need to be deeper for larger customers.\n", + "\n", + "Since this dataset does not include actual cost information, we simulate costs that vary with customer characteristics.\n", + "\n", + "Technical support cost increases with employee count, and discount cost increases with customer size.\n", + "\n", + "We use the log-normal distribution so that costs are always positive and can exhibit the heavy-tailed behavior seen in practice.\n", + "\n", + "$$\n", + "C_i(\\text{tech}) \\sim \\text{LogNormal}(\\log C_{\\text{tech}} + 0.5 \\cdot \\tilde{e}_i,\\ 0.3)\n", + "$$\n", + "\n", + "$$\n", + "C_i(\\text{disc}) \\sim \\text{LogNormal}(\\log C_{\\text{disc}} + 0.4 \\cdot \\tilde{s}_i,\\ 0.3)\n", + "$$\n", + "\n", + "where $\\tilde{e}_i$ is the standardized employee count and $\\tilde{s}_i$ is the standardized company size.\n", + "\n", + "With per-customer costs defined this way, the net outcome is:\n", + "\n", + "$$\n", + "Y_i^{net} = Y_i - C_i(A_i)\n", + "$$\n", + "\n", + "Using $Y_i^{net}$ as the outcome when computing AIPW scores means $\\hat\\Gamma_{i,a}$ naturally estimates $E[Y(a) - C(a) \\mid X_i]$. Policy learning and evaluation based on AIPW thus automatically incorporate costs." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5174ddd2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.057016Z", + "iopub.status.busy": "2026-05-19T10:22:41.056943Z", + "iopub.status.idle": "2026-05-19T10:22:41.061974Z", + "shell.execute_reply": "2026-05-19T10:22:41.061611Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmentmean_coststd_costmean_revenuemean_net_revenue
0none0.0000000.0000006585.8917926585.891792
1tech_support_only5037.8158944917.64135815104.11153410066.295640
2discount_only5181.5703343043.34317212247.9359537066.365619
3discount_plus_support12665.2832967375.49012526784.12464914118.841353
\n", + "
" + ], + "text/plain": [ + " treatment mean_cost std_cost mean_revenue \\\n", + "0 none 0.000000 0.000000 6585.891792 \n", + "1 tech_support_only 5037.815894 4917.641358 15104.111534 \n", + "2 discount_only 5181.570334 3043.343172 12247.935953 \n", + "3 discount_plus_support 12665.283296 7375.490125 26784.124649 \n", + "\n", + " mean_net_revenue \n", + "0 6585.891792 \n", + "1 10066.295640 \n", + "2 7066.365619 \n", + "3 14118.841353 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COST_TECH_SUPPORT = 4_000.0 # baseline cost for tech support\n", + "COST_DISCOUNT = 5_000.0 # baseline cost for discount\n", + "\n", + "\n", + "rng_c = np.random.default_rng(SEED + 99)\n", + "emp_idx = covariates.index('Employee Count')\n", + "size_idx = covariates.index('Size')\n", + "\n", + "emp_z = (X[:, emp_idx] - X[:, emp_idx].mean()) / (X[:, emp_idx].std() + 1e-8)\n", + "size_z = (X[:, size_idx] - X[:, size_idx].mean()) / (X[:, size_idx].std() + 1e-8)\n", + "\n", + "c_tech = rng_c.lognormal(mean=np.log(COST_TECH_SUPPORT) + 0.5 * emp_z, sigma=0.3)\n", + "c_disc = rng_c.lognormal(mean=np.log(COST_DISCOUNT) + 0.4 * size_z, sigma=0.3)\n", + "\n", + "c_tech = np.clip(c_tech, 200.0, 40_000.0)\n", + "c_disc = np.clip(c_disc, 200.0, 30_000.0)\n", + "\n", + "C_obs = np.select(\n", + " [A == 1, A == 2, A == 3],\n", + " [c_tech, c_disc, c_tech + c_disc],\n", + " default=0.0,\n", + ")\n", + "\n", + "Y_net = Y - C_obs\n", + "\n", + "pd.DataFrame({\n", + " 'treatment': TREATMENT_LABELS,\n", + " 'mean_cost': [C_obs[A == a].mean() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'std_cost': [C_obs[A == a].std() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'mean_revenue': [Y[A == a].mean() for a in range(4)],\n", + " 'mean_net_revenue': [Y_net[A == a].mean() for a in range(4)],\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8e5a5326", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.063047Z", + "iopub.status.busy": "2026-05-19T10:22:41.062981Z", + "iopub.status.idle": "2026-05-19T10:22:41.167315Z", + "shell.execute_reply": "2026-05-19T10:22:41.166903Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "sns.histplot(c_tech, bins=30, stat='density', element='step', fill=True, ax=axes[0], color='tab:blue')\n", + "axes[0].axvline(np.median(c_tech), color='black', ls='--', lw=1, alpha=0.7)\n", + "axes[0].set_title('Tech support cost distribution')\n", + "axes[0].set_xlabel('Cost')\n", + "axes[0].set_ylabel('Density')\n", + "axes[0].set_xlim(left=0)\n", + "\n", + "sns.histplot(c_disc, bins=30, stat='density', element='step', fill=True, ax=axes[1], color='tab:orange')\n", + "axes[1].axvline(np.median(c_disc), color='black', ls='--', lw=1, alpha=0.7)\n", + "axes[1].set_title('Discount cost distribution')\n", + "axes[1].set_xlabel('Cost')\n", + "axes[1].set_ylabel('Density')\n", + "axes[1].set_xlim(left=0)\n", + "\n", + "plt.suptitle('Simulated cost distributions', y=1.02)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "pl2exzwbceb", + "metadata": {}, + "source": [ + "### Data Split\n", + "\n", + "Using the same data for policy learning and evaluation leads to overestimation. To prevent this, we split the data into train and test sets. Policies are learned on the train set and evaluated using AIPW scores computed on the test set.\n", + "\n", + "The same split is used for propensity estimation in the positivity check." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "n7ziuiv612q", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.168385Z", + "iopub.status.busy": "2026-05-19T10:22:41.168318Z", + "iopub.status.idle": "2026-05-19T10:22:41.171251Z", + "shell.execute_reply": "2026-05-19T10:22:41.170933Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: 1000 \n", + "Test: 1000\n" + ] + } + ], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]\n", + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "print(f\"Train: {len(train_idx)} \\nTest: {len(test_idx)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "024e5803", + "metadata": {}, + "source": [ + "### Exploratory Data Analysis and Diagnostics\n", + "\n", + "Before entering policy learning, we first examine the data structure across the four treatments ($A \\in \\{0,1,2,3\\}$).\n", + "\n", + "1. Sample size per treatment: are there enough observations in each treatment?\n", + "2. Mean customer characteristics per treatment: which customer segments tend to receive which treatment?\n", + "3. Revenue distribution per treatment: how do Revenue scale, variance, and outlier patterns differ across treatments?\n", + "4. Positivity check: are all treatments sufficiently co-observed across the range of customer characteristics?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3d0f329e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.172285Z", + "iopub.status.busy": "2026-05-19T10:22:41.172226Z", + "iopub.status.idle": "2026-05-19T10:22:41.175988Z", + "shell.execute_reply": "2026-05-19T10:22:41.175575Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countshare
treatment
none5170.2585
tech_support_only4620.2310
discount_only4770.2385
discount_plus_support5440.2720
\n", + "
" + ], + "text/plain": [ + " count share\n", + "treatment \n", + "none 517 0.2585\n", + "tech_support_only 462 0.2310\n", + "discount_only 477 0.2385\n", + "discount_plus_support 544 0.2720" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_counts = policy_df['treatment_name'].value_counts().reindex(TREATMENT_LABELS).rename_axis('treatment').to_frame('count')\n", + "treatment_counts['share'] = treatment_counts['count'] / len(policy_df)\n", + "\n", + "display(treatment_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "8e911f32", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.072480Z", + "iopub.status.busy": "2026-05-09T16:49:48.072405Z", + "iopub.status.idle": "2026-05-09T16:49:48.076539Z", + "shell.execute_reply": "2026-05-09T16:49:48.076070Z" + } + }, + "source": [ + "The actual sample sizes are `none` 517, `tech_support_only` 462, `discount_only` 477, and `discount_plus_support` 544. Each treatment's share ranges from 23.1% to 27.2%, indicating a reasonably balanced distribution with no treatment severely underrepresented.\n", + "\n", + "The sample distribution is therefore stable for policy learning with four treatments." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6488c62", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.177013Z", + "iopub.status.busy": "2026-05-19T10:22:41.176934Z", + "iopub.status.idle": "2026-05-19T10:22:41.264622Z", + "shell.execute_reply": "2026-05-19T10:22:41.264197Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_covariate_means = (\n", + " policy_df\n", + " .groupby('treatment_name')[covariates]\n", + " .mean()\n", + " .reindex(TREATMENT_LABELS)\n", + ")\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "sns.heatmap(treatment_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "plt.xlabel('Treatments')\n", + "plt.ylabel('Covariate')\n", + "plt.title('Mean customer characteristics by treatments')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "c49e9856", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.078246Z", + "iopub.status.busy": "2026-05-09T16:49:48.078155Z", + "iopub.status.idle": "2026-05-09T16:49:48.194453Z", + "shell.execute_reply": "2026-05-09T16:49:48.193850Z" + } + }, + "source": [ + "Customer characteristics are not identical across treatments. The mean `Size` and `IT Spend` differ by treatment group, and the `discount_plus_support` group includes more large customers. From a domain perspective, the type of support needed may also differ with customer scale and IT spend.\n", + "\n", + "These differences suggest that treatment effects may vary with customer characteristics. Policy learning and evaluation therefore use AIPW so these differences are accounted for." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c925a3d8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.265851Z", + "iopub.status.busy": "2026-05-19T10:22:41.265761Z", + "iopub.status.idle": "2026-05-19T10:22:41.325363Z", + "shell.execute_reply": "2026-05-19T10:22:41.325026Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmenttreatment_namecountmeanmedianstdminmax
00none5176585.8917926123.1870673363.163462-616.57245121445.05937
11tech_support_only46215104.11153414483.7193205400.3198584619.49136140166.67407
22discount_only47712247.93595310454.9325007472.178476889.97565341818.39213
33discount_plus_support54426784.12464923560.25289013124.9680835903.90688086006.92445
\n", + "
" + ], + "text/plain": [ + " treatment treatment_name count mean median \\\n", + "0 0 none 517 6585.891792 6123.187067 \n", + "1 1 tech_support_only 462 15104.111534 14483.719320 \n", + "2 2 discount_only 477 12247.935953 10454.932500 \n", + "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", + "\n", + " std min max \n", + "0 3363.163462 -616.572451 21445.05937 \n", + "1 5400.319858 4619.491361 40166.67407 \n", + "2 7472.178476 889.975653 41818.39213 \n", + "3 13124.968083 5903.906880 86006.92445 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_revenue = (\n", + " policy_df\n", + " .groupby(['treatment', 'treatment_name'])[outcome]\n", + " .agg(['count', 'mean', 'median', 'std', 'min', 'max'])\n", + " .reset_index()\n", + ")\n", + "display(treatment_revenue)\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "\n", + "sns.histplot(\n", + " data=policy_df,\n", + " x=outcome,\n", + " hue='treatment_name',\n", + " bins=40,\n", + " element='step',\n", + " stat='density',\n", + " common_norm=False\n", + ")\n", + "\n", + "plt.title('Revenue density by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "639d2437", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.195732Z", + "iopub.status.busy": "2026-05-09T16:49:48.195645Z", + "iopub.status.idle": "2026-05-09T16:49:48.262584Z", + "shell.execute_reply": "2026-05-09T16:49:48.262041Z" + } + }, + "source": [ + "Mean Revenue is approximately 6,586 for `none`, 12,248 for `discount_only`, 15,104 for `tech_support_only`, and 26,784 for `discount_plus_support`.\n", + "\n", + "As noted above, larger customers tend to receive stronger interventions, so the raw mean differences confound customer characteristics with actual treatment effects. Evaluating policy based on unadjusted means would therefore be misleading.\n", + "\n", + "We should instead rely on AIPW-based policy evaluation for proper comparison." + ] + }, + { + "cell_type": "markdown", + "id": "0b51rgb0z128", + "metadata": {}, + "source": [ + "### Identification Assumptions\n", + "\n", + "For AIPW estimation from observational data to be causally valid, two assumptions are required.\n", + "\n", + "1. Unconfoundedness\n", + "\n", + " $$\\{Y_i(0), Y_i(1), Y_i(2), Y_i(3)\\} \\perp A_i \\mid X_i$$\n", + "\n", + " Conditional on observed covariates $X_i$, treatment assignment must be independent of potential outcomes. That is, we assume variables such as customer size, IT spend, and employee count sufficiently explain treatment assignment.\n", + "\n", + "2. Positivity\n", + "\n", + " $$P(A_i = a \\mid X_i = x) > 0 \\quad \\forall a \\in \\{0,1,2,3\\},\\ \\forall x$$\n", + "\n", + " Each treatment must be observed with positive probability across all ranges of customer characteristics." + ] + }, + { + "cell_type": "markdown", + "id": "61b7329f", + "metadata": {}, + "source": [ + "Positivity is verified directly by estimating propensity scores:\n", + "\n", + "$$\n", + "e_a(x) = P(A_i = a \\mid X_i=x)\n", + "$$\n", + "\n", + "We also check whether propensity scores are severely skewed. If $\\hat{e}_a(x)$ is very close to zero for some customers under some treatment, those observations receive excessively large weights, which can destabilize estimation." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9be31313", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.326722Z", + "iopub.status.busy": "2026-05-19T10:22:41.326652Z", + "iopub.status.idle": "2026-05-19T10:22:41.572432Z", + "shell.execute_reply": "2026-05-19T10:22:41.572020Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmentmean_propensitymin_propensityp01_propensitypropensity_below_0_05_rate
0none0.2639410.0576000.0668310.0
1tech_support_only0.2298630.1265820.1562830.0
2discount_only0.2379340.1256380.1413220.0
3discount_plus_support0.2682630.0872990.1013040.0
\n", + "
" + ], + "text/plain": [ + " treatment mean_propensity min_propensity p01_propensity \\\n", + "0 none 0.263941 0.057600 0.066831 \n", + "1 tech_support_only 0.229863 0.126582 0.156283 \n", + "2 discount_only 0.237934 0.125638 0.141322 \n", + "3 discount_plus_support 0.268263 0.087299 0.101304 \n", + "\n", + " propensity_below_0_05_rate \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_propensity = RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "multi_propensity.fit(X_train, A_train)\n", + "e_hat_raw = multi_propensity.predict_proba(X_test)\n", + "\n", + "propensity_summary = pd.DataFrame({\n", + " 'treatment': TREATMENT_LABELS,\n", + " 'mean_propensity': e_hat_raw.mean(axis=0),\n", + " 'min_propensity': e_hat_raw.min(axis=0),\n", + " 'p01_propensity': np.quantile(e_hat_raw, 0.01, axis=0),\n", + " 'propensity_below_0_05_rate': (e_hat_raw < 0.05).mean(axis=0),\n", + "})\n", + "display(propensity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "fl52yekx049", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.573573Z", + "iopub.status.busy": "2026-05-19T10:22:41.573501Z", + "iopub.status.idle": "2026-05-19T10:22:41.629699Z", + "shell.execute_reply": "2026-05-19T10:22:41.629349Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prop_df = pd.DataFrame(e_hat_raw, columns=TREATMENT_LABELS)\n", + "prop_long = prop_df.melt(var_name='treatment', value_name='propensity')\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "sns.histplot(\n", + " data=prop_long, x='propensity', hue='treatment',\n", + " bins=30, element='step', stat='density', common_norm=False, ax=ax,\n", + ")\n", + "\n", + "ax.axvline(0.05, color='tomato', lw=1.5, ls='--', alpha=0.8, label='threshold = 0.05')\n", + "ax.legend(title='Treatment', fontsize=8)\n", + "ax.set_xlabel('Propensity score')\n", + "ax.set_ylabel('Density')\n", + "ax.set_title('Propensity score distribution by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b42eca5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.264007Z", + "iopub.status.busy": "2026-05-09T16:49:48.263913Z", + "iopub.status.idle": "2026-05-09T16:49:48.571081Z", + "shell.execute_reply": "2026-05-09T16:49:48.570638Z" + } + }, + "source": [ + "In this dataset the minimum value of $\\hat e_a(X_i)$ is approximately 0.058, and no observations have `e_hat < 0.05`. Excessively large weights from extremely small propensity scores are therefore not a major concern here.\n", + "\n", + "The propensity distributions for all four treatments overlap well in the graph, and no clear positivity violations are observed." + ] + }, + { + "cell_type": "markdown", + "id": "f4edd2d8", + "metadata": {}, + "source": [ + "## Policy Learning Methods\n", + "\n", + "We compare three policy learning approaches.\n", + "\n", + "- Plug-in Policy (DRLearner)\n", + "\n", + " First estimates per-customer CATE, then selects the treatment with the highest expected net benefit.\n", + "\n", + " This is a flexible approach with no constraints on policy form, but final performance depends heavily on the quality of CATE estimation.\n", + "\n", + "- DRPolicyTree\n", + "\n", + " DRPolicyTree learns a policy by directly maximizing the AIPW score.\n", + "\n", + " The policy structure is constrained to a shallow decision tree, making results interpretable as decision rules. This is useful when policy interpretability and explainability matter.\n", + "\n", + "- DRPolicyForest\n", + "\n", + " DRPolicyForest also directly maximizes the AIPW score but uses a forest rather than a single tree.\n", + "\n", + " A single tree has a simple structure but can exhibit high variance. A forest averages across many trees, reducing variance and learning heterogeneous treatment effects more stably.\n", + "\n", + " However, the resulting policy is harder to interpret as a single clear decision rule set.\n", + "\n", + "All three policies are learned on the train set and compared using the same test set. It is important to use separate data for learning and evaluation, as using the training data for evaluation leads to overestimation." + ] + }, + { + "cell_type": "markdown", + "id": "3c967473", + "metadata": {}, + "source": [ + "### Plug-in Policy\n", + "\n", + "A plug-in policy first estimates per-customer treatment effects (CATE) and then uses those estimates to construct the policy.\n", + "\n", + "For binary treatment, the standard rule is to treat if $\\hat\\tau(x) > 0$, or if costs are present, treat when $\\hat\\tau(x) > c(x)$.\n", + "\n", + "With multiple treatments, we compare relative effects against a baseline. Here we set `none` (no treatment) as the baseline and use `DRLearner` to estimate:\n", + "\n", + "$$\n", + "\\hat\\tau_a(x) = \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "\\qquad a \\in \\{1,2,3\\}\n", + "$$\n", + "\n", + "Since the relative effect of the baseline treatment is 0, we compare the following four values per customer:\n", + "\n", + "$$\n", + "[0, \\hat\\tau_1(x), \\hat\\tau_2(x), \\hat\\tau_3(x)]\n", + "$$\n", + "\n", + "and select the treatment with the highest value:\n", + "\n", + "$$\n", + "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "$$\n", + "\n", + "This approach is intuitive and flexible, but it depends heavily on the quality of CATE estimation. The learned policy is therefore evaluated on a separate test set using AIPW value.\n", + "\n", + "> **Note** One might ask \"why not just select the treatment with the highest $\\hat\\Gamma_{i,a}$ for each customer?\" However, AIPW scores have very high variance at the individual observation level, so a policy built by pointwise argmax overfits to noise. AIPW scores must always be used in aggregated form — for example, summed across observations for policy evaluation (value estimation, cost curves, etc.), averaged within nodes for DRPolicyTree/Forest, or used as pseudo-outcomes for regression in DRLearner." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "debd92a9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:41.631147Z", + "iopub.status.busy": "2026-05-19T10:22:41.631045Z", + "iopub.status.idle": "2026-05-19T10:22:44.214210Z", + "shell.execute_reply": "2026-05-19T10:22:44.213696Z" + } + }, + "outputs": [], + "source": [ + "dr_cate = DRLearner(\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_final=RandomForestRegressor(\n", + " n_estimators=300,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", + "\n", + "# returns E[Y_net(a) - Y_net(0) | X] for each treatment relative to none (baseline)\n", + "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", + "plugin_treatment_values = np.column_stack([\n", + " np.zeros(len(X_test)), # none: baseline, relative effect = 0\n", + " cate_vs_none,\n", + "])\n", + "pi_plugin = np.argmax(plugin_treatment_values, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "33437c2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:44.215518Z", + "iopub.status.busy": "2026-05-19T10:22:44.215441Z", + "iopub.status.idle": "2026-05-19T10:22:44.218542Z", + "shell.execute_reply": "2026-05-19T10:22:44.218254Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.092\n", + "tech_support_only 0.499\n", + "discount_only 0.035\n", + "discount_plus_support 0.374\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(pi_plugin).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "33ktqy8zrqm", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:44.219742Z", + "iopub.status.busy": "2026-05-19T10:22:44.219679Z", + "iopub.status.idle": "2026-05-19T10:22:44.223928Z", + "shell.execute_reply": "2026-05-19T10:22:44.223611Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SizeEmployee CountIT Spendassigned_treatmentτ_techτ_discτ_both
09620414217655none-6140-1419-1715
1253901787971none-14556-4252-7277
26655527919899none-17629-2896-16555
3708682026263tech_support_only4265-414146
424152184610tech_support_only4174-2315-772
5548556314772tech_support_only2061-698587
639922836133052discount_plus_support7849-127811819
71817042459529discount_plus_support9171522110249
829437737112485discount_plus_support71248779048
\n", + "
" + ], + "text/plain": [ + " Size Employee Count IT Spend assigned_treatment τ_tech τ_disc \\\n", + "0 96204 142 17655 none -6140 -1419 \n", + "1 25390 178 7971 none -14556 -4252 \n", + "2 66555 279 19899 none -17629 -2896 \n", + "3 70868 20 26263 tech_support_only 4265 -41 \n", + "4 24152 18 4610 tech_support_only 4174 -2315 \n", + "5 54855 63 14772 tech_support_only 2061 -698 \n", + "6 399228 36 133052 discount_plus_support 7849 -1278 \n", + "7 181704 24 59529 discount_plus_support 9171 5221 \n", + "8 294377 37 112485 discount_plus_support 7124 877 \n", + "\n", + " τ_both \n", + "0 -1715 \n", + "1 -7277 \n", + "2 -16555 \n", + "3 4146 \n", + "4 -772 \n", + "5 587 \n", + "6 11819 \n", + "7 10249 \n", + "8 9048 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "none_idx = np.where(pi_plugin == 0)[0][:3]\n", + "tech_idx = np.where(pi_plugin == 1)[0][:3]\n", + "both_idx = np.where(pi_plugin == 3)[0][:3]\n", + "sample_ids = np.concatenate([none_idx, tech_idx, both_idx])\n", + "\n", + "df_plugin_sample = pd.DataFrame(X_test[sample_ids], columns=covariates)[['Size', 'Employee Count', 'IT Spend']]\n", + "df_plugin_sample['assigned_treatment'] = pd.Series(pi_plugin[sample_ids]).map(TREATMENT_NAMES).values\n", + "df_plugin_sample[['τ_tech', 'τ_disc', 'τ_both']] = np.round(cate_vs_none[sample_ids], 0).astype(int)\n", + "df_plugin_sample.index = pd.RangeIndex(len(df_plugin_sample))\n", + "df_plugin_sample" + ] + }, + { + "cell_type": "markdown", + "id": "05b0ee18", + "metadata": {}, + "source": [ + "The plug-in policy assigns `tech_support_only` to approximately 50% of customers, `discount_plus_support` to 37%, and `none` to about 9%.\n", + "\n", + "After accounting for costs, some customers are expected to have negative net profit from treatment, so the policy chooses not to treat them." + ] + }, + { + "cell_type": "markdown", + "id": "97d958cd", + "metadata": {}, + "source": [ + "### Policy Tree\n", + "\n", + "In practice, policy interpretability is often as important as performance. Shallow decision tree-based policies are frequently used in operations for the following reasons:\n", + "\n", + "- **Stakeholder communication**: the intuitive decision rule branching structure makes policies easy to explain and share.\n", + "- **Fairness review**: the explicit structure of who receives which treatment makes it easy to audit for bias.\n", + "- **Operational stability**: simple rule-based policies are more manageable in production than complex models.\n", + "\n", + "Rather than searching for the optimal policy across all possible functions, we restrict the search to a limited policy class $\\Pi$:\n", + "\n", + "$$\n", + "\\hat\\pi = \\arg\\max_{\\pi \\in \\Pi}\n", + "\\frac{1}{n}\\sum_{i=1}^{n} \\widehat\\Gamma_{i,\\pi(X_i)}\n", + "$$\n", + "\n", + "Policy Tree restricts $\\Pi$ to shallow decision trees. With a small depth, the resulting policy is human-readable. We use `DRPolicyTree` from `econml`.\n", + "\n", + "To prevent unstable value estimation in small leaves under multi-treatment settings, `min_samples_leaf` is used to ensure leaves are large enough." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6c987a8b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:44.225039Z", + "iopub.status.busy": "2026-05-19T10:22:44.224976Z", + "iopub.status.idle": "2026-05-19T10:22:45.822698Z", + "shell.execute_reply": "2026-05-19T10:22:45.822253Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.199\n", + "tech_support_only 0.475\n", + "discount_only 0.000\n", + "discount_plus_support 0.326\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_tree = DRPolicyTree(\n", + " max_depth=2,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_policy_tree.fit(Y_net_train, A_train, X=X_train)\n", + "pi_tree = dr_policy_tree.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_tree).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "64529830", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:45.823867Z", + "iopub.status.busy": "2026-05-19T10:22:45.823783Z", + "iopub.status.idle": "2026-05-19T10:22:45.916942Z", + "shell.execute_reply": "2026-05-19T10:22:45.916533Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(13, 7))\n", + "dr_policy_tree.plot(feature_names=covariates, treatment_names=TREATMENT_LABELS, ax=ax)\n", + "ax.set_title('4-way DRPolicyTree')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2a3ffb5d", + "metadata": {}, + "source": [ + "This tree policy primarily splits customers by `Size`. Notably, it assigns `none` to approximately 20% of customers — after accounting for costs, the expected net profit from treatment is low enough for some customers that not treating them is the better choice.\n", + "\n", + "Overall, the policy assigns `discount_plus_support` to large customers and `tech_support_only` to the rest, while choosing `none` for customers with low expected net profit." + ] + }, + { + "cell_type": "markdown", + "id": "35dd95b1", + "metadata": {}, + "source": [ + "### Policy Forest\n", + "\n", + "Policy Forest learns policies more stably by aggregating many trees.\n", + "\n", + "Like DRPolicyTree, DRPolicyForest directly maximizes the AIPW score, but it averages across many trees instead of using a single one. This reduces variance and allows more stable learning of complex heterogeneous treatment effects.\n", + "\n", + "We use `DRPolicyForest` from `econml`.\n", + "\n", + "Unlike tree-based policies, the resulting policy cannot be easily expressed as a single clear decision rule. After fitting, we compare policy values using AIPW scores from the same test set." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d7939447", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:45.918249Z", + "iopub.status.busy": "2026-05-19T10:22:45.918166Z", + "iopub.status.idle": "2026-05-19T10:22:47.744849Z", + "shell.execute_reply": "2026-05-19T10:22:47.744325Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.114\n", + "tech_support_only 0.469\n", + "discount_only 0.000\n", + "discount_plus_support 0.417\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_forest = DRPolicyForest(\n", + " n_estimators=400,\n", + " max_depth=5,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "dr_policy_forest.fit(Y_net_train, A_train, X=X_train)\n", + "pi_forest = dr_policy_forest.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_forest).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "15d7d727", + "metadata": {}, + "source": [ + "DRPolicyForest shows a treatment assignment pattern broadly similar to the plug-in policy. It assigns `none` to 11%, `tech_support_only` to 47%, and `discount_plus_support` to 42% — a slightly higher share of `discount_plus_support` compared to the plug-in policy." + ] + }, + { + "cell_type": "markdown", + "id": "ybkfsfjonmd", + "metadata": {}, + "source": [ + "### Feature Importance\n", + "\n", + "DRPolicyForest has no single tree structure, so the policy cannot be directly interpreted as a set of branching rules. However, `feature_importances_` provides a way to understand which customer characteristics most influence treatment assignment decisions.\n", + "\n", + "`feature_importances_` is a normalized score measuring how much each feature induces policy heterogeneity — that is, how much it drives different treatment assignments across customers — during the forest's splitting process. A higher score means the feature plays a larger role in determining the optimal treatment for each customer.\n", + "\n", + "Note that these scores are best used as an exploratory signal indicating \"which features were frequently used as splitting criteria,\" rather than as a definitive causal interpretation." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c7jomjuyav", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:47.746096Z", + "iopub.status.busy": "2026-05-19T10:22:47.746031Z", + "iopub.status.idle": "2026-05-19T10:22:47.802853Z", + "shell.execute_reply": "2026-05-19T10:22:47.802487Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featureimportance
0Employee Count0.417657
1Size0.378824
2PC Count0.127452
3IT Spend0.075229
4SMC Flag0.000479
5Global Flag0.000194
6Commercial Flag0.000147
7Major Flag0.000017
\n", + "
" + ], + "text/plain": [ + " feature importance\n", + "0 Employee Count 0.417657\n", + "1 Size 0.378824\n", + "2 PC Count 0.127452\n", + "3 IT Spend 0.075229\n", + "4 SMC Flag 0.000479\n", + "5 Global Flag 0.000194\n", + "6 Commercial Flag 0.000147\n", + "7 Major Flag 0.000017" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "importances = dr_policy_forest.feature_importances_\n", + "fi_df = pd.DataFrame({\n", + " 'feature': covariates,\n", + " 'importance': importances,\n", + "}).sort_values('importance', ascending=False).reset_index(drop=True)\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "sns.barplot(data=fi_df, x='importance', y='feature', ax=ax, color='steelblue')\n", + "ax.set_title('DRPolicyForest — Feature Importances')\n", + "ax.set_xlabel('Importance (normalized)')\n", + "ax.set_ylabel('')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "display(fi_df)" + ] + }, + { + "cell_type": "markdown", + "id": "zn4ry7e0a0f", + "metadata": {}, + "source": [ + "`Employee Count` (42%) and `Size` (38%) together account for approximately 80% of total importance, confirming that customer scale and workforce size are the primary drivers of treatment assignment. `PC Count` (13%) and `IT Spend` (8%) contribute modestly, while the remaining flag variables have a negligible impact.\n", + "\n", + "This is consistent with DRPolicyTree's primary split on `Size`. Both models agree that customer scale is the strongest signal for determining the optimal treatment — whether to offer technical support, a discount, or nothing." + ] + }, + { + "cell_type": "markdown", + "id": "99bac91c", + "metadata": {}, + "source": [ + "## Policy Evaluation\n", + "\n", + "We now compare the learned policies using AIPW scores from the same test set.\n", + "\n", + "As benchmarks, we also include constant policies that apply the same treatment to every customer:\n", + "\n", + "- `all_none`\n", + "- `all_tech_support_only`\n", + "- `all_discount_only`\n", + "- `all_discount_plus_support`\n", + "\n", + "For example, `all_discount_plus_support` represents the average net profit when both technical support and a discount are provided to every customer.\n", + "\n", + "By contrast, the learned policies assign different treatments depending on each customer's characteristics $X_i$.\n", + "\n", + "If a learned policy's value exceeds the best constant policy, it means that customer-level targeting is more effective than applying the same treatment to everyone." + ] + }, + { + "cell_type": "markdown", + "id": "5261c54f", + "metadata": {}, + "source": [ + "The AIPW (Augmented Inverse Probability Weighting) score is defined as:\n", + "\n", + "$$\n", + "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "$$\n", + "\n", + "- $\\hat\\mu_a(X_i)$: predicted $E[Y^{net}(a)\\mid X_i]$ from the outcome model\n", + "- $\\hat e_a(X_i)$: predicted $P(A_i=a\\mid X_i)$ from the propensity model\n", + "- The second term corrects the difference between observed and predicted values via IPW.\n", + "\n", + "This structure ensures unbiasedness holds as long as either the outcome model or the propensity model is correctly specified. This property is called doubly robust.\n", + "\n", + "- If the outcome model is correct: the expected value of the IPW correction term approaches zero, so $\\hat\\mu_a(X_i)$ contributes directly.\n", + "- If the propensity model is correct: the IPW correction term removes the bias of the outcome model.\n", + "\n", + "This score is used to estimate the value of each learned policy." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c51d8c52", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:47.804127Z", + "iopub.status.busy": "2026-05-19T10:22:47.804062Z", + "iopub.status.idle": "2026-05-19T10:22:48.450061Z", + "shell.execute_reply": "2026-05-19T10:22:48.449542Z" + } + }, + "outputs": [], + "source": [ + "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", + "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", + "\n", + "gamma_net = np.zeros((len(X_test), 4))\n", + "mu_hat_net = np.zeros((len(X_test), 4))\n", + "\n", + "for treatment_id in range(4):\n", + " outcome_model = RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " )\n", + " outcome_model.fit(X_train[A_train == treatment_id], Y_net_train[A_train == treatment_id])\n", + " mu_a = outcome_model.predict(X_test)\n", + " observed_a = (A_test == treatment_id).astype(float)\n", + "\n", + " gamma_net[:, treatment_id] = mu_a + observed_a / e_hat_net[:, treatment_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, treatment_id] = mu_a" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0349d9ab", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:48.451518Z", + "iopub.status.busy": "2026-05-19T10:22:48.451432Z", + "iopub.status.idle": "2026-05-19T10:22:48.458073Z", + "shell.execute_reply": "2026-05-19T10:22:48.457597Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
samplee_hatmu_hatgamma
sample_idactual_armobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_support
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
33none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
\n", + "
" + ], + "text/plain": [ + " sample e_hat \\\n", + " sample_id actual_arm observed_net_outcome none \n", + "0 0 discount_plus_support 9254.558541 0.248633 \n", + "1 1 tech_support_only 7702.861662 0.211087 \n", + "2 2 tech_support_only 5062.184420 0.376570 \n", + "3 3 none 8930.495022 0.339653 \n", + "4 4 none 8156.575502 0.312265 \n", + "\n", + " mu_hat \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 0.267338 0.272366 0.211662 5370.099281 \n", + "1 0.307204 0.243394 0.238315 10468.942522 \n", + "2 0.241901 0.256261 0.125268 2879.741786 \n", + "3 0.194701 0.246505 0.219142 8458.779217 \n", + "4 0.250021 0.312123 0.125590 10137.469821 \n", + "\n", + " gamma \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", + "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", + "2 5449.409859 937.515103 2959.514347 2879.741786 \n", + "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", + "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", + "\n", + " \n", + " tech_support_only discount_only discount_plus_support \n", + "0 9559.777763 4547.484552 17708.540116 \n", + "1 9899.108508 8373.316633 11121.826609 \n", + "2 3848.652052 937.515103 2959.514347 \n", + "3 8254.372069 6054.598973 6560.028069 \n", + "4 3378.748035 5743.802939 2988.217314 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ids = np.arange(5)\n", + "\n", + "sample_prediction_table = pd.concat(\n", + " {\n", + " 'sample': pd.DataFrame({\n", + " 'sample_id': sample_ids,\n", + " 'actual_arm': pd.Series(A_test[sample_ids]).map(TREATMENT_NAMES).to_numpy(),\n", + " 'observed_net_outcome': Y_net_test[sample_ids],\n", + " }),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=TREATMENT_LABELS),\n", + " },\n", + " axis=1,\n", + ")\n", + "\n", + "sample_prediction_table" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "14b616ad", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:48.459287Z", + "iopub.status.busy": "2026-05-19T10:22:48.459210Z", + "iopub.status.idle": "2026-05-19T10:22:48.465436Z", + "shell.execute_reply": "2026-05-19T10:22:48.465098Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
policyvalueci_lowerci_upperrate_nonerate_tech_support_onlyrate_discount_onlyrate_discount_plus_support
4plugin_drlearner_4treatment11797.81314711270.04636912325.5799260.0920.4990.0350.374
6dr_policy_forest_4treatment11619.02719511094.85834412143.1960460.1140.4690.0000.417
5dr_policy_tree_4treatment11148.54966510700.70293811596.3963920.1990.4750.0000.326
1all_tech_support_only10526.5947669833.11012211220.0794100.0001.0000.0000.000
3all_discount_plus_support10136.7252889439.46167410833.9889030.0000.0000.0001.000
2all_discount_only7545.0926616993.5474078096.6379150.0000.0001.0000.000
0all_none7249.3454676955.4474327543.2435021.0000.0000.0000.000
\n", + "
" + ], + "text/plain": [ + " policy value ci_lower ci_upper \\\n", + "4 plugin_drlearner_4treatment 11797.813147 11270.046369 12325.579926 \n", + "6 dr_policy_forest_4treatment 11619.027195 11094.858344 12143.196046 \n", + "5 dr_policy_tree_4treatment 11148.549665 10700.702938 11596.396392 \n", + "1 all_tech_support_only 10526.594766 9833.110122 11220.079410 \n", + "3 all_discount_plus_support 10136.725288 9439.461674 10833.988903 \n", + "2 all_discount_only 7545.092661 6993.547407 8096.637915 \n", + "0 all_none 7249.345467 6955.447432 7543.243502 \n", + "\n", + " rate_none rate_tech_support_only rate_discount_only \\\n", + "4 0.092 0.499 0.035 \n", + "6 0.114 0.469 0.000 \n", + "5 0.199 0.475 0.000 \n", + "1 0.000 1.000 0.000 \n", + "3 0.000 0.000 0.000 \n", + "2 0.000 0.000 1.000 \n", + "0 1.000 0.000 0.000 \n", + "\n", + " rate_discount_plus_support \n", + "4 0.374 \n", + "6 0.417 \n", + "5 0.326 \n", + "1 0.000 \n", + "3 1.000 \n", + "2 0.000 \n", + "0 0.000 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "policy_assignments = {\n", + " **{f'all_{treatment_name}': np.full(len(X_test), treatment_id) for treatment_id, treatment_name in TREATMENT_NAMES.items()},\n", + " 'plugin_drlearner_4treatment': pi_plugin,\n", + " 'dr_policy_tree_4treatment': pi_tree,\n", + " 'dr_policy_forest_4treatment': pi_forest,\n", + "}\n", + "\n", + "eval_rows = []\n", + "for policy_name, policy_assignment in policy_assignments.items():\n", + " pi = np.asarray(policy_assignment).astype(int).ravel()\n", + " scores = gamma_net[np.arange(len(pi)), pi]\n", + "\n", + " row = {\n", + " 'policy': policy_name,\n", + " 'value': scores.mean(),\n", + " 'value_se': scores.std(ddof=1) / np.sqrt(len(scores)),\n", + " }\n", + " for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " row[f'rate_{treatment_name}'] = np.mean(pi == treatment_id)\n", + " eval_rows.append(row)\n", + "\n", + "policy_eval = pd.DataFrame(eval_rows)\n", + "policy_eval['ci_lower'] = policy_eval['value'] - 1.96 * policy_eval['value_se']\n", + "policy_eval['ci_upper'] = policy_eval['value'] + 1.96 * policy_eval['value_se']\n", + "\n", + "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in TREATMENT_LABELS]\n", + "policy_eval.sort_values('value', ascending=False)[display_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4814d1ed", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:48.466945Z", + "iopub.status.busy": "2026-05-19T10:22:48.466866Z", + "iopub.status.idle": "2026-05-19T10:22:48.581481Z", + "shell.execute_reply": "2026-05-19T10:22:48.581072Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", + "plot_df = policy_eval.sort_values('value', ascending=False)\n", + "sns.barplot(data=plot_df, x='value', y='policy', ax=axes[0], color='seagreen')\n", + "axes[0].set_title('4-way net policy value')\n", + "axes[0].set_xlabel('AIPW-estimated net value')\n", + "axes[0].set_ylabel('')\n", + "\n", + "treatment_rate_cols = [f'rate_{name}' for name in TREATMENT_LABELS]\n", + "rate_df = policy_eval.set_index('policy')[treatment_rate_cols]\n", + "rate_df.columns = TREATMENT_LABELS\n", + "rate_df.loc[['plugin_drlearner_4treatment', 'dr_policy_tree_4treatment', 'dr_policy_forest_4treatment']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", + "axes[1].set_title('Assigned treatment share')\n", + "axes[1].set_xlabel('Share')\n", + "axes[1].set_ylabel('')\n", + "axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "dec4e9f6", + "metadata": {}, + "source": [ + "All three learned policies achieve higher value than any constant policy, confirming that assigning different treatments by customer is more effective than treating everyone the same.\n", + "\n", + "Among the learned policies, DRLearner plug-in (11,810) ranked first, followed by DRPolicyForest (11,626) and DRPolicyTree (11,148). However, since the plug-in and forest confidence intervals overlap substantially, the difference between them is not statistically clear. In theory, DR policies that directly optimize the AIPW score should have an advantage, but in practice the outcome depends on sample size and data characteristics. The tree policy trades some performance for an interpretable structure.\n", + "\n", + "Among constant policies, `all_tech_support_only` (10,527) performed best. `all_discount_plus_support` (10,137) falls short because discount costs scale with customer size, so for some customers the discount benefit does not sufficiently offset the cost." + ] + }, + { + "cell_type": "markdown", + "id": "9bh57tga2aq", + "metadata": {}, + "source": [ + "## Policy Comparison under Budget Constraints\n", + "\n", + "So far we have compared policies by their overall AIPW value. In practice, however, budgets are limited. Cost curves are useful for comparing which policy is more efficient within the same budget." + ] + }, + { + "cell_type": "markdown", + "id": "qdxockulyq9", + "metadata": {}, + "source": [ + "### Estimating Per-Customer Expected Costs\n", + "\n", + "To construct cost curves, we first estimate $E[C(a) \\mid X_i]$ per customer. Since actual costs are observed only for the treatment the customer received, we fit a separate cost regression model on the training data for each treatment and apply it to the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a1z6z7eabyj", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:48.582872Z", + "iopub.status.busy": "2026-05-19T10:22:48.582772Z", + "iopub.status.idle": "2026-05-19T10:22:48.959039Z", + "shell.execute_reply": "2026-05-19T10:22:48.958612Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated E[C(a)|X] — test set (mean ± std):\n", + " tech_support_only : 4,892 ± 3,466\n", + " discount_only : 5,454 ± 2,330\n", + " discount_plus_support : 10,687 ± 4,139\n" + ] + } + ], + "source": [ + "# Estimate E[C(a) | X] per treatment\n", + "# c_hat_test[i, a] = predicted cost for customer i under treatment a\n", + "c_true_by_treatment = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", + "\n", + "c_hat_test = np.zeros((len(X_test), 4)) # treatment 0 → cost = 0\n", + "for treatment_id in [1, 2, 3]:\n", + " mask_train = (A_train == treatment_id)\n", + " mdl = RandomForestRegressor(n_estimators=300, min_samples_leaf=20,\n", + " random_state=SEED, n_jobs=1)\n", + " mdl.fit(X_train[mask_train], c_true_by_treatment[treatment_id][train_idx[mask_train]])\n", + " c_hat_test[:, treatment_id] = np.maximum(mdl.predict(X_test), 200.0)\n", + "\n", + "print(\"Estimated E[C(a)|X] — test set (mean ± std):\")\n", + "for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " if treatment_id > 0:\n", + " print(f\" {treatment_name:25s}: {c_hat_test[:, treatment_id].mean():8,.0f} ± {c_hat_test[:, treatment_id].std():6,.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dgt4sg335xc", + "metadata": {}, + "source": [ + "### Cost Curves and Budget-Constrained Comparison\n", + "\n", + "The two axes of a cost curve are:\n", + "\n", + "- **x-axis**: cumulative average treatment cost per customer\n", + "- **y-axis**: cumulative average net profit per customer — the increment relative to baseline(`none`), with **costs already deducted**\n", + "\n", + "Within each policy, customers are treated in descending order of their benefit-to-cost ratio $\\hat\\rho(x) = \\hat\\tau(x) / \\hat\\gamma(x)$. Drawing a vertical line at $x = B$ gives the budget-$B$ policy comparison. A higher $y$ value at the same budget means a more efficient policy.\n", + "\n", + "The endpoint (\\u25cf) of each curve shows the position when all targeted customers have been treated. Customers assigned `none` incur no treatment cost and therefore do not appear in the curve." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ttwi6dbqcfo", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:22:48.960221Z", + "iopub.status.busy": "2026-05-19T10:22:48.960131Z", + "iopub.status.idle": "2026-05-19T10:22:49.032643Z", + "shell.execute_reply": "2026-05-19T10:22:49.032239Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def policy_cost_curve(pi_eval, gamma_matrix, c_hat_matrix):\n", + " \"\"\"\n", + " Generate cost curves using per-treatment estimated customer costs.\n", + " c_hat_matrix[i, a] = Ê[C(a) | X_i]\n", + "\n", + " x = cumulative average treatment cost per customer (gross)\n", + " y = cumulative average net profit per customer (costs already deducted from Y_net)\n", + "\n", + " Treated customers are sorted in descending order of net profit / estimated cost ratio.\n", + " \"\"\"\n", + " pi = np.asarray(pi_eval).ravel().astype(int)\n", + " n = len(pi)\n", + " treated = pi > 0\n", + " if treated.sum() == 0:\n", + " return np.array([0.0]), np.array([0.0])\n", + "\n", + " benefit = gamma_matrix[np.arange(n), pi] - gamma_matrix[:, 0]\n", + " cost = c_hat_matrix[np.arange(n), pi].astype(float)\n", + "\n", + " b_t, c_t = benefit[treated], cost[treated]\n", + " ratio = np.where(c_t > 0, b_t / c_t, b_t)\n", + " order = np.argsort(-ratio)\n", + "\n", + " cum_cost = np.r_[0, np.cumsum(c_t[order])]\n", + " cum_benefit = np.r_[0, np.cumsum(b_t[order])]\n", + " return cum_cost / n, cum_benefit / n\n", + "\n", + "\n", + "n_test = len(X_test)\n", + "policies_curve = [\n", + " ('DRLearner plug-in', pi_plugin),\n", + " ('DRPolicyTree (depth=2)', pi_tree),\n", + " ('DRPolicyForest', pi_forest),\n", + " ('all_discount_plus_support', np.full(n_test, 3)),\n", + " ('all_tech_support_only', np.full(n_test, 1)),\n", + "]\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']\n", + "\n", + "for (name, pi_eval), color in zip(policies_curve, colors):\n", + " cc, cg = policy_cost_curve(pi_eval, gamma_net, c_hat_test)\n", + " ax.plot(cc, cg, lw=2, color=color,\n", + " label=f'{name} (cost={cc[-1]:,.0f}, net benefit={cg[-1]:,.0f})')\n", + " ax.scatter([cc[-1]], [cg[-1]], s=50, color=color, zorder=5)\n", + "\n", + "budget_example = 5_000\n", + "ax.axvline(budget_example, color='gray', lw=1.5, ls='--', alpha=0.7,\n", + " label=f'Budget = ${budget_example:,}/customer')\n", + "\n", + "ax.set_xlabel('Avg cumulative cost per customer ($)', fontsize=11)\n", + "ax.set_ylabel('Avg cumulative net benefit per customer ($)', fontsize=11)\n", + "ax.set_title('Policy cost curves — cut at x=B for budget-constrained comparison', fontsize=11)\n", + "ax.legend(fontsize=8, loc='upper left')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "qoq9ch9m2fg", + "metadata": {}, + "source": [ + "The most effective policy varies with the available budget. At low budgets of \\$1,000\\u2013\\$3,000, `all_tech_support_only` is the most efficient. Its lower average cost means that even a small budget can reach many high benefit-to-cost customers quickly.\n", + "\n", + "As the budget grows to \\$4,000\\u2013\\$6,000, the learned policies (plug-in, forest) pull ahead. Customer-level targeting starts paying off, delivering higher net profit for the same spend.\n", + "\n", + "Even when budgets are fully exhausted, the learned policies remain most efficient. Plug-in spends approximately \\$6,600 to yield \\$4,561 in net profit, and forest spends around \\$6,900 for \\$4,376. By contrast, `all_discount_plus_support` exhausts \\$10,700 and still achieves only \\$2,887 in net profit, making it the least efficient in terms of budget utilization." + ] + }, + { + "cell_type": "markdown", + "id": "b40f71c0", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html)\n", + "- [Athey and Wager (2021, Econometrica) — Policy Learning with Observational Data](https://arxiv.org/abs/1702.02896)\n", + "- [Sun, Du, Wager et al. (2021) — Treatment Allocation under Uncertain Costs](https://arxiv.org/abs/2103.11066)\n", + "- [Imai and Li (2019) — Experimental Evaluation of Individualized Treatment Rules](https://arxiv.org/pdf/1905.05389.pdf)\n", + "- [arXiv 2604.06123](https://arxiv.org/html/2604.06123v1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/who_should_be_treated/who_should_be_treated_ko.ipynb b/book/who_should_be_treated/who_should_be_treated_ko.ipynb new file mode 100644 index 0000000..a2938f3 --- /dev/null +++ b/book/who_should_be_treated/who_should_be_treated_ko.ipynb @@ -0,0 +1,2596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ae5a6c4", + "metadata": {}, + "source": [ + "**🌐 언어:** [← English](/who-should-be-treated-en) | **한국어**\n", + "\n", + "# 누구에게 어떤 프로모션을 제공해야 할까요?\n", + "\n", + "인과추론의 많은 문제는 평균 처치 효과(ATE)에 집중합니다. 예를 들어 “프로모션을 진행할 것인가, 하지 않을 것인가?”라는 질문은 모든 고객에게 동일한 프로모션을 제공하는 정책과 아무에게도 제공하지 않는 정책을 비교하는 문제입니다.\n", + "\n", + "하지만 예산이 제한되어 있고 그 안에서 최대 효과를 내야 한다면 이야기가 달라집니다. 프로모션 효과가 고객마다 다르다면, 모두에게 동일하게 제공하는 전략이 최선이 아닐 수 있습니다.\n", + "\n", + "- 마케팅 유무와 상관없이 어차피 구매하거나(always-converters), 반대로 아무리 혜택을 줘도 사지 않을 고객(lost causes)에게 불필요한 마케팅 예산을 낭비하게 됩니다.\n", + "- 마케팅에 부정적으로 반응하는 고객(sleeping dogs) 때문에 오히려 손실이 발생하며,\n", + "- 정작 효과가 클 고객(high-responders)을 제대로 우선순위에 두지 못해 잠재 매출을 놓칩니다.\n", + "\n", + "따라서 핵심 질문은 다음과 같습니다.\n", + "\n", + "_\"어떤 고객을 대상으로 해야 순이익이 가장 커질까?\"_\n", + "\n", + "이번에는 가능한 처치 규칙들을 탐색해 전체 평균 순수익을 근사적으로 최대화하는 방법을 배웁니다. 다시 말해, “누구를 처치해야 하는가?”라는 질문에 답하는 문제를 다룹니다. 우리는 이를 정책학습(policy learning)이라고 부릅니다." + ] + }, + { + "cell_type": "markdown", + "id": "2b06ptb53eu", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fc2741f9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:42.634047Z", + "iopub.status.busy": "2026-05-19T10:15:42.633929Z", + "iopub.status.idle": "2026-05-19T10:15:44.535110Z", + "shell.execute_reply": "2026-05-19T10:15:44.534527Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import tempfile\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "os.environ.setdefault(\"MPLCONFIGDIR\", str(Path(tempfile.gettempdir()) / \"matplotlib\"))\n", + "os.environ.setdefault(\"MPLBACKEND\", \"Agg\")\n", + "os.environ.setdefault(\"LOKY_MAX_CPU_COUNT\", \"8\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from econml.dr import DRLearner\n", + "from econml.policy import DRPolicyForest, DRPolicyTree\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 1\n", + "rng = np.random.default_rng(SEED)\n", + "np.random.seed(SEED)\n", + "sns.set_theme(style='whitegrid')" + ] + }, + { + "cell_type": "markdown", + "id": "8b741c26", + "metadata": {}, + "source": [ + "## 정책학습 문제 정의\n", + "\n", + "한 소프트웨어 판매 회사는 할인이나 기술지원 같은 프로모션이 고객의 소프트웨어 구매를 실제로 늘리는지, 그리고 어떤 고객에게 더 효과적인지 알고 싶어합니다.\n", + "\n", + "고객마다 서로 다른 프로모션을 무작위로 배정하는 실험(RCT)을 수행하는 것이 가장 이상적이지만 실제 비즈니스 환경에서는 비용 문제, 영업 전략, 대형 고객을 놓칠 위험 등으로 인해 무작위 실험을 수행하기 어렵습니다. 따라서 이번 데이터는 무작위 실험 데이터가 아닌 관찰 데이터입니다.\n", + "\n", + "데이터에는 약 2,000명의 고객 정보가 포함되어 있으며, 다음과 같은 변수들로 구성됩니다.\n", + "\n", + "- 고객 특성: 각 고객의 산업 분야, 규모, 매출, 기술 프로필에 대한 세부 정보.\n", + "- 개입: 고객에게 제공된 프로모션에 대한 정보.\n", + "- 결과: 프로모션 제공 이후 1년 동안의 소프트웨어 구매 금액\n", + "\n", + "| 변수명 | 타입 | 설명 |\n", + "| --------------- | -- | -------------------------------------------------------------- |\n", + "| Global Flag | X | 고객이 글로벌 오피스(해외 지사)를 보유하고 있는지 여부 |\n", + "| Major Flag | X | 고객이 해당 산업에서 대규모 소비 고객인지 여부 (SMC 또는 SMB와 구분) |\n", + "| SMC Flag | X | 고객이 중소기업(SMC, Small Medium Corporation)인지 여부 (Major 및 SMB와 구분) |\n", + "| Commercial Flag | X | 고객의 사업 유형이 상업용(Commercial)인지 여부 (공공 부문과 구분) |\n", + "| IT Spend | X | IT 관련 구매에 사용한 금액 |\n", + "| Employee Count | X | 직원 수 |\n", + "| PC Count | X | 고객이 사용하는 PC 수 |\n", + "| Size | X | 연간 총매출 기준 고객 규모 |\n", + "| Tech Support | T | 고객이 기술 지원(Tech Support)을 받았는지 여부 (이진값) |\n", + "| Discount | T | 고객이 할인 혜택을 받았는지 여부 (이진값) |\n", + "| Revenue | Y | 고객의 소프트웨어 구매 금액 기준 매출(Revenue) |\n", + "\n", + "\n", + "`Tech Support`와 `Discount`는 모두 개입(intervention)이므로, 이를 조합해 다음과 같이 네 가지 처치로 정의합니다.\n", + "\n", + "$$\n", + "A_i \\in \\{0, 1, 2, 3\\}\n", + "$$\n", + "\n", + "각 처치는 다음을 의미합니다.\n", + "- $A_i = 0$: 아무 개입도 제공하지 않음\n", + "- $A_i = 1$: 기술지원만 제공\n", + "- $A_i = 2$: 할인만 제공\n", + "- $A_i = 3$: 기술지원과 할인을 모두 제공" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "477aed7a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:44.536653Z", + "iopub.status.busy": "2026-05-19T10:15:44.536512Z", + "iopub.status.idle": "2026-05-19T10:15:44.546802Z", + "shell.execute_reply": "2026-05-19T10:15:44.546382Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2000, 13)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Global FlagMajor FlagSMC FlagCommercial FlagIT SpendEmployee CountPC CountSizeTech SupportDiscountRevenuetreatmenttreatment_name
010104553726261522050117688.363002discount_only
1001120842107701590380114981.435592discount_only
20001821711072649351132917.138943discount_plus_support
30000302884039775221114773.768553discount_plus_support
40010259303743914461117098.698233discount_plus_support
\n", + "
" + ], + "text/plain": [ + " Global Flag Major Flag SMC Flag Commercial Flag IT Spend \\\n", + "0 1 0 1 0 45537 \n", + "1 0 0 1 1 20842 \n", + "2 0 0 0 1 82171 \n", + "3 0 0 0 0 30288 \n", + "4 0 0 1 0 25930 \n", + "\n", + " Employee Count PC Count Size Tech Support Discount Revenue \\\n", + "0 26 26 152205 0 1 17688.36300 \n", + "1 107 70 159038 0 1 14981.43559 \n", + "2 10 7 264935 1 1 32917.13894 \n", + "3 40 39 77522 1 1 14773.76855 \n", + "4 37 43 91446 1 1 17098.69823 \n", + "\n", + " treatment treatment_name \n", + "0 2 discount_only \n", + "1 2 discount_only \n", + "2 3 discount_plus_support \n", + "3 3 discount_plus_support \n", + "4 3 discount_plus_support " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('./data/multi_attribution_sample.csv')\n", + "data.columns = data.columns.str.strip()\n", + "\n", + "covariates = [\n", + " 'Global Flag',\n", + " 'Major Flag',\n", + " 'SMC Flag',\n", + " 'Commercial Flag',\n", + " 'IT Spend',\n", + " 'Employee Count',\n", + " 'PC Count',\n", + " 'Size',\n", + "]\n", + "outcome = 'Revenue'\n", + "\n", + "TREATMENT_NAMES = {\n", + " 0: 'none',\n", + " 1: 'tech_support_only',\n", + " 2: 'discount_only',\n", + " 3: 'discount_plus_support',\n", + "}\n", + "TREATMENT_LABELS = [TREATMENT_NAMES[i] for i in range(4)]\n", + "\n", + "required_columns = covariates + ['Tech Support', 'Discount', outcome]\n", + "for col in required_columns:\n", + " data[col] = pd.to_numeric(data[col], errors='coerce')\n", + "\n", + "policy_df = data[required_columns].dropna().copy()\n", + "policy_df['treatment'] = (\n", + " 2 * policy_df['Discount'].astype(int)\n", + " + policy_df['Tech Support'].astype(int)\n", + ").astype(int)\n", + "policy_df['treatment_name'] = policy_df['treatment'].map(TREATMENT_NAMES)\n", + "\n", + "n = len(policy_df)\n", + "X = policy_df[covariates].to_numpy()\n", + "Y = policy_df[outcome].to_numpy(dtype=float)\n", + "A = policy_df['treatment'].to_numpy(dtype=int)\n", + "\n", + "print(policy_df.shape)\n", + "policy_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8bfa27b6", + "metadata": {}, + "source": [ + "### 비용 설정\n", + "\n", + "수익 극대화를 위해서는 매출과 함께 비용도 고려해야 합니다. 또한 동일한 프로모션이라도 고객 특성에 따라 비용이 달라질 수 있습니다.\n", + "\n", + "예를 들어 기술지원은 직원 수가 많은 기업일수록 더 많은 지원 시간이 필요할 수 있고, 할인 역시 규모가 큰 고객일수록 더 큰 할인 폭을 요구할 수 있습니다.\n", + "\n", + "하지만 이 데이터에는 실제 비용 정보가 포함되어 있지 않기 때문에 고객 특성에 따라 달라지는 비용을 시뮬레이션해 사용합니다.\n", + "\n", + "기술지원 비용은 직원 수가 많을수록 증가하도록 설정하고, 할인 비용은 고객 규모가 클수록 증가하도록 설정합니다.\n", + "\n", + "이때 로그정규분포를 사용해 비용은 항상 양수이고, 일부 고객에서 큰 비용이 발생할 수 있는 분포를 반영합니다.\n", + "\n", + "$$\n", + "C_i(\\text{tech}) \\sim \\text{LogNormal}(\\log C_{\\text{tech}} + 0.5 \\cdot \\tilde{e}_i,\\ 0.3)\n", + "$$\n", + "\n", + "$$\n", + "C_i(\\text{disc}) \\sim \\text{LogNormal}(\\log C_{\\text{disc}} + 0.4 \\cdot \\tilde{s}_i,\\ 0.3)\n", + "$$\n", + "\n", + "여기서 $\\tilde{e}_i$는 표준화된 직원 수, $\\tilde{s}_i$는 표준화된 회사 규모입니다. \n", + "\n", + "이렇게 정의한 고객별 비용을 반영한 최종 순이익(net outcome)은 다음과 같습니다.\n", + "\n", + "$$\n", + "Y_i^{net} = Y_i - C_i(A_i)\n", + "$$\n", + "\n", + "결과적으로 AIPW score가 순이익을 기준으로 계산되므로, 비용을 고려한 정책 최적화와 평가가 가능해집니다." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5174ddd2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:44.548132Z", + "iopub.status.busy": "2026-05-19T10:15:44.548063Z", + "iopub.status.idle": "2026-05-19T10:15:44.553363Z", + "shell.execute_reply": "2026-05-19T10:15:44.552896Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmentmean_coststd_costmean_revenuemean_net_revenue
0none0.0000000.0000006585.8917926585.891792
1tech_support_only5037.8158944917.64135815104.11153410066.295640
2discount_only5181.5703343043.34317212247.9359537066.365619
3discount_plus_support12665.2832967375.49012526784.12464914118.841353
\n", + "
" + ], + "text/plain": [ + " treatment mean_cost std_cost mean_revenue \\\n", + "0 none 0.000000 0.000000 6585.891792 \n", + "1 tech_support_only 5037.815894 4917.641358 15104.111534 \n", + "2 discount_only 5181.570334 3043.343172 12247.935953 \n", + "3 discount_plus_support 12665.283296 7375.490125 26784.124649 \n", + "\n", + " mean_net_revenue \n", + "0 6585.891792 \n", + "1 10066.295640 \n", + "2 7066.365619 \n", + "3 14118.841353 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COST_TECH_SUPPORT = 4_000.0 # baseline cost for tech support\n", + "COST_DISCOUNT = 5_000.0 # baseline cost for discount\n", + "\n", + "\n", + "rng_c = np.random.default_rng(SEED + 99)\n", + "emp_idx = covariates.index('Employee Count')\n", + "size_idx = covariates.index('Size')\n", + "\n", + "emp_z = (X[:, emp_idx] - X[:, emp_idx].mean()) / (X[:, emp_idx].std() + 1e-8)\n", + "size_z = (X[:, size_idx] - X[:, size_idx].mean()) / (X[:, size_idx].std() + 1e-8)\n", + "\n", + "c_tech = rng_c.lognormal(mean=np.log(COST_TECH_SUPPORT) + 0.5 * emp_z, sigma=0.3)\n", + "c_disc = rng_c.lognormal(mean=np.log(COST_DISCOUNT) + 0.4 * size_z, sigma=0.3)\n", + "\n", + "c_tech = np.clip(c_tech, 200.0, 40_000.0)\n", + "c_disc = np.clip(c_disc, 200.0, 30_000.0)\n", + "\n", + "C_obs = np.select(\n", + " [A == 1, A == 2, A == 3],\n", + " [c_tech, c_disc, c_tech + c_disc],\n", + " default=0.0,\n", + ")\n", + "\n", + "Y_net = Y - C_obs\n", + "\n", + "pd.DataFrame({\n", + " 'treatment': TREATMENT_LABELS,\n", + " 'mean_cost': [C_obs[A == a].mean() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'std_cost': [C_obs[A == a].std() if (A == a).sum() > 0 else 0.0 for a in range(4)],\n", + " 'mean_revenue': [Y[A == a].mean() for a in range(4)],\n", + " 'mean_net_revenue': [Y_net[A == a].mean() for a in range(4)],\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b6f2c6a1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:44.554403Z", + "iopub.status.busy": "2026-05-19T10:15:44.554329Z", + "iopub.status.idle": "2026-05-19T10:15:44.659534Z", + "shell.execute_reply": "2026-05-19T10:15:44.658911Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "sns.histplot(c_tech, bins=30, stat='density', element='step', fill=True, ax=axes[0], color='tab:blue')\n", + "axes[0].axvline(np.median(c_tech), color='black', ls='--', lw=1, alpha=0.7)\n", + "axes[0].set_title('Tech support cost distribution')\n", + "axes[0].set_xlabel('Cost')\n", + "axes[0].set_ylabel('Density')\n", + "axes[0].set_xlim(left=0)\n", + "\n", + "sns.histplot(c_disc, bins=30, stat='density', element='step', fill=True, ax=axes[1], color='tab:orange')\n", + "axes[1].axvline(np.median(c_disc), color='black', ls='--', lw=1, alpha=0.7)\n", + "axes[1].set_title('Discount cost distribution')\n", + "axes[1].set_xlabel('Cost')\n", + "axes[1].set_ylabel('Density')\n", + "axes[1].set_xlim(left=0)\n", + "\n", + "plt.suptitle('Simulated cost distributions', y=1.02)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "dc34dc79", + "metadata": {}, + "source": [ + "### 데이터 분할\n", + "\n", + "정책학습과 평가를 같은 데이터로 수행하면 과대추정이 발생할 수 있습니다. 따라서 데이터를 train/test로 나눠 학습과 평가를 분리합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1272f0ea", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:44.660687Z", + "iopub.status.busy": "2026-05-19T10:15:44.660549Z", + "iopub.status.idle": "2026-05-19T10:15:44.663934Z", + "shell.execute_reply": "2026-05-19T10:15:44.663307Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: 1000 \n", + "Test: 1000\n" + ] + } + ], + "source": [ + "idx = np.arange(len(policy_df))\n", + "train_idx, test_idx = train_test_split(\n", + " idx,\n", + " test_size=0.5,\n", + " stratify=A,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "X_train = X[train_idx]\n", + "X_test = X[test_idx]\n", + "A_train = A[train_idx]\n", + "A_test = A[test_idx]\n", + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "print(f\"Train: {len(train_idx)} \\nTest: {len(test_idx)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "024e5803", + "metadata": {}, + "source": [ + "### 데이터 탐색 및 진단\n", + "\n", + "정책학습에 들어가기 전에, 먼저 네 가지 처치 조합($A \\in {0,1,2,3}$)에 대해 데이터의 구조를 점검합니다.\n", + "\n", + "1. 처치별 표본 수: 각 처치에 충분한 관측치가 존재하는가?\n", + "2. 처치별 고객 특성 평균: 어떤 고객군이 특정 처치를 받는 경향이 있는가?\n", + "3. 처치별 Revenue 분포: Revenue의 규모, 분산, 이상치 분포가 처치마다 어떻게 다른가?\n", + "4. Positivity Check: 고객 특성 전반에서 각 처치가 충분히 함께 관측되는가?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3d0f329e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:44.665166Z", + "iopub.status.busy": "2026-05-19T10:15:44.665085Z", + "iopub.status.idle": "2026-05-19T10:15:44.669148Z", + "shell.execute_reply": "2026-05-19T10:15:44.668604Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countshare
treatment
none5170.2585
tech_support_only4620.2310
discount_only4770.2385
discount_plus_support5440.2720
\n", + "
" + ], + "text/plain": [ + " count share\n", + "treatment \n", + "none 517 0.2585\n", + "tech_support_only 462 0.2310\n", + "discount_only 477 0.2385\n", + "discount_plus_support 544 0.2720" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_counts = policy_df['treatment_name'].value_counts().reindex(TREATMENT_LABELS).rename_axis('treatment').to_frame('count')\n", + "treatment_counts['share'] = treatment_counts['count'] / len(policy_df)\n", + "\n", + "display(treatment_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "8e911f32", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.072480Z", + "iopub.status.busy": "2026-05-09T16:49:48.072405Z", + "iopub.status.idle": "2026-05-09T16:49:48.076539Z", + "shell.execute_reply": "2026-05-09T16:49:48.076070Z" + } + }, + "source": [ + "실제 표본 수는 `none` 517명, `tech_support_only` 462명, `discount_only` 477명, `discount_plus_support` 544명입니다. 각 처치의 비중은 23.1%에서 27.2% 사이로 비교적 고르게 분포되어 있으며, 특정 처치에 표본이 극단적으로 부족한 문제는 보이지 않습니다.\n", + "\n", + "따라서 네 가지 처치를 비교하는 정책학습을 진행하기에 표본 분포는 안정적인 편입니다." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6488c62", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:44.670180Z", + "iopub.status.busy": "2026-05-19T10:15:44.670112Z", + "iopub.status.idle": "2026-05-19T10:15:44.764131Z", + "shell.execute_reply": "2026-05-19T10:15:44.763723Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_covariate_means = (\n", + " policy_df\n", + " .groupby('treatment_name')[covariates]\n", + " .mean()\n", + " .reindex(TREATMENT_LABELS)\n", + ")\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "sns.heatmap(treatment_covariate_means.T, annot=True, fmt='.2f', cmap='YlGnBu', cbar_kws={'label': 'Mean'})\n", + "plt.xlabel('Treatments')\n", + "plt.ylabel('Covariate')\n", + "plt.title('Mean customer characteristics by treatments')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "c49e9856", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.078246Z", + "iopub.status.busy": "2026-05-09T16:49:48.078155Z", + "iopub.status.idle": "2026-05-09T16:49:48.194453Z", + "shell.execute_reply": "2026-05-09T16:49:48.193850Z" + } + }, + "source": [ + "처치별 고객 특성은 완전히 동일하지 않습니다. `Size`와 `IT Spend`의 평균이 처치 그룹별로 다르게 나타나며, 특히 `discount_plus_support` 그룹에서 큰 고객이 더 많이 포함되어 있습니다. 도메인적으로도 고객 규모나 IT 지출 수준에 따라 필요한 지원의 형태가 달라질 수 있습니다.\n", + "\n", + "이런 차이는 처치 효과가 고객 특성에 따라 달라질 가능성을 시사합니다. 따라서 이후 정책 학습과 평가는 이러한 차이를 반영할 수 있도록 AIPW를 사용합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c925a3d8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:44.765352Z", + "iopub.status.busy": "2026-05-19T10:15:44.765259Z", + "iopub.status.idle": "2026-05-19T10:15:44.824660Z", + "shell.execute_reply": "2026-05-19T10:15:44.824209Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmenttreatment_namecountmeanmedianstdminmax
00none5176585.8917926123.1870673363.163462-616.57245121445.05937
11tech_support_only46215104.11153414483.7193205400.3198584619.49136140166.67407
22discount_only47712247.93595310454.9325007472.178476889.97565341818.39213
33discount_plus_support54426784.12464923560.25289013124.9680835903.90688086006.92445
\n", + "
" + ], + "text/plain": [ + " treatment treatment_name count mean median \\\n", + "0 0 none 517 6585.891792 6123.187067 \n", + "1 1 tech_support_only 462 15104.111534 14483.719320 \n", + "2 2 discount_only 477 12247.935953 10454.932500 \n", + "3 3 discount_plus_support 544 26784.124649 23560.252890 \n", + "\n", + " std min max \n", + "0 3363.163462 -616.572451 21445.05937 \n", + "1 5400.319858 4619.491361 40166.67407 \n", + "2 7472.178476 889.975653 41818.39213 \n", + "3 13124.968083 5903.906880 86006.92445 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "treatment_revenue = (\n", + " policy_df\n", + " .groupby(['treatment', 'treatment_name'])[outcome]\n", + " .agg(['count', 'mean', 'median', 'std', 'min', 'max'])\n", + " .reset_index()\n", + ")\n", + "display(treatment_revenue)\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "\n", + "sns.histplot(\n", + " data=policy_df,\n", + " x=outcome,\n", + " hue='treatment_name',\n", + " bins=40,\n", + " element='step',\n", + " stat='density',\n", + " common_norm=False\n", + ")\n", + "\n", + "plt.title('Revenue density by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "639d2437", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.195732Z", + "iopub.status.busy": "2026-05-09T16:49:48.195645Z", + "iopub.status.idle": "2026-05-09T16:49:48.262584Z", + "shell.execute_reply": "2026-05-09T16:49:48.262041Z" + } + }, + "source": [ + "Revenue 평균은 `none` 약 6,586, `discount_only` 약 12,248, `tech_support_only` 약 15,104, `discount_plus_support` 약 26,784로 나타납니다.\n", + "\n", + "앞서 확인했듯이 규모가 큰 고객일수록 더 강한 개입을 받는 경향이 있기 때문에 평균 차이를 그대로 정책을 평가하면 안 됩니다. 즉, Revenue 차이에는 고객 특성의 영향과 실제 처치 효과가 함께 섞여 있습니다." + ] + }, + { + "cell_type": "markdown", + "id": "0b51rgb0z128", + "metadata": {}, + "source": [ + "### 식별 가정\n", + "\n", + "관찰 데이터에서 AIPW 추정이 인과적으로 유효하려면 두 가지 가정이 필요합니다.\n", + "\n", + "1. Unconfoundedness\n", + "\n", + " $$\\{Y_i(0), Y_i(1), Y_i(2), Y_i(3)\\} \\perp A_i \\mid X_i$$\n", + "\n", + " 관측된 공변량 $X_i$를 조건부로 했을 때, 처치 배정은 잠재 결과와 독립이어야 합니다. 즉, 고객 규모, IT 지출, 직원 수 등의 변수들이 처치 배정을 충분히 설명한다고 가정합니다.\n", + "\n", + "2. Positivity\n", + "\n", + " $$P(A_i = a \\mid X_i = x) > 0 \\quad \\forall a \\in \\{0,1,2,3\\},\\ \\forall x$$\n", + "\n", + " 모든 고객 특성 범위에서 각 처치가 어느 정도 관측되어야 합니다." + ] + }, + { + "cell_type": "markdown", + "id": "61b7329f", + "metadata": {}, + "source": [ + "이 중 Positivity는 propensity score를 추정해 직접 확인합니다.\n", + "\n", + "$$\n", + "e_a(x) = P(A_i = a \\mid X_i=x)\n", + "$$\n", + "\n", + "추가로 propensity score가 극단적으로 치우쳐 있지 않은지도 확인합니다. 만약 특정 고객군에서 어떤 처치의 $e_a(x)$ 값이 0에 매우 가까우면, 일부 관측치에 지나치게 큰 가중치가 부여되어 추정이 불안정해질 수 있습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9be31313", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:44.825901Z", + "iopub.status.busy": "2026-05-19T10:15:44.825818Z", + "iopub.status.idle": "2026-05-19T10:15:45.057620Z", + "shell.execute_reply": "2026-05-19T10:15:45.057263Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatmentmean_propensitymin_propensityp01_propensitypropensity_below_0_05_rate
0none0.2639410.0576000.0668310.0
1tech_support_only0.2298630.1265820.1562830.0
2discount_only0.2379340.1256380.1413220.0
3discount_plus_support0.2682630.0872990.1013040.0
\n", + "
" + ], + "text/plain": [ + " treatment mean_propensity min_propensity p01_propensity \\\n", + "0 none 0.263941 0.057600 0.066831 \n", + "1 tech_support_only 0.229863 0.126582 0.156283 \n", + "2 discount_only 0.237934 0.125638 0.141322 \n", + "3 discount_plus_support 0.268263 0.087299 0.101304 \n", + "\n", + " propensity_below_0_05_rate \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_propensity = RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "multi_propensity.fit(X_train, A_train)\n", + "e_hat_raw = multi_propensity.predict_proba(X_test)\n", + "\n", + "propensity_summary = pd.DataFrame({\n", + " 'treatment': TREATMENT_LABELS,\n", + " 'mean_propensity': e_hat_raw.mean(axis=0),\n", + " 'min_propensity': e_hat_raw.min(axis=0),\n", + " 'p01_propensity': np.quantile(e_hat_raw, 0.01, axis=0),\n", + " 'propensity_below_0_05_rate': (e_hat_raw < 0.05).mean(axis=0),\n", + "})\n", + "display(propensity_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "fl52yekx049", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:45.058956Z", + "iopub.status.busy": "2026-05-19T10:15:45.058865Z", + "iopub.status.idle": "2026-05-19T10:15:45.112248Z", + "shell.execute_reply": "2026-05-19T10:15:45.111874Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prop_df = pd.DataFrame(e_hat_raw, columns=TREATMENT_LABELS)\n", + "prop_long = prop_df.melt(var_name='treatment', value_name='propensity')\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "sns.histplot(\n", + " data=prop_long, x='propensity', hue='treatment',\n", + " bins=30, element='step', stat='density', common_norm=False, ax=ax,\n", + ")\n", + "\n", + "ax.axvline(0.05, color='tomato', lw=1.5, ls='--', alpha=0.8, label='threshold = 0.05')\n", + "ax.legend(title='Treatment', fontsize=8)\n", + "ax.set_xlabel('Propensity score')\n", + "ax.set_ylabel('Density')\n", + "ax.set_title('Propensity score distribution by treatment')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b42eca5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-09T16:49:48.264007Z", + "iopub.status.busy": "2026-05-09T16:49:48.263913Z", + "iopub.status.idle": "2026-05-09T16:49:48.571081Z", + "shell.execute_reply": "2026-05-09T16:49:48.570638Z" + } + }, + "source": [ + "이 데이터에서는 $\\hat e_a(X_i)$의 최솟값이 약 0.058이며, `e_hat < 0.05`인 경우도 관측되지 않았습니다. 즉, 극단적으로 작은 propensity score로 인해 일부 관측치에 과도한 가중치가 부여되는 문제는 크지 않아 보입니다.\n", + "\n", + "또한 그래프에서도 네 가지 처치의 propensity 분포가 전반적으로 잘 겹쳐 나타나므로, 뚜렷한 positivity 위반은 관찰되지 않습니다." + ] + }, + { + "cell_type": "markdown", + "id": "f4edd2d8", + "metadata": {}, + "source": [ + "## 정책학습 방법론\n", + "\n", + "세 가지 정책학습 방법을 비교합니다.\n", + "\n", + "- Plug-in Policy (DRLearner)\n", + "\n", + " 먼저 고객별 CATE를 추정한 뒤, 기대 순이익(net benefit)이 가장 큰 처치를 선택합니다.\n", + "\n", + " 정책 형태에 제약이 없는 유연한 접근이지만, 최종 성능은 CATE 추정 품질에 크게 영향을 받습니다.\n", + "\n", + "- DRPolicyTree\n", + "\n", + " DRPolicyTree는 AIPW score를 직접 최대화하도록 정책을 학습합니다.\n", + "\n", + " 정책 구조를 얕은 decision tree로 제한하기 때문에, 결과를 조건 분기 규칙으로 해석할 수 있습니다. 따라서 정책 해석과 설명이 중요한 상황에서 유용합니다.\n", + "\n", + "- DRPolicyForest\n", + "\n", + " DRPolicyForest 역시 AIPW score를 직접 최대화하지만, 단일 트리 대신 forest 구조를 사용합니다.\n", + "\n", + " 단일 tree는 구조가 단순하지만 분산이 커질 수 있습니다. 반면 forest는 여러 트리의 결과를 평균적으로 활용하기 때문에 분산을 줄이면서 더 안정적으로 이질적인 처치 효과를 학습할 수 있습니다.\n", + "\n", + " 대신 최종 정책을 하나의 명확한 조건 분기 규칙 형태로 해석하기는 더 어렵습니다.\n", + "\n", + "세 정책은 모두 train set에서 학습하고, 동일한 test set에서 정책 가치를 비교합니다. 학습에 사용한 데이터로 정책을 평가하면 과대추정이 발생하므로, 학습과 평가에 서로 다른 데이터를 사용하는 것이 중요합니다." + ] + }, + { + "cell_type": "markdown", + "id": "3c967473", + "metadata": {}, + "source": [ + "### Plug-in Policy\n", + "\n", + "Plug-in policy는 먼저 고객별 처치 효과(CATE)를 추정한 뒤, 그 값을 이용해 정책을 만드는 방식입니다.\n", + "\n", + "Binary treatment에서는 일반적으로 $\\hat\\tau(x) > 0$이면 처치하고, 비용이 있으면 $\\hat\\tau(x) > c(x)$일 때 처치합니다.\n", + "\n", + "여러 처치가 있는 경우에는 baseline 처치와의 상대효과를 비교합니다. 여기서는 `none`(처치 없음)을 baseline으로 두고, `DRLearner`로 다음 값을 추정합니다.\n", + "\n", + "$$\n", + "\\hat\\tau_a(x) = \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "\\qquad a \\in \\{1,2,3\\}\n", + "$$\n", + "\n", + "baseline 처치의 상대효과는 0이므로, 고객별로 다음 네 값을 비교합니다.\n", + "\n", + "$$\n", + "[0, \\hat\\tau_1(x), \\hat\\tau_2(x), \\hat\\tau_3(x)]\n", + "$$\n", + "\n", + "그리고 가장 큰 값을 갖는 처치를 선택합니다.\n", + "\n", + "$$\n", + "\\hat\\pi_{plugin}(x) = \\arg\\max_{a \\in \\{0,1,2,3\\}} \\widehat{\\mathbb{E}}[Y^{net}(a) - Y^{net}(0) \\mid X=x]\n", + "$$\n", + "\n", + "이 방식은 직관적이고 유연하지만, CATE 추정 품질에 크게 영향을 받습니다. 따라서 학습된 정책은 별도의 test set에서 AIPW value로 평가합니다.\n", + "\n", + "> **NOTE** \"각 고객의 $\\hat\\Gamma_{i,a}$ 중 가장 큰 처치를 선택하면 되지 않나?\"라고 생각할 수 있습니다. 그러나 AIPW score는 개별 관측치 수준에서 분산이 매우 크기 때문에, pointwise argmax로 만든 정책은 노이즈에 과적합됩니다. AIPW score는 반드시 집계된 형태로 사용해야 합니다. 정책 평가(value 추정, 비용 곡선 등)처럼 여러 관측치를 합산하거나, DRPolicyTree/Forest처럼 노드 단위 평균을 최적화하거나, DRLearner처럼 pseudo-outcome으로 회귀하는 방식이 그 예입니다." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "debd92a9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:45.113789Z", + "iopub.status.busy": "2026-05-19T10:15:45.113691Z", + "iopub.status.idle": "2026-05-19T10:15:47.607303Z", + "shell.execute_reply": "2026-05-19T10:15:47.606773Z" + } + }, + "outputs": [], + "source": [ + "dr_cate = DRLearner(\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_final=RandomForestRegressor(\n", + " n_estimators=300,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_cate.fit(Y_net_train, A_train, X=X_train)\n", + "\n", + "# none을 baseline으로, 나머지 처치별 E[Y_net(a) - Y_net(0) | X] 반환\n", + "cate_vs_none = dr_cate.const_marginal_effect(X_test)\n", + "plugin_treatment_values = np.column_stack([\n", + " np.zeros(len(X_test)), # none: baseline, 상대효과 = 0\n", + " cate_vs_none,\n", + "])\n", + "pi_plugin = np.argmax(plugin_treatment_values, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "33437c2c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:47.608568Z", + "iopub.status.busy": "2026-05-19T10:15:47.608484Z", + "iopub.status.idle": "2026-05-19T10:15:47.611570Z", + "shell.execute_reply": "2026-05-19T10:15:47.611196Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.092\n", + "tech_support_only 0.499\n", + "discount_only 0.035\n", + "discount_plus_support 0.374\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(pi_plugin).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "33ktqy8zrqm", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:47.612786Z", + "iopub.status.busy": "2026-05-19T10:15:47.612713Z", + "iopub.status.idle": "2026-05-19T10:15:47.617141Z", + "shell.execute_reply": "2026-05-19T10:15:47.616847Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SizeEmployee CountIT Spendassigned_treatmentτ_techτ_discτ_both
09620414217655none-6140-1419-1715
1253901787971none-14556-4252-7277
26655527919899none-17629-2896-16555
3708682026263tech_support_only4265-414146
424152184610tech_support_only4174-2315-772
5548556314772tech_support_only2061-698587
639922836133052discount_plus_support7849-127811819
71817042459529discount_plus_support9171522110249
829437737112485discount_plus_support71248779048
\n", + "
" + ], + "text/plain": [ + " Size Employee Count IT Spend assigned_treatment τ_tech τ_disc \\\n", + "0 96204 142 17655 none -6140 -1419 \n", + "1 25390 178 7971 none -14556 -4252 \n", + "2 66555 279 19899 none -17629 -2896 \n", + "3 70868 20 26263 tech_support_only 4265 -41 \n", + "4 24152 18 4610 tech_support_only 4174 -2315 \n", + "5 54855 63 14772 tech_support_only 2061 -698 \n", + "6 399228 36 133052 discount_plus_support 7849 -1278 \n", + "7 181704 24 59529 discount_plus_support 9171 5221 \n", + "8 294377 37 112485 discount_plus_support 7124 877 \n", + "\n", + " τ_both \n", + "0 -1715 \n", + "1 -7277 \n", + "2 -16555 \n", + "3 4146 \n", + "4 -772 \n", + "5 587 \n", + "6 11819 \n", + "7 10249 \n", + "8 9048 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "none_idx = np.where(pi_plugin == 0)[0][:3]\n", + "tech_idx = np.where(pi_plugin == 1)[0][:3]\n", + "both_idx = np.where(pi_plugin == 3)[0][:3]\n", + "sample_ids = np.concatenate([none_idx, tech_idx, both_idx])\n", + "\n", + "df_plugin_sample = pd.DataFrame(X_test[sample_ids], columns=covariates)[['Size', 'Employee Count', 'IT Spend']]\n", + "df_plugin_sample['assigned_treatment'] = pd.Series(pi_plugin[sample_ids]).map(TREATMENT_NAMES).values\n", + "df_plugin_sample[['τ_tech', 'τ_disc', 'τ_both']] = np.round(cate_vs_none[sample_ids], 0).astype(int)\n", + "df_plugin_sample.index = pd.RangeIndex(len(df_plugin_sample))\n", + "df_plugin_sample" + ] + }, + { + "cell_type": "markdown", + "id": "05b0ee18", + "metadata": {}, + "source": [ + "plug-in 정책은 약 50% 고객에게 `tech_support_only`, 37% 고객에게 `discount_plus_support`, 그리고 약 9% 고객에게 `none`을 배정합니다.\n", + "\n", + "비용을 함께 고려하면서, 기대 순이익이 음수로 예상되는 일부 고객에게는 처치를 하지 않는 선택이 나타납니다." + ] + }, + { + "cell_type": "markdown", + "id": "97d958cd", + "metadata": {}, + "source": [ + "### Policy Tree\n", + "\n", + "현업에서는 성능뿐 아니라 정책의 설명 가능성도 중요한 경우가 많습니다. 얕은 decision tree 기반 정책은 다음과 같은 이유로 실무에서 자주 사용됩니다.\n", + "\n", + "- **이해관계자 설명**: 직관적인 분기 규칙 형태라 정책을 쉽게 설명하고 공유할 수 있습니다.\n", + "- **공정성 검토**: 어떤 고객이 어떤 처치를 받는지 구조가 명확해 편향 여부를 점검하기 쉽습니다.\n", + "- **운영 안정성**: 복잡한 모델보다 단순한 규칙 기반 정책이 실제 운영과 관리 측면에서 안정적입니다.\n", + "\n", + "이 경우 정책을 모든 가능한 함수에서 찾는 대신, 제한된 정책 클래스 $\\Pi$ 안에서 탐색합니다.\n", + "\n", + "$$\n", + "\\hat\\pi = \\arg\\max_{\\pi \\in \\Pi}\n", + "\\frac{1}{n}\\sum_{i=1}^{n} \\widehat\\Gamma_{i,\\pi(X_i)}\n", + "$$\n", + "\n", + "Policy Tree는 $\\Pi$를 얕은 decision tree로 제한합니다. 따라서 depth를 작게 두면 사람이 읽을 수 있는 형태로 정책을 해석할 수 있습니다. 여기서는 `econml`의 `DRPolicyTree`를 사용합니다.\n", + "\n", + "또한 leaf가 지나치게 작아지면 다중 처치 환경에서 value 추정이 불안정해질 수 있으므로, `min_samples_leaf`를 사용해 너무 작은 leaf를 방지합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6c987a8b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:47.618491Z", + "iopub.status.busy": "2026-05-19T10:15:47.618419Z", + "iopub.status.idle": "2026-05-19T10:15:49.221505Z", + "shell.execute_reply": "2026-05-19T10:15:49.221159Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.199\n", + "tech_support_only 0.475\n", + "discount_only 0.000\n", + "discount_plus_support 0.326\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_tree = DRPolicyTree(\n", + " max_depth=2,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + ")\n", + "dr_policy_tree.fit(Y_net_train, A_train, X=X_train)\n", + "pi_tree = dr_policy_tree.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_tree).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "64529830", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:49.222736Z", + "iopub.status.busy": "2026-05-19T10:15:49.222659Z", + "iopub.status.idle": "2026-05-19T10:15:49.316133Z", + "shell.execute_reply": "2026-05-19T10:15:49.315692Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(13, 7))\n", + "dr_policy_tree.plot(feature_names=covariates, treatment_names=TREATMENT_LABELS, ax=ax)\n", + "ax.set_title('4-way DRPolicyTree')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2a3ffb5d", + "metadata": {}, + "source": [ + "이 트리 정책은 주로 `Size`를 기준으로 고객을 나눕니다. 특징적으로 약 20%의 고객에게는 `none`을 배정합니다. 비용을 반영한 결과, 일부 고객은 처치했을 때 기대 순이익이 낮아 처치하지 않는 편이 더 낫다고 판단한 것입니다.\n", + "\n", + "전체적으로는 규모가 큰 고객에게 `discount_plus_support`를, 그 외 고객에게는 주로 `tech_support_only`를 배정합니다. 기대 순이익이 낮은 고객에게는 `none`을 선택합니다." + ] + }, + { + "cell_type": "markdown", + "id": "35dd95b1", + "metadata": {}, + "source": [ + "### Policy Forest\n", + "\n", + "Policy Forest는 여러 개의 tree를 활용해 보다 안정적으로 정책을 학습합니다.\n", + "\n", + "Policy Forest 역시 AIPW score를 직접 최대화하지만, 단일 tree 대신 여러 tree의 결과를 평균적으로 활용하기 때문에 분산이 더 작고 복잡한 이질성을 더 안정적으로 학습할 수 있습니다.\n", + "\n", + "여기서는 `econml`의 `DRPolicyForest`를 사용합니다.\n", + "\n", + "다만 tree 기반 정책처럼 하나의 명확한 의사결정 규칙 형태로 해석하기는 어렵습니다. 학습 이후에는 동일한 test set의 AIPW score를 사용해 정책 가치를 비교합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d7939447", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:49.317330Z", + "iopub.status.busy": "2026-05-19T10:15:49.317253Z", + "iopub.status.idle": "2026-05-19T10:15:51.122853Z", + "shell.execute_reply": "2026-05-19T10:15:51.122383Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "none 0.114\n", + "tech_support_only 0.469\n", + "discount_only 0.000\n", + "discount_plus_support 0.417\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_policy_forest = DRPolicyForest(\n", + " n_estimators=400,\n", + " max_depth=5,\n", + " min_samples_leaf=30,\n", + " model_regression=RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " model_propensity=RandomForestClassifier(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " ),\n", + " categories=[0, 1, 2, 3],\n", + " min_propensity=0.02,\n", + " cv=3,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + ")\n", + "dr_policy_forest.fit(Y_net_train, A_train, X=X_train)\n", + "pi_forest = dr_policy_forest.predict(X_test).astype(int).ravel()\n", + "\n", + "pd.Series(pi_forest).map(TREATMENT_NAMES).value_counts(normalize=True).reindex(TREATMENT_LABELS).fillna(0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "15d7d727", + "metadata": {}, + "source": [ + "DRPolicyForest는 전반적으로 plug-in 정책과 유사한 배정을 보입니다. `none` 11%, `tech_support_only` 47%, `discount_plus_support` 42%로, plug-in 정책보다 `discount_plus_support` 비중이 조금 더 높게 나타납니다." + ] + }, + { + "cell_type": "markdown", + "id": "sdqhrcm2hee", + "metadata": {}, + "source": [ + "### Feature Importance\n", + "\n", + "DRPolicyForest는 단일 트리 구조가 없어 정책을 직접 분기 규칙 형태로 해석하기 어렵습니다. 그러나 `feature_importances_`를 통해 어떤 고객 특성이 처치 배정 결정에 더 큰 영향을 미치는지 파악할 수 있습니다.\n", + "\n", + "`feature_importances_`는 각 특성이 forest의 분기 과정에서 정책 이질성(policy heterogeneity)을 얼마나 유발하는지를 정규화한 점수입니다. 값이 클수록 고객별 최적 처치를 결정하는 데 해당 특성이 더 중요한 역할을 합니다.\n", + "\n", + "단, 이 값은 완전한 인과적 해석보다는 \"어떤 특성이 처치 분기 기준으로 자주 사용되었는가\"를 나타내는 탐색적 지표로 활용하는 것이 적절합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "m87bny2yj3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:51.124121Z", + "iopub.status.busy": "2026-05-19T10:15:51.124038Z", + "iopub.status.idle": "2026-05-19T10:15:51.180004Z", + "shell.execute_reply": "2026-05-19T10:15:51.179528Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featureimportance
0Employee Count0.417657
1Size0.378824
2PC Count0.127452
3IT Spend0.075229
4SMC Flag0.000479
5Global Flag0.000194
6Commercial Flag0.000147
7Major Flag0.000017
\n", + "
" + ], + "text/plain": [ + " feature importance\n", + "0 Employee Count 0.417657\n", + "1 Size 0.378824\n", + "2 PC Count 0.127452\n", + "3 IT Spend 0.075229\n", + "4 SMC Flag 0.000479\n", + "5 Global Flag 0.000194\n", + "6 Commercial Flag 0.000147\n", + "7 Major Flag 0.000017" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "importances = dr_policy_forest.feature_importances_\n", + "fi_df = pd.DataFrame({\n", + " 'feature': covariates,\n", + " 'importance': importances,\n", + "}).sort_values('importance', ascending=False).reset_index(drop=True)\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4))\n", + "sns.barplot(data=fi_df, x='importance', y='feature', ax=ax, color='steelblue')\n", + "ax.set_title('DRPolicyForest — Feature Importances')\n", + "ax.set_xlabel('Importance (normalized)')\n", + "ax.set_ylabel('')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "display(fi_df)" + ] + }, + { + "cell_type": "markdown", + "id": "kcb2wiyixla", + "metadata": {}, + "source": [ + "`Employee Count`(42%)와 `Size`(38%)가 전체 중요도의 약 80%를 차지하며, 고객 규모와 직원 수가 처치 배정을 주도하는 핵심 요인임을 확인할 수 있습니다. `PC Count`(13%)와 `IT Spend`(8%)도 일부 기여하지만, 나머지 Flag 변수들은 거의 영향을 미치지 않습니다.\n", + "\n", + "이는 앞서 DRPolicyTree가 `Size`를 기준으로 주로 분기했던 결과와 일치합니다. 즉, 고객 규모가 처치 종류(기술지원, 할인, 또는 없음)를 결정하는 가장 강한 신호임을 두 모델 모두에서 확인할 수 있습니다." + ] + }, + { + "cell_type": "markdown", + "id": "99bac91c", + "metadata": {}, + "source": [ + "## 정책 평가\n", + "\n", + "이제 학습된 정책들을 동일한 test set의 AIPW score 기준으로 비교합니다.\n", + "\n", + "비교를 위해 모든 고객에게 동일한 처치를 적용하는 baseline 정책도 함께 사용합니다.\n", + "\n", + "- `all_none`\n", + "- `all_tech_support_only`\n", + "- `all_discount_only`\n", + "- `all_discount_plus_support`\n", + "\n", + "예를 들어 `all_discount_plus_support`는 모든 고객에게 기술지원과 할인을 함께 제공했을 때의 평균 순이익을 의미합니다.\n", + "\n", + "반면 학습된 정책은 고객 특성 $X_i$에 따라 서로 다른 처치를 배정합니다.\n", + "\n", + "만약 학습된 정책의 value가 가장 좋은 baseline 정책보다 높다면, 모든 고객에게 동일한 처치를 적용하는 것보다 고객별 targeting이 더 효과적이라는 의미입니다." + ] + }, + { + "cell_type": "markdown", + "id": "1e2f1760", + "metadata": {}, + "source": [ + "AIPW(Augmented Inverse Probability Weighting) score는 다음과 같이 정의합니다.\n", + "\n", + "$$\n", + "\\hat\\Gamma_{i,a} = \\hat\\mu_a(X_i) + \\frac{\\mathbf{1}[A_i=a]}{\\hat e_a(X_i)}\\bigl(Y_i^{net} - \\hat\\mu_a(X_i)\\bigr)\n", + "$$\n", + "\n", + "- $\\hat\\mu_a(X_i)$: outcome model이 예측한 $E[Y^{net}(a)\\mid X_i]$\n", + "- $\\hat e_a(X_i)$: propensity model이 예측한 $P(A_i=a\\mid X_i)$\n", + "- 두 번째 항은 실제 관측값과 예측값의 차이를 IPW로 보정하는 역할을 합니다.\n", + "\n", + "이 구조 덕분에 outcome model과 propensity model 중 하나만 올바르게 추정되어도 불편성이 유지됩니다. 이를 doubly robust(이중 견고성)라고 부릅니다.\n", + "\n", + "- outcome model이 정확하면, IPW 보정항의 기댓값이 0에 가까워져 $\\hat\\mu_a(X_i)$가 직접 기여합니다.\n", + "- propensity model이 정확하면, IPW 보정항이 outcome model의 편향을 제거합니다.\n", + "\n", + "이 score를 사용해 학습된 정책의 가치를 추정합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9c3bfe27", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:51.181255Z", + "iopub.status.busy": "2026-05-19T10:15:51.181178Z", + "iopub.status.idle": "2026-05-19T10:15:51.813166Z", + "shell.execute_reply": "2026-05-19T10:15:51.812685Z" + } + }, + "outputs": [], + "source": [ + "Y_net_train = Y_net[train_idx]\n", + "Y_net_test = Y_net[test_idx]\n", + "\n", + "e_hat_net = np.clip(e_hat_raw, 0.02, 0.98)\n", + "e_hat_net = e_hat_net / e_hat_net.sum(axis=1, keepdims=True)\n", + "\n", + "gamma_net = np.zeros((len(X_test), 4))\n", + "mu_hat_net = np.zeros((len(X_test), 4))\n", + "\n", + "for treatment_id in range(4):\n", + " outcome_model = RandomForestRegressor(\n", + " n_estimators=400,\n", + " min_samples_leaf=20,\n", + " random_state=SEED,\n", + " n_jobs=1,\n", + " )\n", + " outcome_model.fit(X_train[A_train == treatment_id], Y_net_train[A_train == treatment_id])\n", + " mu_a = outcome_model.predict(X_test)\n", + " observed_a = (A_test == treatment_id).astype(float)\n", + "\n", + " gamma_net[:, treatment_id] = mu_a + observed_a / e_hat_net[:, treatment_id] * (Y_net_test - mu_a)\n", + " mu_hat_net[:, treatment_id] = mu_a" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0d014d17", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:51.814570Z", + "iopub.status.busy": "2026-05-19T10:15:51.814496Z", + "iopub.status.idle": "2026-05-19T10:15:51.820677Z", + "shell.execute_reply": "2026-05-19T10:15:51.820308Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
samplee_hatmu_hatgamma
sample_idactual_treatmentobserved_net_outcomenonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_supportnonetech_support_onlydiscount_onlydiscount_plus_support
00discount_plus_support9254.5585410.2486330.2673380.2723660.2116625370.0992819559.7777634547.4845526984.7316095370.0992819559.7777634547.48455217708.540116
11tech_support_only7702.8616620.2110870.3072040.2433940.23831510468.9425226728.9884198373.31663311121.82660910468.9425229899.1085088373.31663311121.826609
22tech_support_only5062.1844200.3765700.2419010.2562610.1252682879.7417865449.409859937.5151032959.5143472879.7417863848.652052937.5151032959.514347
33none8930.4950220.3396530.1947010.2465050.2191428458.7792178254.3720696054.5989736560.0280699847.5980528254.3720696054.5989736560.028069
44none8156.5755020.3122650.2500210.3121230.12559010137.4698213378.7480355743.8029392988.2173143793.8450543378.7480355743.8029392988.217314
\n", + "
" + ], + "text/plain": [ + " sample e_hat \\\n", + " sample_id actual_treatment observed_net_outcome none \n", + "0 0 discount_plus_support 9254.558541 0.248633 \n", + "1 1 tech_support_only 7702.861662 0.211087 \n", + "2 2 tech_support_only 5062.184420 0.376570 \n", + "3 3 none 8930.495022 0.339653 \n", + "4 4 none 8156.575502 0.312265 \n", + "\n", + " mu_hat \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 0.267338 0.272366 0.211662 5370.099281 \n", + "1 0.307204 0.243394 0.238315 10468.942522 \n", + "2 0.241901 0.256261 0.125268 2879.741786 \n", + "3 0.194701 0.246505 0.219142 8458.779217 \n", + "4 0.250021 0.312123 0.125590 10137.469821 \n", + "\n", + " gamma \\\n", + " tech_support_only discount_only discount_plus_support none \n", + "0 9559.777763 4547.484552 6984.731609 5370.099281 \n", + "1 6728.988419 8373.316633 11121.826609 10468.942522 \n", + "2 5449.409859 937.515103 2959.514347 2879.741786 \n", + "3 8254.372069 6054.598973 6560.028069 9847.598052 \n", + "4 3378.748035 5743.802939 2988.217314 3793.845054 \n", + "\n", + " \n", + " tech_support_only discount_only discount_plus_support \n", + "0 9559.777763 4547.484552 17708.540116 \n", + "1 9899.108508 8373.316633 11121.826609 \n", + "2 3848.652052 937.515103 2959.514347 \n", + "3 8254.372069 6054.598973 6560.028069 \n", + "4 3378.748035 5743.802939 2988.217314 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ids = np.arange(5)\n", + "\n", + "sample_prediction_table = pd.concat(\n", + " {\n", + " 'sample': pd.DataFrame({\n", + " 'sample_id': sample_ids,\n", + " 'actual_treatment': pd.Series(A_test[sample_ids]).map(TREATMENT_NAMES).to_numpy(),\n", + " 'observed_net_outcome': Y_net_test[sample_ids],\n", + " }),\n", + " 'e_hat': pd.DataFrame(e_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'mu_hat': pd.DataFrame(mu_hat_net[sample_ids], columns=TREATMENT_LABELS),\n", + " 'gamma': pd.DataFrame(gamma_net[sample_ids], columns=TREATMENT_LABELS),\n", + " },\n", + " axis=1,\n", + ")\n", + "\n", + "sample_prediction_table" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "14b616ad", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:51.821658Z", + "iopub.status.busy": "2026-05-19T10:15:51.821594Z", + "iopub.status.idle": "2026-05-19T10:15:51.827731Z", + "shell.execute_reply": "2026-05-19T10:15:51.827380Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
policyvalueci_lowerci_upperrate_nonerate_tech_support_onlyrate_discount_onlyrate_discount_plus_support
4plugin_drlearner_4treatment11797.81314711270.04636912325.5799260.0920.4990.0350.374
6dr_policy_forest_4treatment11619.02719511094.85834412143.1960460.1140.4690.0000.417
5dr_policy_tree_4treatment11148.54966510700.70293811596.3963920.1990.4750.0000.326
1all_tech_support_only10526.5947669833.11012211220.0794100.0001.0000.0000.000
3all_discount_plus_support10136.7252889439.46167410833.9889030.0000.0000.0001.000
2all_discount_only7545.0926616993.5474078096.6379150.0000.0001.0000.000
0all_none7249.3454676955.4474327543.2435021.0000.0000.0000.000
\n", + "
" + ], + "text/plain": [ + " policy value ci_lower ci_upper \\\n", + "4 plugin_drlearner_4treatment 11797.813147 11270.046369 12325.579926 \n", + "6 dr_policy_forest_4treatment 11619.027195 11094.858344 12143.196046 \n", + "5 dr_policy_tree_4treatment 11148.549665 10700.702938 11596.396392 \n", + "1 all_tech_support_only 10526.594766 9833.110122 11220.079410 \n", + "3 all_discount_plus_support 10136.725288 9439.461674 10833.988903 \n", + "2 all_discount_only 7545.092661 6993.547407 8096.637915 \n", + "0 all_none 7249.345467 6955.447432 7543.243502 \n", + "\n", + " rate_none rate_tech_support_only rate_discount_only \\\n", + "4 0.092 0.499 0.035 \n", + "6 0.114 0.469 0.000 \n", + "5 0.199 0.475 0.000 \n", + "1 0.000 1.000 0.000 \n", + "3 0.000 0.000 0.000 \n", + "2 0.000 0.000 1.000 \n", + "0 1.000 0.000 0.000 \n", + "\n", + " rate_discount_plus_support \n", + "4 0.374 \n", + "6 0.417 \n", + "5 0.326 \n", + "1 0.000 \n", + "3 1.000 \n", + "2 0.000 \n", + "0 0.000 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "policy_assignments = {\n", + " **{f'all_{treatment_name}': np.full(len(X_test), treatment_id) for treatment_id, treatment_name in TREATMENT_NAMES.items()},\n", + " 'plugin_drlearner_4treatment': pi_plugin,\n", + " 'dr_policy_tree_4treatment': pi_tree,\n", + " 'dr_policy_forest_4treatment': pi_forest,\n", + "}\n", + "\n", + "eval_rows = []\n", + "for policy_name, policy_assignment in policy_assignments.items():\n", + " pi = np.asarray(policy_assignment).astype(int).ravel()\n", + " scores = gamma_net[np.arange(len(pi)), pi]\n", + "\n", + " row = {\n", + " 'policy': policy_name,\n", + " 'value': scores.mean(),\n", + " 'value_se': scores.std(ddof=1) / np.sqrt(len(scores)),\n", + " }\n", + " for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " row[f'rate_{treatment_name}'] = np.mean(pi == treatment_id)\n", + " eval_rows.append(row)\n", + "\n", + "policy_eval = pd.DataFrame(eval_rows)\n", + "policy_eval['ci_lower'] = policy_eval['value'] - 1.96 * policy_eval['value_se']\n", + "policy_eval['ci_upper'] = policy_eval['value'] + 1.96 * policy_eval['value_se']\n", + "\n", + "display_cols = ['policy', 'value', 'ci_lower', 'ci_upper'] + [f'rate_{n}' for n in TREATMENT_LABELS]\n", + "policy_eval.sort_values('value', ascending=False)[display_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4814d1ed", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:51.828713Z", + "iopub.status.busy": "2026-05-19T10:15:51.828645Z", + "iopub.status.idle": "2026-05-19T10:15:51.941518Z", + "shell.execute_reply": "2026-05-19T10:15:51.941192Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", + "plot_df = policy_eval.sort_values('value', ascending=False)\n", + "sns.barplot(data=plot_df, x='value', y='policy', ax=axes[0], color='seagreen')\n", + "axes[0].set_title('4-way net policy value')\n", + "axes[0].set_xlabel('AIPW-estimated net value')\n", + "axes[0].set_ylabel('')\n", + "\n", + "treatment_rate_cols = [f'rate_{name}' for name in TREATMENT_LABELS]\n", + "rate_df = policy_eval.set_index('policy')[treatment_rate_cols]\n", + "rate_df.columns = TREATMENT_LABELS\n", + "rate_df.loc[['plugin_drlearner_4treatment', 'dr_policy_tree_4treatment', 'dr_policy_forest_4treatment']].plot(kind='barh', stacked=True, ax=axes[1], colormap='tab20')\n", + "axes[1].set_title('Assigned treatment share')\n", + "axes[1].set_xlabel('Share')\n", + "axes[1].set_ylabel('')\n", + "axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "dec4e9f6", + "metadata": {}, + "source": [ + "세 학습 정책 모두 단일 처치 정책보다 높은 가치를 가집니다. 고객별로 처치를 다르게 배정하는 것이 모두에게 같은 처치를 적용하는 것보다 효과적이라는 의미입니다.\n", + "\n", + "학습 정책 중에서는 DRLearner plug-in(11,810)이 가장 높았고, DRPolicyForest(11,626)와 DRPolicyTree(11,148)가 뒤를 이었습니다. 다만 plug-in과 forest는 신뢰구간이 상당 부분 겹쳐 실질적인 차이가 있다고 보기는 어렵습니다. 이론적으로는 AIPW score를 직접 최적화하는 DR 정책이 더 유리할 수 있지만, 실제로는 표본 크기나 데이터 특성에 따라 결과가 달라집니다. tree 정책은 해석 가능한 구조를 유지하는 대신 성능을 일부 내준 모습입니다.\n", + "\n", + "단일 처치 정책에서는 `all_tech_support_only`(10,527)가 가장 좋았습니다. `all_discount_plus_support`(10,137)는 할인 비용이 고객 규모에 따라 커지도록 설정했기 때문에, 일부 고객에서는 할인의 순수익이 비용을 충분히 상쇄하지 못한 것으로 보입니다." + ] + }, + { + "cell_type": "markdown", + "id": "9bh57tga2aq", + "metadata": {}, + "source": [ + "## 예산 제약 하의 정책 비교\n", + "\n", + "지금까지는 어떤 정책이 전체적으로 더 높은 AIPW value를 갖는지 비교했습니다. 그러나 실제 환경에서는 예산이 제한되어 있을 수 있습니다. 같은 예산 안에서 어떤 정책이 더 효율적인지 비교하려면 비용 곡선(cost curve)이 유용합니다." + ] + }, + { + "cell_type": "markdown", + "id": "qdxockulyq9", + "metadata": {}, + "source": [ + "### 고객별 기대 비용 추정\n", + "\n", + "이를 위해 먼저 고객별 $E[C(a) \\mid X_i]$를 추정합니다. 실제 비용은 해당 고객이 받은 처치에서만 관측되므로, 처치별로 훈련 데이터에서 비용 회귀 모델을 학습해 test set에 적용합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a1z6z7eabyj", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:51.942917Z", + "iopub.status.busy": "2026-05-19T10:15:51.942837Z", + "iopub.status.idle": "2026-05-19T10:15:52.313199Z", + "shell.execute_reply": "2026-05-19T10:15:52.312759Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated E[C(a)|X] — test set (mean ± std):\n", + " tech_support_only : 4,892 ± 3,466\n", + " discount_only : 5,454 ± 2,330\n", + " discount_plus_support : 10,687 ± 4,139\n" + ] + } + ], + "source": [ + "# 처치별 E[C(a) | X] 추정\n", + "# c_hat_test[i, a] = 고객 i가 처치 a를 받을 때의 예측 비용\n", + "c_true_by_treatment = {1: c_tech, 2: c_disc, 3: c_tech + c_disc}\n", + "\n", + "c_hat_test = np.zeros((len(X_test), 4)) # 처치 0 → 비용 = 0\n", + "for treatment_id in [1, 2, 3]:\n", + " mask_train = (A_train == treatment_id)\n", + " mdl = RandomForestRegressor(n_estimators=300, min_samples_leaf=20,\n", + " random_state=SEED, n_jobs=1)\n", + " mdl.fit(X_train[mask_train], c_true_by_treatment[treatment_id][train_idx[mask_train]])\n", + " c_hat_test[:, treatment_id] = np.maximum(mdl.predict(X_test), 200.0)\n", + "\n", + "print(\"Estimated E[C(a)|X] — test set (mean ± std):\")\n", + "for treatment_id, treatment_name in TREATMENT_NAMES.items():\n", + " if treatment_id > 0:\n", + " print(f\" {treatment_name:25s}: {c_hat_test[:, treatment_id].mean():8,.0f} ± {c_hat_test[:, treatment_id].std():6,.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dgt4sg335xc", + "metadata": {}, + "source": [ + "### 비용 곡선과 예산 제약 하 비교\n", + "\n", + "비용 곡선의 두 축은 다음과 같습니다.\n", + "\n", + "- **x축**: 고객 1인당 누적 평균 처치 비용\n", + "- **y축**: 고객 1인당 누적 평균 순수익 — baseline(`none`) 대비 증분이며, **비용이 이미 차감된 값**입니다.\n", + "\n", + "각 정책에서 순수익/비용 비율 $\\hat\\rho(x) = \\hat\\tau(x) / \\hat\\gamma(x)$이 높은 고객부터 순서대로 처치합니다. x = B에서 세로선을 그으면 예산 B 하의 정책 비교가 됩니다. 같은 예산에서 y값이 높을수록 더 효율적인 정책입니다.\n", + "\n", + "그래프의 종점(●)은 각 정책의 처치 대상 전원을 처치했을 때의 위치입니다. `none`으로 배정된 고객은 처치 비용이 없으므로 곡선에 포함되지 않습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ttwi6dbqcfo", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-19T10:15:52.314360Z", + "iopub.status.busy": "2026-05-19T10:15:52.314287Z", + "iopub.status.idle": "2026-05-19T10:15:52.384609Z", + "shell.execute_reply": "2026-05-19T10:15:52.384207Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def policy_cost_curve(pi_eval, gamma_matrix, c_hat_matrix):\n", + " \"\"\"\n", + " 처치별 고객 비용 추정값을 사용해 비용 곡선을 생성합니다.\n", + " c_hat_matrix[i, a] = Ê[C(a) | X_i]\n", + "\n", + " x = 고객 1인당 누적 평균 처치 비용 (gross)\n", + " y = 고객 1인당 누적 평균 순이익 (Y_net에 비용이 이미 차감된 값)\n", + "\n", + " 처치 고객은 순이익 / 추정 비용 비율(내림차순)로 정렬합니다.\n", + " \"\"\"\n", + " pi = np.asarray(pi_eval).ravel().astype(int)\n", + " n = len(pi)\n", + " treated = pi > 0\n", + " if treated.sum() == 0:\n", + " return np.array([0.0]), np.array([0.0])\n", + "\n", + " benefit = gamma_matrix[np.arange(n), pi] - gamma_matrix[:, 0]\n", + " cost = c_hat_matrix[np.arange(n), pi].astype(float)\n", + "\n", + " b_t, c_t = benefit[treated], cost[treated]\n", + " ratio = np.where(c_t > 0, b_t / c_t, b_t)\n", + " order = np.argsort(-ratio)\n", + "\n", + " cum_cost = np.r_[0, np.cumsum(c_t[order])]\n", + " cum_benefit = np.r_[0, np.cumsum(b_t[order])]\n", + " return cum_cost / n, cum_benefit / n\n", + "\n", + "\n", + "n_test = len(X_test)\n", + "policies_curve = [\n", + " ('DRLearner plug-in', pi_plugin),\n", + " ('DRPolicyTree (depth=2)', pi_tree),\n", + " ('DRPolicyForest', pi_forest),\n", + " ('all_discount_plus_support', np.full(n_test, 3)),\n", + " ('all_tech_support_only', np.full(n_test, 1)),\n", + "]\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']\n", + "\n", + "for (name, pi_eval), color in zip(policies_curve, colors):\n", + " cc, cg = policy_cost_curve(pi_eval, gamma_net, c_hat_test)\n", + " ax.plot(cc, cg, lw=2, color=color,\n", + " label=f'{name} (cost={cc[-1]:,.0f}, net benefit={cg[-1]:,.0f})')\n", + " ax.scatter([cc[-1]], [cg[-1]], s=50, color=color, zorder=5)\n", + "\n", + "budget_example = 5_000\n", + "ax.axvline(budget_example, color='gray', lw=1.5, ls='--', alpha=0.7,\n", + " label=f'Budget = ${budget_example:,}/customer')\n", + "\n", + "ax.set_xlabel('Avg cumulative cost per customer ($)', fontsize=11)\n", + "ax.set_ylabel('Avg cumulative net benefit per customer ($)', fontsize=11)\n", + "ax.set_title('Policy cost curves — cut at x=B for budget-constrained comparison', fontsize=11)\n", + "ax.legend(fontsize=8, loc='upper left')\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "qoq9ch9m2fg", + "metadata": {}, + "source": [ + "예산 규모에 따라 효과적인 정책이 다릅니다. 예산이 \\$1,000~\\$3,000 수준으로 적을 때는 `all_tech_support_only`가 가장 효율적입니다. 기술지원 평균 비용이 낮아 소규모 예산으로도 순수익/비용 비율이 높은 고객부터 빠르게 처치할 수 있기 때문입니다.\n", + "\n", + "예산이 \\$4,000~\\$6,000 수준으로 늘어나면 학습 정책(plug-in, forest)이 앞서기 시작합니다. 고객별 최적 처치를 배정하는 타겟팅 효과가 발휘되며, 같은 비용으로 더 높은 순이익을 냅니다.\n", + "\n", + "예산을 전부 소진하는 상황에서도 학습 정책이 가장 효율적입니다. plug-in은 약 \\$6,600을 써서 \\$4,561, forest는 약 \\$6,900을 써서 \\$4,376의 순이익을 냅니다. 반면 `all_discount_plus_support`는 \\$10,700을 전부 소진해도 순이익이 \\$2,887에 그쳐 예산 대비 효율이 가장 낮습니다." + ] + }, + { + "cell_type": "markdown", + "id": "b40f71c0", + "metadata": {}, + "source": [ + "## 참고 자료\n", + "\n", + "- [Stanford ML+CI Tutorial — Policy Learning I (Binary Treatment)](https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/policy-learning-i---binary-treatment.html)\n", + "- [Athey and Wager (2021, Econometrica) — Policy Learning with Observational Data](https://arxiv.org/abs/1702.02896)\n", + "- [Sun, Du, Wager et al. (2021) — Treatment Allocation under Uncertain Costs](https://arxiv.org/abs/2103.11066)\n", + "- [Imai and Li (2019) — Experimental Evaluation of Individualized Treatment Rules](https://arxiv.org/pdf/1905.05389.pdf)\n", + "- [arXiv 2604.06123](https://arxiv.org/html/2604.06123v1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements-book.txt b/requirements-book.txt index 898be45..0548471 100644 --- a/requirements-book.txt +++ b/requirements-book.txt @@ -5,6 +5,9 @@ pandas scipy matplotlib seaborn -scikit-learn +scikit-learn<1.7 statsmodels graphviz +econml +scikit-uplift +kagglehub diff --git a/requirements.txt b/requirements.txt index d010b0b..c75646f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,6 @@ # Jupyter Book (2.x - MyST-MD 기반) jupyter-book>=2.0.0 +jupyterlab # Video Production manim>=0.18.0 @@ -9,6 +10,18 @@ numpy pandas matplotlib seaborn -scikit-learn +scikit-learn<1.7 +scikit-uplift statsmodels graphviz +econml +kagglehub +ipywidgets + +# PDF Processing +pypdf +pdfplumber +reportlab +pdf2image +pytesseract +Pillow