diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..d2fbffc --- /dev/null +++ b/.dockerignore @@ -0,0 +1,10 @@ +# Allow-list: only what the image needs is sent to the build context. +* +!pyproject.toml +!uv.lock +!README.md +!src/ +!app/ +!.streamlit/ +**/__pycache__ +**/*.py[cod] diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..0b67728 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,15 @@ +version: 2 +updates: + - package-ecosystem: uv + directory: / + schedule: { interval: weekly } + groups: + python-deps: { patterns: ["*"] } + - package-ecosystem: docker + directory: / + schedule: { interval: weekly } + - package-ecosystem: github-actions + directory: / + schedule: { interval: weekly } + groups: + actions: { patterns: ["*"] } diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..e35dee5 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,162 @@ +name: CI + +on: + push: + branches: [main] + tags: ["v*.*.*"] + pull_request: + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + +env: + UV_VERSION: "0.12.15" + IMAGE_NAME: churn-explorer + +jobs: + lint: + name: Lint & lockfile + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v7 + - uses: astral-sh/setup-uv@v10.1.0 + with: + version: ${{ env.UV_VERSION }} + enable-cache: true + - name: Lockfile is up to date + run: uv lock --check + - name: Install dev dependencies + run: uv sync --locked + - name: Ruff lint + run: uv run ruff check --output-format=github . + - name: Ruff format + run: uv run ruff format --check . + + test: + name: Tests (Python ${{ matrix.python }}) + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python: ["3.12", "3.13"] + steps: + - uses: actions/checkout@v7 + - uses: astral-sh/setup-uv@v10.1.0 + with: + version: ${{ env.UV_VERSION }} + python-version: ${{ matrix.python }} + enable-cache: true + - name: Install + run: uv sync --locked + - name: Run tests with coverage + run: uv run pytest --cov-report=xml --junitxml=junit.xml + - name: Upload test reports + if: always() + uses: actions/upload-artifact@v7 + with: + name: test-reports-py${{ matrix.python }} + path: | + coverage.xml + junit.xml + + docker: + name: Docker build, smoke test & publish + needs: [lint, test] + runs-on: ubuntu-latest + permissions: + contents: read + packages: write + steps: + - uses: actions/checkout@v7 + + - name: Lower-case image reference + id: ref + run: echo "image=ghcr.io/${GITHUB_REPOSITORY_OWNER,,}/${IMAGE_NAME}" >> "$GITHUB_OUTPUT" + + - uses: docker/setup-qemu-action@v4 + - uses: docker/setup-buildx-action@v4 + + - name: Image metadata + id: meta + uses: docker/metadata-action@v6 + with: + images: ${{ steps.ref.outputs.image }} + tags: | + type=ref,event=pr + type=semver,pattern={{version}} + type=semver,pattern={{major}}.{{minor}} + type=sha,format=short + type=raw,value=latest,enable={{is_default_branch}} + + - name: Build image for smoke test + uses: docker/build-push-action@v7 + with: + context: . + load: true + tags: churn-explorer:ci + cache-from: type=gha + cache-to: type=gha,mode=max + + - name: Smoke test container + run: | + # the installed package works inside the image + docker run --rm --entrypoint python churn-explorer:ci -m churn_app.sample_data --users 50 --out /tmp/s.parquet + docker run -d --name smoke -p 8501:8501 churn-explorer:ci + for i in $(seq 1 30); do + status=$(docker inspect -f '{{.State.Health.Status}}' smoke) + [ "$status" = "healthy" ] && break + sleep 2 + done + docker logs smoke + test "$status" = "healthy" + curl -fsS http://localhost:8501/_stcore/health + curl -fsS http://localhost:8501/ | grep -q "Streamlit" + test "$(docker exec smoke id -u)" = "10001" + docker rm -f smoke + + - name: Log in to GHCR + if: github.event_name != 'pull_request' + uses: docker/login-action@v4 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + - name: Log in to Docker Hub (optional) + id: dockerhub + if: github.event_name != 'pull_request' && vars.DOCKERHUB_USERNAME != '' + uses: docker/login-action@v4 + with: + username: ${{ vars.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + + - name: Add Docker Hub tags + id: tags + run: | + { + echo 'list<> "$GITHUB_OUTPUT" + + - name: Build and push multi-arch image + if: github.event_name != 'pull_request' + uses: docker/build-push-action@v7 + with: + context: . + platforms: linux/amd64,linux/arm64 + push: true + tags: ${{ steps.tags.outputs.list }} + labels: ${{ steps.meta.outputs.labels }} + cache-from: type=gha + cache-to: type=gha,mode=max + provenance: mode=max + sbom: true diff --git a/.gitignore b/.gitignore index c8af4de..9304c91 100644 --- a/.gitignore +++ b/.gitignore @@ -39,4 +39,12 @@ venv/ build/ dist/ -codex_report.md \ No newline at end of file +codex_report.md + +# Tooling caches / reports +.pytest_cache/ +.ruff_cache/ +.coverage +coverage.xml +junit.xml +.claude/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..f66607a --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +# uv tool install pre-commit && pre-commit install +# Hooks only cover the app/package; the research code is kept byte-for-byte as committed. +files: ^(src|app|tests|\.github|\.streamlit)/|^(pyproject\.toml|uv\.lock|Dockerfile|docker-compose\.yml|Makefile|\.pre-commit-config\.yaml)$ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: check-yaml + - id: check-toml + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-added-large-files + args: ["--maxkb=1024"] + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.16.7 + hooks: + - id: ruff-check + args: [--fix] + - id: ruff-format + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.12.15 + hooks: + - id: uv-lock diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/.streamlit/config.toml b/.streamlit/config.toml new file mode 100644 index 0000000..ec79946 --- /dev/null +++ b/.streamlit/config.toml @@ -0,0 +1,12 @@ +[server] +headless = true +# The full Kaggle train.parquet is ~1 GB; mount it via DATA_DIR instead when possible. +maxUploadSize = 2048 +fileWatcherType = "none" + +[browser] +gatherUsageStats = false + +[theme] +base = "light" +primaryColor = "#d6453d" diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..35f0d25 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,69 @@ +# syntax=docker/dockerfile:1.7 +# +# Churn Explorer — Streamlit app image. +# +# docker build -t churn-explorer . +# docker run --rm -p 8501:8501 churn-explorer +# docker run --rm -p 8501:8501 -v "$PWD/churn-prediction-25-26:/data:ro" churn-explorer +# +# Base images are pinned by digest (kept fresh by Dependabot) and Python +# dependencies are installed from uv.lock with --frozen, so rebuilding the same +# commit produces the same environment. + +ARG PYTHON_IMAGE=python:3.12-slim-bookworm@sha256:782412e85d0f0984994c290652577d4018aff08145c85b262bb63dc0c7522254 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.12.15@sha256:62f8c047d0a0e9ece6b53fc63df902585a67a47a7f318ddec4a37db586edc8e3 + +FROM ${UV_IMAGE} AS uv + +# --------------------------------------------------------------------------- # +FROM ${PYTHON_IMAGE} AS builder + +COPY --from=uv /uv /usr/local/bin/uv +ENV UV_COMPILE_BYTECODE=1 \ + UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=never \ + UV_PROJECT_ENVIRONMENT=/opt/venv + +WORKDIR /src +# Dependencies first: this layer is cached until pyproject.toml / uv.lock change. +COPY pyproject.toml uv.lock ./ +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-dev --no-install-project + +COPY README.md ./ +COPY src ./src +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-dev --no-editable + +# --------------------------------------------------------------------------- # +FROM ${PYTHON_IMAGE} AS runtime + +LABEL org.opencontainers.image.title="churn-explorer" \ + org.opencontainers.image.description="Streamlit explorer and churn model for streaming-service event logs" \ + org.opencontainers.image.source="https://github.com/Martinoor/datascience" + +RUN groupadd --system --gid 10001 app \ + && useradd --system --uid 10001 --gid app --home-dir /app --shell /usr/sbin/nologin app \ + && mkdir -p /data /app \ + && chown app:app /data /app + +COPY --from=builder /opt/venv /opt/venv +WORKDIR /app +COPY --chown=app:app .streamlit ./.streamlit +COPY --chown=app:app app ./app + +ENV PATH="/opt/venv/bin:${PATH}" \ + PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + DATA_DIR=/data \ + STREAMLIT_SERVER_PORT=8501 \ + STREAMLIT_SERVER_ADDRESS=0.0.0.0 + +USER app +EXPOSE 8501 +VOLUME ["/data"] + +HEALTHCHECK --interval=30s --timeout=5s --start-period=20s --retries=3 \ + CMD ["python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8501/_stcore/health', timeout=4)"] + +ENTRYPOINT ["streamlit", "run", "app/streamlit_app.py"] diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..13b6130 --- /dev/null +++ b/Makefile @@ -0,0 +1,33 @@ +IMAGE ?= churn-explorer:local + +.PHONY: install lint format test run sample docker-build docker-run clean + +install: ## Create .venv from uv.lock (exact versions) + uv sync --locked + +lint: ## Ruff lint + format check + lockfile check + uv lock --check + uv run ruff check . + uv run ruff format --check . + +format: ## Auto-fix lint and formatting + uv run ruff check --fix . + uv run ruff format . + +test: ## Unit + app tests with coverage + uv run pytest + +run: ## Start the app on http://localhost:8501 + uv run streamlit run app/streamlit_app.py + +sample: ## Write a synthetic dataset to data/sample_events.parquet + uv run python -m churn_app.sample_data --users 500 --out data/sample_events.parquet + +docker-build: ## Build the container image + docker build -t $(IMAGE) . + +docker-run: ## Run the image, mounting ./churn-prediction-25-26 as /data + docker run --rm -p 8501:8501 -v "$(CURDIR)/churn-prediction-25-26:/data:ro" $(IMAGE) + +clean: + rm -rf .pytest_cache .ruff_cache .coverage coverage.xml junit.xml diff --git a/README.md b/README.md index 4895dac..606534a 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,102 @@ # Churn Prediction (Kaggle Competition 25/26) +[![CI](https://github.com/Martinoor/datascience/actions/workflows/ci.yml/badge.svg)](https://github.com/Martinoor/datascience/actions/workflows/ci.yml) +[![Image](https://img.shields.io/badge/image-ghcr.io%2Fmartinoor%2Fchurn--explorer-2496ED?logo=docker&logoColor=white)](https://github.com/Martinoor/datascience/pkgs/container/churn-explorer) +![Python](https://img.shields.io/badge/python-3.12%20%7C%203.13-3776AB?logo=python&logoColor=white) + A machine learning project for predicting user churn from streaming service logs. This project implements multiple approaches including **Transformer-based sequence models**, **XGBoost**, and **ensemble methods** to achieve competitive performance on the [Kaggle Churn Prediction Competition](https://www.kaggle.com/competitions/churn-prediction-25-26). +The repository has two parts: + +1. **Churn Explorer**: a tested, containerised **Streamlit app** (`src/churn_app`, `app/`) to load, filter and explore the event logs and train a churn model interactively. See [Churn Explorer app](#-churn-explorer-app) below. +2. **Research pipelines**: the original notebooks, Transformer / XGBoost / ensemble experiments (`final_experiments/`, root `*.py`, `*.ipynb`). See [Problem statement](#-problem-statement) onwards. + +--- + +## 📉 Churn Explorer app + +### Run it + +| How | Command | Then open | +|-----|---------|-----------| +| Pre-built image | `docker run --rm -p 8501:8501 ghcr.io/martinoor/churn-explorer:latest` | http://localhost:8501 | +| Docker Compose (build locally) | `docker compose up --build` | http://localhost:8501 | +| Local Python | `uv sync --locked && uv run streamlit run app/streamlit_app.py` | http://localhost:8501 | + +Without any data, the app starts on a **deterministic synthetic sample** that has the same 19-column schema and page mix as the Kaggle files. To explore the real data, download `train.parquet` / `test.parquet` from the [competition page](https://www.kaggle.com/competitions/churn-prediction-25-26/data) (the data can't be redistributed) and choose one of: + +- **Mount it**: `docker run --rm -p 8501:8501 -v "$PWD/churn-prediction-25-26:/data:ro" ghcr.io/martinoor/churn-explorer:latest`. Files in `/data` (or `$DATA_DIR` locally, default `./data`) appear under *Source → File in /data/*. +- **Upload it** from the sidebar (up to 2 GB). + +The full training file has ~17.5M rows. Use the sidebar's **User sample (%)** and **date range** options: they are applied batch by batch while reading, so memory stays bounded. + +### What it does + +| Tab | Content | +|-----|---------| +| **Overview** | Events, users, sessions, cancellation rate; daily events / active users; page mix; cancellations per day | +| **Segments** | Churn rate by plan, device, state, and by quantile of any user-level feature | +| **User drill-down** | Per-user daily page timeline and raw events | +| **Churn model** | Gradient boosting or logistic regression on user features, **horizon** (competition) or *ever-cancelled* labels, threshold tuned for **balanced accuracy**, permutation importance, confusion matrix, downloadable risk scores | +| **Data** | Filtered rows, column profile, CSV export | + +Sidebar filters (dates, plan, gender, device, state, pages, hide cancellation events) apply to every tab. + +### Design notes + +- **Label leakage.** Features never use `Cancel` / `Cancellation Confirmation` pages or `auth == "Cancelled"`. In horizon mode they only use events strictly before the cutoff, and a test checks that adding future events changes nothing. The *ever-cancelled* label (used by the research notebooks) is available, but the app warns that recency leaks the answer under it. +- **Privacy.** `firstName` / `lastName` are dropped when files are read. +- **Typing.** User IDs are always strings: with CSV input, one blank ID would otherwise turn `1749042` into `1749042.0`. There is a test for this. + +### Project layout (app) + +``` +src/churn_app/ + data.py # read (parquet/csv, path/bytes/upload), validate, normalise, EventFilter, churn_labels + features.py # leakage-safe user-level features + chart aggregations + model.py # training, balanced-accuracy threshold tuning, scoring + sample_data.py # deterministic synthetic generator (also a CLI) +app/streamlit_app.py # UI only: caching, widgets, charts +tests/ # unit tests + headless Streamlit AppTest end-to-end tests +Dockerfile, docker-compose.yml, .dockerignore +.github/workflows/ci.yml, .github/dependabot.yml, .pre-commit-config.yaml +pyproject.toml, uv.lock, .python-version, Makefile +``` + +### Development + +```bash +uv sync --locked # exact environment from uv.lock (Python 3.12, see .python-version) +make lint # lockfile check + ruff lint + ruff format --check +make test # pytest with branch coverage (fails under 85%) +make run # streamlit on :8501 +make sample # write data/sample_events.parquet via the generator CLI +make docker-build docker-run +uv tool install pre-commit && pre-commit install # same checks on every commit +``` + +The tests cover the data layer in detail: format inference, reading from paths, bytes and file-like uploads, missing-column errors, `ts` → `time` derivation, timezone handling, day-inclusive date ranges, deterministic user sampling that is identical across batch sizes, device/state parsing, every filter dimension and their AND/OR semantics, and the edges of the horizon label window. The app itself is exercised headlessly with `streamlit.testing.v1.AppTest`: it renders, filters, trains a model and picks up files in `DATA_DIR`. + +### CI/CD and reproducibility + +GitHub Actions ([`ci.yml`](.github/workflows/ci.yml)) runs on every push and pull request: + +1. **Lint**: `uv lock --check`, then ruff lint and format checks. +2. **Test**: pytest with coverage on Python 3.12 and 3.13, installed with `uv sync --locked`. JUnit and coverage reports are uploaded as artifacts. +3. **Docker**: builds the image, generates data inside it, starts the container, waits for the health check, checks the HTTP endpoints and that it runs as the non-root user. On `main` and `v*.*.*` tags, it pushes a multi-arch (`linux/amd64`, `linux/arm64`) image with SBOM and provenance to `ghcr.io/martinoor/churn-explorer`, tagged `latest`, `sha-` and semver. Pushing to Docker Hub as well is optional: set the repo variable `DOCKERHUB_USERNAME` and the secret `DOCKERHUB_TOKEN`. + +Reproducibility guarantees: + +- Python dependencies are pinned in `uv.lock` and installed with `--locked` / `--frozen` everywhere (local, CI, Docker). +- The Python version is pinned in `.python-version`; base images (`python:3.12-slim-bookworm`, `uv`) are pinned **by digest**. +- Model training, the train/validation split, user sampling and synthetic data are all seeded, and tests check that repeated runs give identical output. +- Dependabot opens weekly PRs for uv, Docker base images and GitHub Actions, so pins don't go stale. +- The runtime image contains only the locked virtualenv, the app and its config. It runs as UID 10001 with a `HEALTHCHECK`. + +To reproduce a published image exactly, use its `sha-` tag or check out that commit and run `docker build .`. + +--- + ## 🎯 Problem Statement Predict whether users will churn (visit the `Cancellation Confirmation` page) within a 10-day window following the observation period (after `2018-11-20`). @@ -205,6 +300,7 @@ Submissions are automatically tracked in `submission_log.csv`. To manually submi ```python from kaggle_submit import submit_and_track + submit_and_track("submission.csv", "churn-prediction-25-26", "run-note") ``` diff --git a/app/streamlit_app.py b/app/streamlit_app.py new file mode 100644 index 0000000..23b2f95 --- /dev/null +++ b/app/streamlit_app.py @@ -0,0 +1,418 @@ +"""Churn Explorer — Streamlit front-end for the churn_app package. + +Run locally: uv run streamlit run app/streamlit_app.py +Run in Docker: docker run -p 8501:8501 ghcr.io/martinoor/datascience:latest +""" + +from __future__ import annotations + +import datetime as dt +import hashlib +import os +from pathlib import Path + +import pandas as pd +import plotly.express as px +import streamlit as st + +from churn_app import __version__ +from churn_app.data import ( + CHURN_PAGE, + EventFilter, + SchemaError, + churn_labels, + filter_events, + filter_options, + load_events, + normalize_events, +) +from churn_app.features import ( + build_user_features, + churn_rate_by, + daily_activity, + page_distribution, + user_timeline, +) +from churn_app.model import MODEL_LABELS, score_users, train_churn_model +from churn_app.sample_data import generate_events + +DATA_DIR = Path(os.environ.get("DATA_DIR", "data")) +KAGGLE_URL = "https://www.kaggle.com/competitions/churn-prediction-25-26" + +st.set_page_config(page_title="Churn Explorer", page_icon="📉", layout="wide") + + +# --------------------------------------------------------------------------- # +# Cached computations. Arguments prefixed with "_" are not hashed; the explicit +# string keys identify the content instead, which avoids hashing large frames. +# --------------------------------------------------------------------------- # +@st.cache_data(show_spinner="Generating synthetic events…", max_entries=4) +def load_sample(n_users: int, seed: int) -> pd.DataFrame: + return normalize_events(generate_events(n_users=n_users, seed=seed)) + + +@st.cache_data(show_spinner="Reading file…", max_entries=2) +def load_file(path: str, mtime: float, start: dt.date | None, end: dt.date | None, fraction: float) -> pd.DataFrame: + del mtime # only part of the cache key, so edited files are re-read + return load_events(path, start=start, end=end, user_fraction=fraction) + + +@st.cache_data(show_spinner="Reading upload…", max_entries=2) +def load_upload(content: bytes, name: str, fraction: float) -> pd.DataFrame: + return load_events(content, fmt=Path(name).suffix, user_fraction=fraction) + + +@st.cache_data(show_spinner=False, max_entries=8) +def cached_filter(_events: pd.DataFrame, data_key: str, flt: EventFilter) -> pd.DataFrame: + return filter_events(_events, flt) + + +@st.cache_data(show_spinner="Building user features…", max_entries=8) +def cached_features(_events: pd.DataFrame, key: str, reference: dt.date | None) -> pd.DataFrame: + return build_user_features(_events, reference) + + +@st.cache_data(show_spinner=False, max_entries=8) +def cached_labels(_events: pd.DataFrame, data_key: str, cutoff: dt.date | None, horizon: int) -> pd.DataFrame: + return churn_labels(_events, cutoff, horizon) + + +# --------------------------------------------------------------------------- # +# Sidebar: data source +# --------------------------------------------------------------------------- # +def sidebar_data() -> tuple[pd.DataFrame, str, str] | None: + st.sidebar.header("1 · Data") + local_files = sorted(p for ext in ("*.parquet", "*.csv") for p in DATA_DIR.glob(ext)) if DATA_DIR.is_dir() else [] + sources = ["Synthetic sample", "Upload a file"] + if local_files: + sources.insert(0, f"File in {DATA_DIR}/") + source = st.sidebar.radio("Source", sources, help=f"Mount real Kaggle files into `{DATA_DIR}/` to see them here.") + + if source == "Synthetic sample": + n_users = st.sidebar.slider("Users", 100, 2_000, 400, step=100) + seed = st.sidebar.number_input("Seed", 0, 10_000, 7) + return load_sample(n_users, int(seed)), f"sample:{n_users}:{seed}", "synthetic sample" + + fraction = ( + st.sidebar.slider( + "User sample (%)", + 1, + 100, + 100, + help="Deterministic hash-based sample of users — use it for the full 17M-row training file.", + ) + / 100 + ) + + if source == "Upload a file": + upload = st.sidebar.file_uploader("Parquet or CSV event log", type=["parquet", "csv"]) + if upload is None: + return None + content = upload.getvalue() + key = f"upload:{hashlib.sha256(content).hexdigest()[:16]}:{fraction}" + return load_upload(content, upload.name, fraction), key, upload.name + + path = st.sidebar.selectbox("File", local_files, format_func=lambda p: p.name) + limit_dates = st.sidebar.checkbox("Only load a date range", value=False) + start = end = None + if limit_dates: + start = st.sidebar.date_input("From", dt.date(2018, 10, 1)) + end = st.sidebar.date_input("To", dt.date(2018, 11, 20)) + mtime = path.stat().st_mtime + key = f"file:{path}:{mtime}:{start}:{end}:{fraction}" + return load_file(str(path), mtime, start, end, fraction), key, path.name + + +def sidebar_filters(events: pd.DataFrame) -> EventFilter: + st.sidebar.header("2 · Filters") + opts = filter_options(events) + min_d, max_d = opts["min_date"], opts["max_date"] + picked = st.sidebar.date_input("Event dates", (min_d, max_d), min_value=min_d, max_value=max_d) + start, end = picked if isinstance(picked, tuple) and len(picked) == 2 else (min_d, max_d) + + def multi(label: str, column: str) -> tuple[str, ...]: + values = opts[column] + if not values: + return () + return tuple(st.sidebar.multiselect(label, values, placeholder="All")) + + return EventFilter( + start=start, + end=end, + levels=multi("Subscription level", "level"), + genders=multi("Gender", "gender"), + devices=multi("Device", "device"), + states=multi("State", "state"), + pages=multi("Pages", "page"), + exclude_leakage=st.sidebar.toggle( + "Hide cancellation events", value=False, help="Drop `Cancel` / `Cancellation Confirmation` rows." + ), + ) + + +# --------------------------------------------------------------------------- # +# Tabs +# --------------------------------------------------------------------------- # +def tab_overview(events: pd.DataFrame, labels_ever: pd.DataFrame) -> None: + users = events["userId"].nunique() + churned = int(labels_ever.loc[labels_ever["userId"].isin(events["userId"].unique()), "churn"].sum()) + c = st.columns(5) + c[0].metric("Events", f"{len(events):,}") + c[1].metric("Users", f"{users:,}") + c[2].metric("Sessions", f"{events['sessionId'].nunique():,}") + c[3].metric("Users who cancelled", f"{churned:,} ({churned / max(users, 1):.1%})") + c[4].metric("Days covered", f"{(events['time'].max() - events['time'].min()).days + 1}") + + left, right = st.columns([3, 2]) + daily = daily_activity(events) + long = daily.melt(id_vars="day", var_name="metric", value_name="count") + long["metric"] = long["metric"].map({"events": "Events", "active_users": "Active users"}) + fig = px.line(long, x="day", y="count", facet_row="metric", title="Daily activity", height=420) + fig.update_yaxes(matches=None, title_text="") + fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1])) + fig.update_layout(xaxis_title="", showlegend=False) + left.plotly_chart(fig, width="stretch") + + pages = page_distribution(events) + fig = px.bar(pages, x="events", y="page", orientation="h", title="Events by page", log_x=True) + fig.update_layout(yaxis={"categoryorder": "total ascending"}, xaxis_title="events (log scale)", yaxis_title="") + right.plotly_chart(fig, width="stretch") + + cancel_days = events.loc[events["page"].astype(str) == CHURN_PAGE, "time"].dt.normalize().value_counts() + if not cancel_days.empty: + fig = px.bar( + cancel_days.sort_index().rename_axis("day").reset_index(name="cancellations"), + x="day", + y="cancellations", + title="Cancellations per day", + ) + st.plotly_chart(fig, width="stretch") + + +def tab_segments(events: pd.DataFrame, features: pd.DataFrame, labels_ever: pd.DataFrame) -> None: + st.caption("User-level churn uses the *ever cancelled* label on the loaded data, before filters.") + cols = st.columns(3) + for col, column, title in zip( + cols, ("level", "device", "state"), ("Subscription level", "Device", "State"), strict=True + ): + if column not in events.columns: + continue + last = events.groupby("userId", observed=True)[column].last().astype(str).rename(column) + seg = last.to_frame().join(labels_ever.set_index("userId")["churn"], how="inner") + agg = seg.groupby(column).agg(users=("churn", "size"), churn_rate=("churn", "mean")).reset_index() + fig = px.bar(agg, x=column, y="churn_rate", hover_data=["users"], title=f"Churn rate by {title.lower()}") + fig.update_layout(yaxis_tickformat=".0%", xaxis_title="", yaxis_title="") + col.plotly_chart(fig, width="stretch") + + if features.empty: + st.info("No users left after filtering.") + return + numeric = [c for c in features.columns if features[c].nunique() > 1] + default = numeric.index("days_since_last_event") if "days_since_last_event" in numeric else 0 + feature = st.selectbox("Churn rate by quantile of…", numeric, index=default) + buckets = churn_rate_by(features, labels_ever, feature) + fig = px.bar(buckets, x="bucket", y="churn_rate", hover_data=["users"], title=f"Churn rate by {feature}") + fig.update_layout(yaxis_tickformat=".0%", xaxis_title=feature, yaxis_title="churn rate") + st.plotly_chart(fig, width="stretch") + if feature == "days_since_last_event": + st.warning( + "Recency looks extremely predictive here, but with the *ever cancelled* label churned users simply " + "stop generating events. Use the horizon label in **Churn model** for an honest estimate." + ) + + +def tab_user(events: pd.DataFrame, labels_ever: pd.DataFrame) -> None: + counts = events["userId"].value_counts() + if counts.empty: + st.info("No users left after filtering.") + return + churned_ids = set(labels_ever.loc[labels_ever["churn"] == 1, "userId"]) + show = st.radio("Pick from", ["Most active users", "Users who cancelled"], horizontal=True) + candidates = [u for u in counts.index if u in churned_ids] if show == "Users who cancelled" else list(counts.index) + if not candidates: + st.info("No matching users in the filtered data.") + return + user = st.selectbox("User ID", candidates[:500]) + ev = events[events["userId"] == user] + c = st.columns(4) + c[0].metric("Events", f"{len(ev):,}") + c[1].metric("Sessions", ev["sessionId"].nunique()) + c[2].metric("Current level", str(ev["level"].iloc[-1])) + c[3].metric("Cancelled", "yes" if user in churned_ids else "no") + tl = user_timeline(events, user) + fig = px.bar(tl, x="day", y="events", color="page", title=f"Daily events for user {user}") + st.plotly_chart(fig, width="stretch") + st.dataframe(ev.tail(50).iloc[::-1], width="stretch", hide_index=True) + + +def tab_model(all_events: pd.DataFrame, events: pd.DataFrame, data_key: str) -> None: + opts = filter_options(all_events) + st.markdown( + "Trains a model on user-level features and tunes the decision threshold for **balanced accuracy**, " + "the competition metric. Users are split into train / validation at random, stratified by label." + ) + with st.form("model_form"): + c = st.columns(4) + mode = c[0].radio( + "Label", + ["Horizon (competition)", "Ever cancelled"], + help="Horizon: features before the cutoff, label = cancels within the next N days. " + "Ever: label = cancels at any time (the original project's proxy; leaks through recency).", + ) + default_cutoff = max(opts["min_date"], opts["max_date"] - dt.timedelta(days=10)) + cutoff = c[1].date_input("Cutoff", default_cutoff, min_value=opts["min_date"], max_value=opts["max_date"]) + horizon = c[1].number_input("Horizon (days)", 1, 30, 10) + model_type = c[2].selectbox("Model", list(MODEL_LABELS), format_func=MODEL_LABELS.get) + val_size = c[2].slider("Validation share", 0.1, 0.5, 0.25, 0.05) + seed = c[3].number_input("Random seed", 0, 10_000, 42) + submitted = st.form_submit_button("Train model", type="primary") + + if submitted: + horizon_mode = mode.startswith("Horizon") + ref = cutoff if horizon_mode else None + labels = cached_labels(all_events, data_key, ref, int(horizon)) + features = cached_features(events, data_key, ref) + try: + with st.spinner("Training…"): + result = train_churn_model(features, labels, model_type, val_size, int(seed)) + except ValueError as exc: + st.error(f"Cannot train: {exc}") + return + st.session_state["trained_model"] = (data_key, result, features, mode, cutoff, horizon) + + if st.session_state.get("trained_model", (None,))[0] != data_key: + st.info("Choose settings and press **Train model**.") + return + + _, result, features, mode, cutoff, horizon = st.session_state["trained_model"] + m = result.metrics + st.subheader(f"{mode} · cutoff {cutoff} · {horizon}-day horizon" if mode.startswith("Horizon") else mode) + c = st.columns(6) + c[0].metric("Balanced accuracy", f"{m['balanced_accuracy']:.3f}") + c[1].metric("ROC AUC", f"{m['roc_auc']:.3f}") + c[2].metric("Recall", f"{m['recall']:.1%}") + c[3].metric("Precision", f"{m['precision']:.1%}") + c[4].metric("Users flagged", f"{m['flagged_share']:.1%}") + c[5].metric("Threshold", f"{result.threshold:.2f}") + st.caption( + f"{int(m['n_train']):,} training / {int(m['n_val']):,} validation users · " + f"base churn rate {m['val_churn_rate']:.1%}" + ) + + left, right = st.columns(2) + imp = result.importance.head(15) + fig = px.bar( + imp, + x="importance", + y="feature", + error_x="std", + orientation="h", + title="Permutation importance (drop in validation AUC)", + ) + fig.update_layout(yaxis={"categoryorder": "total ascending"}, yaxis_title="") + left.plotly_chart(fig, width="stretch") + + val = result.validation.assign(outcome=lambda d: d["churn"].map({0: "retained", 1: "churned"})) + fig = px.histogram( + val, x="proba", color="outcome", nbins=30, barmode="overlay", title="Validation churn probability" + ) + fig.add_vline(x=result.threshold, line_dash="dash", annotation_text="threshold") + right.plotly_chart(fig, width="stretch") + right.dataframe(result.confusion, width="stretch") + + scores = score_users(result, features) + st.markdown("**All users, ranked by churn risk**") + st.caption("Scores for training users are in-sample; use the validation metrics above to judge the model.") + st.dataframe(scores.head(200), width="stretch") + st.download_button("Download scores (CSV)", scores.to_csv().encode(), "churn_scores.csv", "text/csv") + + +def tab_data(events: pd.DataFrame, source_name: str) -> None: + st.markdown(f"**Source:** `{source_name}` · {len(events):,} rows after filters") + st.dataframe(events.head(500), width="stretch", hide_index=True) + profile = pd.DataFrame( + { + "dtype": events.dtypes.astype(str), + "null share": events.isna().mean(), + "distinct": events.nunique(), + } + ) + st.markdown("**Column profile**") + st.dataframe(profile, width="stretch") + if len(events) <= 1_000_000: + st.download_button( + "Download filtered events (CSV)", events.to_csv(index=False).encode(), "events_filtered.csv", "text/csv" + ) + else: + st.caption("Filtered data is over 1M rows; narrow the filters to enable CSV download.") + + +def tab_about() -> None: + st.markdown( + f""" +### About +Interactive companion to the **[Kaggle churn-prediction-25-26]({KAGGLE_URL})** project: predict whether a +streaming-service user visits *Cancellation Confirmation* in the 10 days after the observation window. + +**Data.** The competition files can't be redistributed, so the app ships with a *deterministic synthetic* +generator that reproduces the schema (19 columns) and the page mix. To use the real data, download +`train.parquet` from Kaggle and either upload it or mount its folder at `{DATA_DIR}/` +(`docker run -v $PWD/churn-prediction-25-26:/data …`). First/last names are dropped when files are loaded. + +**Leakage controls.** Features never use `Cancel` / `Cancellation Confirmation` pages or `Cancelled` auth +events, and in horizon mode only events strictly before the cutoff are used. + +**Model.** Fast CPU models from scikit-learn, for interactive use. The Transformer / XGBoost research +pipelines live in `final_experiments/` in the repository. + +Version `{__version__}`. +""" + ) + + +# --------------------------------------------------------------------------- # +def main() -> None: + st.title("📉 Churn Explorer") + st.caption("Explore streaming-service event logs, filter them, and train a churn model.") + + try: + loaded = sidebar_data() + except (SchemaError, ValueError, OSError) as exc: + st.error(f"Could not load data: {exc}") + st.stop() + if loaded is None: + st.info("Upload a `.parquet` or `.csv` event log in the sidebar to begin.") + tab_about() + st.stop() + + all_events, data_key, source_name = loaded + if all_events.empty: + st.warning("The selected data contains no events.") + st.stop() + + flt = sidebar_filters(all_events) + events = cached_filter(all_events, data_key, flt) + st.sidebar.caption(f"{len(events):,} of {len(all_events):,} events after filters") + if events.empty: + st.warning("No events match the current filters.") + st.stop() + + labels_ever = cached_labels(all_events, data_key, None, 10) + features = cached_features(events, f"{data_key}:{flt}", None) + + tabs = st.tabs(["Overview", "Segments", "User drill-down", "Churn model", "Data", "About"]) + with tabs[0]: + tab_overview(events, labels_ever) + with tabs[1]: + tab_segments(events, features, labels_ever) + with tabs[2]: + tab_user(events, labels_ever) + with tabs[3]: + tab_model(all_events, events, f"{data_key}:{flt}") + with tabs[4]: + tab_data(events, source_name) + with tabs[5]: + tab_about() + + +main() diff --git a/data/.gitkeep b/data/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..d6be1ab --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,12 @@ +# docker compose up --build -> http://localhost:8501 +# Put the Kaggle files (train.parquet, test.parquet) in ./churn-prediction-25-26 +# to browse the real data; otherwise the app uses its synthetic sample. +services: + app: + build: . + image: ghcr.io/martinoor/churn-explorer:local + ports: + - "8501:8501" + volumes: + - ./churn-prediction-25-26:/data:ro + restart: unless-stopped diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..cea8705 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,71 @@ +[project] +name = "churn-app" +version = "1.0.0" +description = "Streamlit explorer and churn model for the Kaggle churn-prediction-25-26 streaming-service logs" +readme = "README.md" +requires-python = ">=3.12,<3.14" +authors = [{ name = "Martino Orioli" }] +dependencies = [ + "numpy>=2.2,<3", + "pandas>=2.2,<4", + "plotly>=6,<8", + "pyarrow>=18", + "scikit-learn>=1.6,<2", + "streamlit>=1.50,<2", +] + +[dependency-groups] +dev = [ + "pre-commit>=4", + "pytest>=8", + "pytest-cov>=6", + "ruff>=0.15", +] + +[build-system] +requires = ["hatchling>=1.26"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/churn_app"] + +[tool.uv] +default-groups = ["dev"] + +# --------------------------------------------------------------------------- # +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "-ra --strict-markers --cov=churn_app --cov-report=term-missing" +markers = ["slow: end-to-end tests that run the Streamlit app"] +filterwarnings = ["error::FutureWarning:churn_app.*"] + +[tool.coverage.run] +branch = true +source = ["churn_app"] + +[tool.coverage.report] +fail_under = 85 +show_missing = true +exclude_also = ["if __name__ == .__main__.:"] + +# --------------------------------------------------------------------------- # +[tool.ruff] +line-length = 120 +target-version = "py312" +# The research code predates this app and is kept as-is. +extend-exclude = ["final_experiments", "*.ipynb", "feature_pipeline.py", "kaggle_submit.py", + "submission_utils.py", "train_event_ensemble.py", "transformer_model.py"] + +[tool.ruff.lint] +select = ["E", "F", "W", "I", "B", "UP", "SIM", "N", "PD", "NPY", "RUF"] +ignore = [ + "N803", "N806", # sklearn-style X / X_train names + "PD101", # nunique() > 1 reads clearer than the suggested idiom +] + +[tool.ruff.lint.per-file-ignores] +"src/churn_app/sample_data.py" = ["E501"] # verbatim user-agent strings +"tests/conftest.py" = ["E501"] # one fixture event per line + +[tool.ruff.lint.isort] +known-first-party = ["churn_app"] diff --git a/src/churn_app/__init__.py b/src/churn_app/__init__.py new file mode 100644 index 0000000..959d31c --- /dev/null +++ b/src/churn_app/__init__.py @@ -0,0 +1,24 @@ +"""Churn explorer: data loading, filtering, features and modelling for the +Kaggle *churn-prediction-25-26* streaming-service event logs.""" + +from churn_app.data import ( + CHURN_PAGE, + EventFilter, + SchemaError, + churn_labels, + filter_events, + filter_options, + load_events, +) + +__all__ = [ + "CHURN_PAGE", + "EventFilter", + "SchemaError", + "churn_labels", + "filter_events", + "filter_options", + "load_events", +] + +__version__ = "1.0.0" diff --git a/src/churn_app/data.py b/src/churn_app/data.py new file mode 100644 index 0000000..5bf0254 --- /dev/null +++ b/src/churn_app/data.py @@ -0,0 +1,354 @@ +"""Loading, validating, normalising and filtering raw event logs. + +The raw Kaggle files (``train.parquet`` / ``test.parquet``) hold one row per +user event with 19 columns. The real training file has ~17.5M rows, so loading +is done in record batches with optional date-range and user-sampling filters +applied batch by batch, which keeps peak memory bounded. +""" + +from __future__ import annotations + +import datetime as dt +import io +from collections.abc import Iterable, Iterator +from dataclasses import dataclass, field +from pathlib import Path +from typing import BinaryIO, Literal + +import numpy as np +import pandas as pd +import pyarrow.parquet as pq + +CHURN_PAGE = "Cancellation Confirmation" +# Pages/auth values that only appear at (or right before) cancellation. They +# must never be used as features, otherwise the label leaks into the inputs. +LEAKAGE_PAGES: tuple[str, ...] = ("Cancellation Confirmation", "Cancel") +LEAKAGE_AUTH: tuple[str, ...] = ("Cancelled",) + +# Columns the app cannot work without. ``time`` may be derived from ``ts``. +REQUIRED_COLUMNS: tuple[str, ...] = ("userId", "sessionId", "page", "level") +OPTIONAL_COLUMNS: tuple[str, ...] = ( + "time", + "ts", + "status", + "gender", + "auth", + "itemInSession", + "location", + "userAgent", + "method", + "length", + "song", + "artist", + "registration", +) +# Personal data that is never needed for analysis; dropped on load. +PII_COLUMNS: tuple[str, ...] = ("firstName", "lastName") +CATEGORICAL_COLUMNS: tuple[str, ...] = ("page", "level", "auth", "gender", "method", "device", "state") + +SUPPORTED_FORMATS = ("parquet", "csv") +FileFormat = Literal["parquet", "csv"] +Source = str | Path | bytes | BinaryIO + + +class SchemaError(ValueError): + """Raised when an input file does not look like a churn event log.""" + + +# --------------------------------------------------------------------------- # +# Reading +# --------------------------------------------------------------------------- # +def infer_format(source: Source, fmt: str | None = None) -> FileFormat: + """Return the file format, from ``fmt`` if given, else from the file name.""" + if fmt is not None: + name = fmt + elif isinstance(source, (str, Path)): + name = Path(source).suffix + else: + name = Path(getattr(source, "name", "")).suffix + name = name.lower().lstrip(".") + if name == "pq": + name = "parquet" + if name not in SUPPORTED_FORMATS: + raise ValueError(f"Unsupported or unknown file format {name!r}; expected one of {SUPPORTED_FORMATS}") + return name # type: ignore[return-value] + + +def _as_readable(source: Source) -> str | BinaryIO: + if isinstance(source, Path): + return str(source) + if isinstance(source, (bytes, bytearray)): + return io.BytesIO(source) + return source + + +def _iter_batches(source: Source, fmt: FileFormat, batch_rows: int) -> Iterator[pd.DataFrame]: + readable = _as_readable(source) + if fmt == "parquet": + pf = pq.ParquetFile(readable) + available = set(pf.schema_arrow.names) + wanted = [c for c in (*REQUIRED_COLUMNS, *OPTIONAL_COLUMNS) if c in available] + missing = [c for c in REQUIRED_COLUMNS if c not in available] + if missing: + raise SchemaError(f"Missing required columns: {missing}") + for batch in pf.iter_batches(batch_size=batch_rows, columns=wanted): + yield batch.to_pandas() + else: + # IDs must stay text: one blank userId would otherwise turn "1749042" into 1749042.0 + yield from pd.read_csv(readable, chunksize=batch_rows, low_memory=False, dtype={"userId": "string"}) + + +def user_bucket(user_ids: pd.Series, buckets: int = 10_000) -> np.ndarray: + """Deterministic bucket in ``[0, buckets)`` for each user id. + + ``pandas.util.hash_pandas_object`` uses a fixed hash key, so the same user + always lands in the same bucket across runs, machines and batches. + """ + hashed = pd.util.hash_pandas_object(user_ids.astype(str), index=False).to_numpy() + return (hashed % np.uint64(buckets)).astype(np.int64) + + +def read_events( + source: Source, + *, + fmt: str | None = None, + start: dt.date | None = None, + end: dt.date | None = None, + user_fraction: float = 1.0, + batch_rows: int = 1_000_000, +) -> pd.DataFrame: + """Read an event log, applying cheap row filters batch by batch. + + Args: + source: path, raw bytes or binary file-like object (e.g. a Streamlit upload). + fmt: ``"parquet"`` or ``"csv"``; inferred from the file name when omitted. + start, end: inclusive calendar-day bounds on the event time. + user_fraction: keep a deterministic hash-based sample of users (0, 1]. + batch_rows: rows per record batch. + """ + if not 0 < user_fraction <= 1: + raise ValueError(f"user_fraction must be in (0, 1], got {user_fraction}") + file_format = infer_format(source, fmt) + + parts: list[pd.DataFrame] = [] + for batch in _iter_batches(source, file_format, batch_rows): + batch = batch.drop(columns=[c for c in PII_COLUMNS if c in batch.columns]) + if "time" not in batch.columns and "ts" in batch.columns: + batch["time"] = pd.to_datetime(batch["ts"], unit="ms") + if (start is not None or end is not None) and "time" in batch.columns: + batch = batch[_date_mask(_to_datetime(batch["time"]), start, end)] + if user_fraction < 1: + keep = user_bucket(batch["userId"]) < round(user_fraction * 10_000) + batch = batch[keep] + parts.append(batch) + + if not parts: + raise SchemaError("The file contains no rows") + return pd.concat(parts, ignore_index=True) + + +# --------------------------------------------------------------------------- # +# Validation and normalisation +# --------------------------------------------------------------------------- # +def validate_columns(columns: Iterable[str]) -> None: + """Raise :class:`SchemaError` if required columns are missing.""" + cols = set(columns) + missing = [c for c in REQUIRED_COLUMNS if c not in cols] + if "time" not in cols and "ts" not in cols: + missing.append("time (or ts)") + if missing: + raise SchemaError(f"Missing required columns: {missing}") + + +def _to_datetime(values: pd.Series) -> pd.Series: + if pd.api.types.is_datetime64_any_dtype(values): + return values.dt.tz_localize(None) if values.dt.tz is not None else values + if pd.api.types.is_numeric_dtype(values): + return pd.to_datetime(values, unit="ms", errors="coerce") + return pd.to_datetime(values, errors="coerce", format="mixed") + + +def detect_device(user_agent: pd.Series) -> pd.Series: + """Coarse device family from the user-agent string. + + Vectorised equivalent of ``feature_pipeline.detect_device``; rules are + checked in the same priority order (iPhone before Mac, etc.). + """ + ua = user_agent.astype("string").str.lower() + rules = [ + ("iPhone", "iphone"), + ("iPad", "ipad"), + ("Mac", "macintosh|mac os x"), + ("Windows", "windows"), + ("Android", "android"), + ("Linux", "linux|ubuntu"), + ] + device = pd.Series("Other", index=user_agent.index, dtype="object") + assigned = pd.Series(False, index=user_agent.index) + for label, pattern in rules: + hit = ua.str.contains(pattern, regex=True, na=False) & ~assigned + device[hit] = label + assigned |= hit + return device + + +def parse_state(location: pd.Series) -> pd.Series: + """Primary US state from ``"City-Metro, ST-ST2"`` style locations.""" + state = location.astype("string").str.rsplit(", ", n=1).str[-1].str.split("-").str[0] + return state.fillna("Unknown").astype("object") + + +def normalize_events(df: pd.DataFrame) -> pd.DataFrame: + """Return a clean, typed, sorted copy of a raw event log. + + - drops PII columns (first/last name); + - derives ``time`` from epoch-millisecond ``ts`` when needed; + - drops rows with no user id (logged-out traffic) or unparseable time; + - derives ``device`` (from ``userAgent``) and ``state`` (from ``location``); + - stores low-cardinality text columns as ``category`` to save memory; + - sorts by ``userId`` then ``time``. + """ + validate_columns(df.columns) + out = df.drop(columns=[c for c in PII_COLUMNS if c in df.columns]).copy() + + out["time"] = _to_datetime(out["time"] if "time" in out.columns else out["ts"]) + if pd.api.types.is_float_dtype(out["userId"]): # integer ids with nulls, e.g. from other writers + out["userId"] = out["userId"].astype("Int64") + out["userId"] = out["userId"].astype("string").str.strip() + out = out[out["userId"].notna() & (out["userId"] != "") & out["time"].notna()] + + if "registration" in out.columns: + out["registration"] = _to_datetime(out["registration"]) + if "status" in out.columns: + out["status"] = pd.to_numeric(out["status"], errors="coerce").astype("Int64") + if "length" in out.columns: + out["length"] = pd.to_numeric(out["length"], errors="coerce") + out["sessionId"] = pd.to_numeric(out["sessionId"], errors="coerce").astype("Int64") + + if "userAgent" in out.columns: + out["device"] = detect_device(out["userAgent"]) + out = out.drop(columns=["userAgent"]) + if "location" in out.columns: + out["state"] = parse_state(out["location"]) + + out["userId"] = out["userId"].astype(str) + for col in CATEGORICAL_COLUMNS: + if col in out.columns: + out[col] = out[col].astype("string").fillna("Unknown").astype("category") + + return out.sort_values(["userId", "time"], kind="mergesort").reset_index(drop=True) + + +def load_events(source: Source, **read_kwargs: object) -> pd.DataFrame: + """Read (see :func:`read_events`) and normalise an event log in one call.""" + return normalize_events(read_events(source, **read_kwargs)) # type: ignore[arg-type] + + +# --------------------------------------------------------------------------- # +# Filtering +# --------------------------------------------------------------------------- # +@dataclass(frozen=True) +class EventFilter: + """Declarative filter over a normalised event log. + + Empty tuples mean "no constraint" for that dimension. + """ + + start: dt.date | None = None + end: dt.date | None = None + levels: tuple[str, ...] = () + genders: tuple[str, ...] = () + devices: tuple[str, ...] = () + states: tuple[str, ...] = () + pages: tuple[str, ...] = () + user_ids: tuple[str, ...] = field(default=()) + exclude_leakage: bool = False + + def __post_init__(self) -> None: + if self.start is not None and self.end is not None and self.start > self.end: + raise ValueError(f"start ({self.start}) must not be after end ({self.end})") + + +def _date_mask(time: pd.Series, start: dt.date | None, end: dt.date | None) -> pd.Series: + mask = pd.Series(True, index=time.index) + if start is not None: + mask &= time >= pd.Timestamp(start) + if end is not None: + mask &= time < pd.Timestamp(end) + pd.Timedelta(days=1) + return mask + + +def filter_events(df: pd.DataFrame, flt: EventFilter) -> pd.DataFrame: + """Apply an :class:`EventFilter`; returns a new DataFrame with a fresh index.""" + mask = _date_mask(df["time"], flt.start, flt.end) + for column, allowed in ( + ("level", flt.levels), + ("gender", flt.genders), + ("device", flt.devices), + ("state", flt.states), + ("page", flt.pages), + ("userId", flt.user_ids), + ): + if not allowed: + continue + if column not in df.columns: + raise KeyError(f"Cannot filter on {column!r}: column not in data") + mask &= df[column].astype(str).isin([str(v) for v in allowed]) + if flt.exclude_leakage: + mask &= ~df["page"].astype(str).isin(LEAKAGE_PAGES) + if "auth" in df.columns: + mask &= ~df["auth"].astype(str).isin(LEAKAGE_AUTH) + return df.loc[mask].reset_index(drop=True) + + +def filter_options(df: pd.DataFrame) -> dict[str, object]: + """Values available for each filter widget (sorted), plus the date range.""" + options: dict[str, object] = { + "min_date": df["time"].min().date() if len(df) else None, + "max_date": df["time"].max().date() if len(df) else None, + } + for column in ("level", "gender", "device", "state", "page"): + if column in df.columns: + options[column] = sorted(str(v) for v in df[column].dropna().unique()) + else: + options[column] = [] + return options + + +# --------------------------------------------------------------------------- # +# Labels +# --------------------------------------------------------------------------- # +def churn_labels( + df: pd.DataFrame, + cutoff: dt.date | pd.Timestamp | None = None, + horizon_days: int = 10, +) -> pd.DataFrame: + """Per-user churn labels. + + - ``cutoff=None`` ("ever" mode, as in the original project): a user churns + if they ever reach the ``Cancellation Confirmation`` page. + - with a cutoff ("horizon" mode, the competition definition): only users + active before the cutoff and not yet churned are kept, and they churn if + the confirmation happens in ``(cutoff, cutoff + horizon_days]``. + + Returns columns ``userId``, ``churn`` (0/1) and ``churn_time`` (NaT if none). + """ + churn_time = df.loc[df["page"].astype(str) == CHURN_PAGE].groupby("userId", observed=True)["time"].min() + first_seen = df.groupby("userId", observed=True)["time"].min() + labels = pd.DataFrame({"userId": first_seen.index.astype(str)}) + labels["churn_time"] = labels["userId"].map(churn_time) + + if cutoff is None: + labels["churn"] = labels["churn_time"].notna().astype("int8") + return labels.reset_index(drop=True) + + if horizon_days <= 0: + raise ValueError(f"horizon_days must be positive, got {horizon_days}") + cutoff_ts = pd.Timestamp(cutoff) + horizon_end = cutoff_ts + pd.Timedelta(days=horizon_days) + active_before = labels["userId"].map(first_seen) < cutoff_ts + already_churned = labels["churn_time"] <= cutoff_ts + labels = labels[active_before & ~already_churned.fillna(False)].copy() + in_window = (labels["churn_time"] > cutoff_ts) & (labels["churn_time"] <= horizon_end) + labels["churn"] = in_window.fillna(False).astype("int8") + return labels.reset_index(drop=True) diff --git a/src/churn_app/features.py b/src/churn_app/features.py new file mode 100644 index 0000000..1afeaa8 --- /dev/null +++ b/src/churn_app/features.py @@ -0,0 +1,156 @@ +"""User-level feature engineering and aggregations used by the app. + +Features are computed from events strictly *before* a reference time, and +cancellation pages are always excluded, so labels can't leak into inputs. +""" + +from __future__ import annotations + +import datetime as dt + +import numpy as np +import pandas as pd + +from churn_app.data import LEAKAGE_AUTH, LEAKAGE_PAGES + +# Page -> feature name for per-user page counts. +PAGE_COUNT_FEATURES: dict[str, str] = { + "NextSong": "songs_played", + "Thumbs Up": "thumbs_up", + "Thumbs Down": "thumbs_down", + "Add to Playlist": "add_to_playlist", + "Add Friend": "add_friend", + "Roll Advert": "adverts", + "Help": "help", + "Settings": "settings", + "Error": "errors", + "Upgrade": "upgrade_views", + "Downgrade": "downgrade_views", + "Submit Upgrade": "submit_upgrade", + "Submit Downgrade": "submit_downgrade", + "Logout": "logouts", +} +RECENT_DAYS = 7 + + +def _feature_events(events: pd.DataFrame, reference: pd.Timestamp) -> pd.DataFrame: + mask = (events["time"] < reference) & ~events["page"].astype(str).isin(LEAKAGE_PAGES) + if "auth" in events.columns: + mask &= ~events["auth"].astype(str).isin(LEAKAGE_AUTH) + return events.loc[mask] + + +def build_user_features( + events: pd.DataFrame, + reference: dt.date | pd.Timestamp | None = None, +) -> pd.DataFrame: + """One row per user (indexed by ``userId``) of numeric behavioural features. + + Args: + events: normalised events (see :func:`churn_app.data.normalize_events`). + reference: features use events strictly before this time. Defaults to + one second after the last event, i.e. use everything. + """ + reference_ts = events["time"].max() + pd.Timedelta(seconds=1) if reference is None else pd.Timestamp(reference) + + ev = _feature_events(events, reference_ts) + if ev.empty: + return pd.DataFrame(index=pd.Index([], name="userId", dtype=str)) + + g = ev.groupby("userId", observed=True) + feats = pd.DataFrame( + { + "n_events": g.size(), + "n_sessions": g["sessionId"].nunique(), + "active_days": ev["time"].dt.normalize().groupby(ev["userId"], observed=True).nunique(), + "first_event": g["time"].min(), + "last_event": g["time"].max(), + } + ) + feats["days_since_last_event"] = (reference_ts - feats["last_event"]).dt.total_seconds() / 86_400 + feats["days_observed"] = (feats["last_event"] - feats["first_event"]).dt.total_seconds() / 86_400 + feats["events_per_session"] = feats["n_events"] / feats["n_sessions"].clip(lower=1) + feats["events_per_active_day"] = feats["n_events"] / feats["active_days"].clip(lower=1) + + page_counts = pd.crosstab(ev["userId"].astype(str), ev["page"].astype(str)) + for page, name in PAGE_COUNT_FEATURES.items(): + feats[name] = page_counts[page] if page in page_counts.columns else 0 + feats[list(PAGE_COUNT_FEATURES.values())] = feats[list(PAGE_COUNT_FEATURES.values())].fillna(0) + + n = feats["n_events"].clip(lower=1) + feats["thumbs_down_ratio"] = feats["thumbs_down"] / (feats["thumbs_up"] + feats["thumbs_down"]).clip(lower=1) + feats["advert_rate"] = feats["adverts"] / n + feats["error_rate"] = feats["errors"] / n + feats["help_rate"] = feats["help"] / n + feats["downgrade_rate"] = feats["downgrade_views"] / n + + recent = ev[ev["time"] >= reference_ts - pd.Timedelta(days=RECENT_DAYS)] + feats["events_last_7d"] = recent.groupby("userId", observed=True).size().reindex(feats.index).fillna(0) + weeks_observed = (feats["days_observed"] / RECENT_DAYS).clip(lower=1) + feats["recent_activity_ratio"] = feats["events_last_7d"] / (feats["n_events"] / weeks_observed) + + feats["paid_share"] = (ev["level"].astype(str) == "paid").groupby(ev["userId"], observed=True).mean() + last = ev.groupby("userId", observed=True).tail(1).set_index("userId") + feats["is_paid_now"] = (last["level"].astype(str) == "paid").astype(int) + + if "status" in ev.columns: + feats["http_404_rate"] = (ev["status"] == 404).groupby(ev["userId"], observed=True).mean() + if "gender" in ev.columns: + feats["is_male"] = (last["gender"].astype(str) == "M").astype(int) + if "registration" in ev.columns: + tenure = (reference_ts - last["registration"]).dt.total_seconds() / 86_400 + feats["tenure_days"] = tenure + if "length" in ev.columns: + songs = ev[ev["page"].astype(str) == "NextSong"] + feats["listening_hours"] = ( + songs.groupby("userId", observed=True)["length"].sum().reindex(feats.index).fillna(0) / 3600 + ) + + feats = feats.drop(columns=["first_event", "last_event"]) + feats.index = feats.index.astype(str) + feats.index.name = "userId" + return feats.replace([np.inf, -np.inf], np.nan).astype("float64") + + +# --------------------------------------------------------------------------- # +# Aggregations for charts +# --------------------------------------------------------------------------- # +def daily_activity(events: pd.DataFrame) -> pd.DataFrame: + """Events and distinct active users per calendar day.""" + if events.empty: + return pd.DataFrame(columns=["day", "events", "active_users"]) + day = events["time"].dt.normalize().rename("day") + out = events.groupby(day).agg(events=("userId", "size"), active_users=("userId", "nunique")) + return out.reset_index() + + +def page_distribution(events: pd.DataFrame) -> pd.DataFrame: + """Event count and share per page, most frequent first.""" + counts = events["page"].astype(str).value_counts() + out = counts.rename_axis("page").reset_index(name="events") + out["share"] = out["events"] / max(int(out["events"].sum()), 1) + return out + + +def churn_rate_by(features: pd.DataFrame, labels: pd.DataFrame, column: str, bins: int = 5) -> pd.DataFrame: + """Churn rate by quantile bucket of a numeric feature (or by value if binary).""" + joined = features[[column]].join(labels.set_index("userId")["churn"], how="inner").dropna() + if joined.empty: + return pd.DataFrame(columns=["bucket", "users", "churn_rate"]) + if joined[column].nunique() <= 2: + bucket = joined[column].astype(int).astype(str) + else: + bucket = pd.qcut(joined[column], q=bins, duplicates="drop").astype(str) + out = joined.groupby(bucket, sort=False).agg(users=("churn", "size"), churn_rate=("churn", "mean")) + out = out.rename_axis("bucket").reset_index() + order = joined.groupby(bucket, sort=False)[column].min().reindex(out["bucket"]).to_numpy() + return out.iloc[np.argsort(order, kind="stable")].reset_index(drop=True) + + +def user_timeline(events: pd.DataFrame, user_id: str) -> pd.DataFrame: + """Daily page counts for one user (long format: day, page, events).""" + ev = events[events["userId"].astype(str) == str(user_id)] + if ev.empty: + return pd.DataFrame(columns=["day", "page", "events"]) + out = ev.groupby([ev["time"].dt.normalize().rename("day"), ev["page"].astype(str)]).size() + return out.rename("events").reset_index() diff --git a/src/churn_app/model.py b/src/churn_app/model.py new file mode 100644 index 0000000..f317f2c --- /dev/null +++ b/src/churn_app/model.py @@ -0,0 +1,154 @@ +"""Lightweight churn model: training, threshold tuning and evaluation. + +The competition metric is balanced accuracy, so the decision threshold is +tuned for balanced accuracy on the validation split rather than left at 0.5. +The research code in this repository (``final_experiments/``) holds the +heavier Transformer / XGBoost models; this module is the fast, CPU-only +counterpart suited for interactive use. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal + +import numpy as np +import pandas as pd +from sklearn.base import ClassifierMixin +from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.impute import SimpleImputer +from sklearn.inspection import permutation_importance +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import ( + balanced_accuracy_score, + confusion_matrix, + precision_score, + recall_score, + roc_auc_score, +) +from sklearn.model_selection import train_test_split +from sklearn.pipeline import Pipeline, make_pipeline +from sklearn.preprocessing import StandardScaler + +ModelType = Literal["gradient_boosting", "logistic_regression"] +MODEL_LABELS: dict[ModelType, str] = { + "gradient_boosting": "Gradient boosting (HistGradientBoosting)", + "logistic_regression": "Logistic regression", +} + + +@dataclass +class TrainResult: + model: Pipeline + threshold: float + metrics: dict[str, float] + confusion: pd.DataFrame + importance: pd.DataFrame + validation: pd.DataFrame # userId-indexed: churn, proba, pred + feature_names: list[str] + + +def make_model(model_type: ModelType, seed: int = 42) -> Pipeline: + estimator: ClassifierMixin + if model_type == "gradient_boosting": + estimator = HistGradientBoostingClassifier( + max_iter=300, + learning_rate=0.05, + max_leaf_nodes=31, + l2_regularization=1.0, + class_weight="balanced", + early_stopping=False, + random_state=seed, + ) + return make_pipeline(estimator) + if model_type == "logistic_regression": + estimator = LogisticRegression(max_iter=2_000, class_weight="balanced", random_state=seed) + return make_pipeline(SimpleImputer(strategy="median"), StandardScaler(), estimator) + raise ValueError(f"Unknown model_type {model_type!r}") + + +def best_threshold(y_true: np.ndarray, proba: np.ndarray, grid: np.ndarray | None = None) -> tuple[float, float]: + """Threshold maximising balanced accuracy. Returns ``(threshold, score)``. + + Ties are broken towards the threshold closest to 0.5 for stability. + """ + if grid is None: + grid = np.round(np.arange(0.05, 0.951, 0.01), 2) + y_true = np.asarray(y_true) + proba = np.asarray(proba) + scores = np.array([balanced_accuracy_score(y_true, (proba >= t).astype(int)) for t in grid]) + best = np.flatnonzero(np.isclose(scores, scores.max())) + idx = best[np.argmin(np.abs(grid[best] - 0.5))] + return float(grid[idx]), float(scores[idx]) + + +def train_churn_model( + features: pd.DataFrame, + labels: pd.DataFrame, + model_type: ModelType = "gradient_boosting", + val_size: float = 0.25, + seed: int = 42, + importance_repeats: int = 5, +) -> TrainResult: + """Fit a model on user features, tune the threshold and evaluate on a + stratified hold-out split of users.""" + data = features.join(labels.set_index("userId")["churn"], how="inner") + if data.empty: + raise ValueError("No overlap between features and labels") + y = data.pop("churn").astype(int).to_numpy() + classes, counts = np.unique(y, return_counts=True) + if len(classes) < 2 or counts.min() < 4: + raise ValueError("Need at least 4 churned and 4 retained users to train a model") + + X_train, X_val, y_train, y_val = train_test_split(data, y, test_size=val_size, stratify=y, random_state=seed) + model = make_model(model_type, seed) + model.fit(X_train, y_train) + + proba = model.predict_proba(X_val)[:, 1] + threshold, bal_acc = best_threshold(y_val, proba) + pred = (proba >= threshold).astype(int) + + metrics = { + "balanced_accuracy": bal_acc, + "roc_auc": float(roc_auc_score(y_val, proba)), + "precision": float(precision_score(y_val, pred, zero_division=0)), + "recall": float(recall_score(y_val, pred, zero_division=0)), + "flagged_share": float(pred.mean()), + "val_churn_rate": float(y_val.mean()), + "n_train": float(len(y_train)), + "n_val": float(len(y_val)), + } + cm = confusion_matrix(y_val, pred, labels=[0, 1]) + confusion = pd.DataFrame( + cm, index=["actual: retained", "actual: churned"], columns=["predicted: retained", "predicted: churned"] + ) + + imp = permutation_importance( + model, X_val, y_val, scoring="roc_auc", n_repeats=importance_repeats, random_state=seed + ) + importance = ( + pd.DataFrame({"feature": data.columns, "importance": imp.importances_mean, "std": imp.importances_std}) + .sort_values("importance", ascending=False) + .reset_index(drop=True) + ) + validation = pd.DataFrame({"churn": y_val, "proba": proba, "pred": pred}, index=X_val.index) + + return TrainResult( + model=model, + threshold=threshold, + metrics=metrics, + confusion=confusion, + importance=importance, + validation=validation, + feature_names=list(data.columns), + ) + + +def score_users(result: TrainResult, features: pd.DataFrame) -> pd.DataFrame: + """Churn probability and 0/1 prediction for every user in ``features``.""" + X = features.reindex(columns=result.feature_names) + proba = result.model.predict_proba(X)[:, 1] + return pd.DataFrame( + {"churn_probability": proba, "predicted_churn": (proba >= result.threshold).astype(int)}, + index=features.index, + ).sort_values("churn_probability", ascending=False) diff --git a/src/churn_app/sample_data.py b/src/churn_app/sample_data.py new file mode 100644 index 0000000..179680f --- /dev/null +++ b/src/churn_app/sample_data.py @@ -0,0 +1,211 @@ +"""Deterministic synthetic event logs with the same schema as the Kaggle data. + +The competition data can't be redistributed, so the app, the tests and CI all +use this generator. Output is fully determined by the arguments, so the same +seed always yields the same rows. + +Usage:: + + python -m churn_app.sample_data --users 500 --out data/sample_events.parquet +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import numpy as np +import pandas as pd + +START = pd.Timestamp("2018-10-01") +END = pd.Timestamp("2018-11-20") + +PAGES = np.array( + [ + "NextSong", + "Thumbs Up", + "Home", + "Add to Playlist", + "Roll Advert", + "Add Friend", + "Logout", + "Thumbs Down", + "Downgrade", + "Settings", + "Help", + "Upgrade", + "About", + "Save Settings", + "Error", + "Submit Downgrade", + "Submit Upgrade", + ] +) +# Approximate page mix of the real training data (NextSong ~82%). +BASE_PAGE_P = np.array( + [ + 0.80, + 0.045, + 0.037, + 0.023, + 0.016, + 0.015, + 0.012, + 0.009, + 0.007, + 0.006, + 0.005, + 0.0022, + 0.002, + 0.0012, + 0.001, + 0.0003, + 0.0006, + ] +) +SUBMIT_PAGES = ("Submit Upgrade", "Submit Downgrade") +COLUMNS = ( + "status", "gender", "firstName", "level", "lastName", "userId", "ts", "auth", "page", "sessionId", + "location", "itemInSession", "userAgent", "method", "length", "song", "artist", "registration", +) # fmt: skip +GET_PAGES = {"Home", "Help", "About", "Settings", "Downgrade", "Upgrade", "Error", "Roll Advert"} + +LOCATIONS = [ + "Dallas-Fort Worth-Arlington, TX", + "New York-Newark-Jersey City, NY-NJ-PA", + "Los Angeles-Long Beach-Anaheim, CA", + "Chicago-Naperville-Elgin, IL-IN-WI", + "Miami-Fort Lauderdale-West Palm Beach, FL", + "Seattle-Tacoma-Bellevue, WA", + "Boston-Cambridge-Newton, MA-NH", + "Phoenix-Mesa-Scottsdale, AZ", +] +USER_AGENTS = [ + '"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36"', + '"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.77.4 (KHTML, like Gecko) Version/7.0.5 Safari/537.77.4"', + '"Mozilla/5.0 (iPhone; CPU iPhone OS 7_1_2 like Mac OS X) AppleWebKit/537.51.2 (KHTML, like Gecko) Version/7.0 Mobile/11D257 Safari/9537.53"', + '"Mozilla/5.0 (X11; Linux x86_64; rv:31.0) Gecko/20100101 Firefox/31.0"', + '"Mozilla/5.0 (iPad; CPU OS 7_1_2 like Mac OS X) AppleWebKit/537.51.2 (KHTML, like Gecko) Version/7.0 Mobile/11D257 Safari/9537.53"', +] + + +def _page_probs(churner: bool) -> np.ndarray: + p = BASE_PAGE_P.copy() + if churner: # mild, realistic signal: more ads/thumbs-down/help, fewer thumbs-up + p[PAGES == "Thumbs Down"] *= 1.8 + p[PAGES == "Roll Advert"] *= 1.6 + p[PAGES == "Help"] *= 1.4 + p[PAGES == "Downgrade"] *= 1.5 + p[PAGES == "Thumbs Up"] *= 0.8 + return p / p.sum() + + +def generate_events( + n_users: int = 300, + churn_rate: float = 0.22, + seed: int = 7, + start: pd.Timestamp = START, + end: pd.Timestamp = END, +) -> pd.DataFrame: + """Generate a raw (un-normalised) event log mimicking ``train.parquet``.""" + if n_users <= 0: + raise ValueError("n_users must be positive") + rng = np.random.default_rng(seed) + span_s = (end - start).total_seconds() + cols: dict[str, list[np.ndarray]] = {c: [] for c in COLUMNS} + session_id = 1 + + for u in range(n_users): + user_id = str(1_000_000 + u * 37) + churner = rng.random() < churn_rate + gender = rng.choice(["F", "M"]) + level = "paid" if rng.random() < 0.6 else "free" + location = LOCATIONS[rng.integers(len(LOCATIONS))] + agent = USER_AGENTS[rng.integers(len(USER_AGENTS))] + registration = (start - pd.Timedelta(days=float(rng.uniform(1, 300)))).value // 1_000_000 + first_offset = rng.uniform(0, 0.3) * span_s + last_offset = rng.uniform(0.3, 1.0) * span_s if churner else span_s + sessions_per_day = rng.lognormal(mean=-0.7, sigma=0.6) + days = max((last_offset - first_offset) / 86_400, 0.5) + n_sessions = max(1, rng.poisson(sessions_per_day * days)) + starts = np.sort(rng.uniform(first_offset, last_offset, size=n_sessions)) + probs = _page_probs(churner) + cursor = -np.inf # end of the previous session: sessions never overlap + + for s_idx, s_start in enumerate(starts): + s_start = max(s_start, cursor + rng.exponential(3_600)) + n_ev = int(rng.geometric(1 / 25)) + pages = rng.choice(PAGES, size=n_ev, p=probs).astype(object) + gaps = np.where(pages == "NextSong", rng.normal(240, 50, n_ev).clip(30), rng.exponential(20, n_ev)) + offsets = s_start + np.concatenate([[0.0], np.cumsum(gaps[:-1])]) + is_last = churner and s_idx == len(starts) - 1 + if is_last: + pages = np.append(pages, ["Cancel", "Cancellation Confirmation"]) + offsets = np.append(offsets, [offsets[-1] + 30, offsets[-1] + 40]) + n = len(pages) + cursor = offsets[-1] + keep = offsets < span_s + if not keep.any(): + continue + song_ids = rng.integers(0, 400, n) + is_song = pages == "NextSong" + auth = np.full(n, "Logged In", dtype=object) + if is_last: + auth[-1] = "Cancelled" + levels = np.full(n, level, dtype=object) + changed = np.flatnonzero(np.isin(pages, ["Submit Upgrade", "Submit Downgrade"])) + for i in changed: # a submitted plan change takes effect from the next event + level = "paid" if pages[i] == "Submit Upgrade" else "free" + levels[i + 1 :] = level + + values = { + "status": np.where( + pages == "Error", 404, np.where(np.isin(pages, ["Logout", "Cancel", *SUBMIT_PAGES]), 307, 200) + ), + "gender": np.full(n, gender, dtype=object), + "firstName": np.full(n, "Synthetic", dtype=object), + "level": levels, + "lastName": np.full(n, f"User{u}", dtype=object), + "userId": np.full(n, user_id, dtype=object), + "ts": start.value // 1_000_000 + (offsets * 1000).astype(np.int64), + "auth": auth, + "page": pages, + "sessionId": np.full(n, session_id, dtype=np.int64), + "location": np.full(n, location, dtype=object), + "itemInSession": np.arange(n, dtype=np.int64), + "userAgent": np.full(n, agent, dtype=object), + "method": np.where(np.isin(pages, list(GET_PAGES)), "GET", "PUT").astype(object), + "length": np.where(is_song, rng.normal(245, 60, n).clip(30).round(5), np.nan), + "song": np.where(is_song, np.char.add("Song ", song_ids.astype(str)), None), + "artist": np.where(is_song, np.char.add("Artist ", (song_ids // 8).astype(str)), None), + "registration": np.full(n, registration, dtype=np.int64), + } + for name, arr in values.items(): + cols[name].append(arr[keep]) + session_id += 1 + + df = pd.DataFrame({name: np.concatenate(parts) for name, parts in cols.items()}) + df["time"] = pd.to_datetime(df["ts"], unit="ms").astype("datetime64[us]") + df["registration"] = pd.to_datetime(df["registration"], unit="ms").astype("datetime64[us]") + return df.sort_values(["ts", "userId"], kind="mergesort").reset_index(drop=True) + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--users", type=int, default=300) + parser.add_argument("--churn-rate", type=float, default=0.22) + parser.add_argument("--seed", type=int, default=7) + parser.add_argument("--out", type=Path, default=Path("data/sample_events.parquet")) + args = parser.parse_args(argv) + + df = generate_events(args.users, args.churn_rate, args.seed) + args.out.parent.mkdir(parents=True, exist_ok=True) + if args.out.suffix == ".csv": + df.to_csv(args.out, index=False) + else: + df.to_parquet(args.out, index=False) + print(f"Wrote {len(df):,} events for {df['userId'].nunique()} users to {args.out}") + + +if __name__ == "__main__": + main() diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..fb2de3c --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import pandas as pd +import pytest + +from churn_app.data import normalize_events +from churn_app.sample_data import generate_events + +WINDOWS_UA = '"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0 Safari/537.36"' +IPHONE_UA = '"Mozilla/5.0 (iPhone; CPU iPhone OS 7_1_2 like Mac OS X) AppleWebKit/537.51.2 Mobile/11D257"' +MAC_UA = '"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit/537.77.4 Safari/537.77.4"' + +# (userId, time, page, sessionId, level, gender, auth, status, location, userAgent, length) +_ROWS = [ + # user 1: paid, never cancels, two sessions + ("1", "2018-10-01 10:00:00", "NextSong", 11, "paid", "M", "Logged In", 200, "Dallas-Fort Worth-Arlington, TX", WINDOWS_UA, 200.0), + ("1", "2018-10-01 10:03:20", "Thumbs Up", 11, "paid", "M", "Logged In", 307, "Dallas-Fort Worth-Arlington, TX", WINDOWS_UA, None), + ("1", "2018-10-01 10:04:00", "Home", 11, "paid", "M", "Logged In", 200, "Dallas-Fort Worth-Arlington, TX", WINDOWS_UA, None), + ("1", "2018-10-05 09:00:00", "Thumbs Down", 12, "paid", "M", "Logged In", 307, "Dallas-Fort Worth-Arlington, TX", WINDOWS_UA, None), + ("1", "2018-10-05 09:01:00", "NextSong", 12, "paid", "M", "Logged In", 200, "Dallas-Fort Worth-Arlington, TX", WINDOWS_UA, 160.0), + # user 2: free, cancels on 2018-10-10 + ("2", "2018-10-02 20:00:00", "NextSong", 21, "free", "F", "Logged In", 200, "New York-Newark-Jersey City, NY-NJ-PA", IPHONE_UA, 180.0), + ("2", "2018-10-02 20:03:00", "Roll Advert", 21, "free", "F", "Logged In", 200, "New York-Newark-Jersey City, NY-NJ-PA", IPHONE_UA, None), + ("2", "2018-10-10 08:00:00", "Error", 22, "free", "F", "Logged In", 404, "New York-Newark-Jersey City, NY-NJ-PA", IPHONE_UA, None), + ("2", "2018-10-10 08:01:00", "Cancel", 22, "free", "F", "Logged In", 307, "New York-Newark-Jersey City, NY-NJ-PA", IPHONE_UA, None), + ("2", "2018-10-10 08:01:10", "Cancellation Confirmation", 22, "free", "F", "Cancelled", 200, "New York-Newark-Jersey City, NY-NJ-PA", IPHONE_UA, None), + # logged-out traffic without a user id: must be dropped + ("", "2018-10-03 12:00:00", "Home", 99, "free", None, "Logged Out", 200, None, None, None), + # user 3: first seen late (after typical cutoffs) + ("3", "2018-11-15 23:59:59", "NextSong", 31, "paid", "M", "Logged In", 200, "Seattle-Tacoma-Bellevue, WA", MAC_UA, 240.0), + ("3", "2018-11-19 07:00:00", "Help", 32, "paid", "M", "Logged In", 200, "Seattle-Tacoma-Bellevue, WA", MAC_UA, None), +] # fmt: skip +_COLUMNS = [ + "userId", + "time", + "page", + "sessionId", + "level", + "gender", + "auth", + "status", + "location", + "userAgent", + "length", +] + + +@pytest.fixture +def raw_events() -> pd.DataFrame: + """Small hand-written raw log in the Kaggle schema (incl. PII + ts columns).""" + df = pd.DataFrame(_ROWS, columns=_COLUMNS) + df["time"] = pd.to_datetime(df["time"]) + df["ts"] = df["time"].astype("datetime64[ms]").astype("int64") + df["registration"] = pd.Timestamp("2018-09-01") + df["firstName"] = "Ada" + df["lastName"] = "Lovelace" + return df + + +@pytest.fixture +def events(raw_events: pd.DataFrame) -> pd.DataFrame: + return normalize_events(raw_events) + + +@pytest.fixture(scope="session") +def sample_raw() -> pd.DataFrame: + return generate_events(n_users=250, seed=11) + + +@pytest.fixture(scope="session") +def sample_events(sample_raw: pd.DataFrame) -> pd.DataFrame: + return normalize_events(sample_raw) diff --git a/tests/test_app.py b/tests/test_app.py new file mode 100644 index 0000000..4dcdcb2 --- /dev/null +++ b/tests/test_app.py @@ -0,0 +1,57 @@ +"""End-to-end smoke tests that execute the Streamlit script headlessly.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest +from streamlit.testing.v1 import AppTest + +APP = str(Path(__file__).resolve().parents[1] / "app" / "streamlit_app.py") +pytestmark = pytest.mark.slow + + +@pytest.fixture +def app(monkeypatch, tmp_path) -> AppTest: + monkeypatch.setenv("DATA_DIR", str(tmp_path)) # empty: sample data is the default source + at = AppTest.from_file(APP, default_timeout=120) + at.run() + return at + + +def test_app_renders_with_sample_data(app: AppTest): + assert not app.exception + assert app.title[0].value.startswith("📉 Churn Explorer") + labels = [m.label for m in app.metric] + assert {"Events", "Users", "Sessions"} <= set(labels) + assert len(app.tabs) == 6 + + +def test_app_filters_update_metrics(app: AppTest): + events_before = next(m.value for m in app.metric if m.label == "Events") + level = next(w for w in app.sidebar.multiselect if w.label == "Subscription level") + level.select("paid").run() + assert not app.exception + events_after = next(m.value for m in app.metric if m.label == "Events") + assert int(events_after.replace(",", "")) < int(events_before.replace(",", "")) + + +def test_app_trains_model(app: AppTest): + train = next(b for b in app.button if b.label == "Train model") + train.click().run() + assert not app.exception + labels = {m.label for m in app.metric} + assert {"Balanced accuracy", "ROC AUC", "Threshold"} <= labels + + +def test_app_lists_files_in_data_dir(monkeypatch, tmp_path): + from churn_app.sample_data import generate_events + + generate_events(60, seed=5).to_parquet(tmp_path / "train.parquet") + monkeypatch.setenv("DATA_DIR", str(tmp_path)) + at = AppTest.from_file(APP, default_timeout=120) + at.run() + assert not at.exception + assert at.sidebar.radio[0].value.startswith("File in") + users = next(m.value for m in at.metric if m.label == "Users") + assert users == "60" diff --git a/tests/test_data_loading.py b/tests/test_data_loading.py new file mode 100644 index 0000000..2bdc1cb --- /dev/null +++ b/tests/test_data_loading.py @@ -0,0 +1,228 @@ +"""Tests for reading, validating and normalising event logs.""" + +from __future__ import annotations + +import datetime as dt +import io +from pathlib import Path + +import pandas as pd +import pytest + +from churn_app.data import ( + SchemaError, + detect_device, + infer_format, + load_events, + normalize_events, + parse_state, + read_events, + user_bucket, + validate_columns, +) + + +# --------------------------------------------------------------------------- # +# infer_format +# --------------------------------------------------------------------------- # +@pytest.mark.parametrize( + ("source", "fmt", "expected"), + [ + ("train.parquet", None, "parquet"), + (Path("dir/TRAIN.PARQUET"), None, "parquet"), + ("events.pq", None, "parquet"), + ("events.csv", None, "csv"), + (b"bytes", ".csv", "csv"), + ("mislabelled.csv", "parquet", "parquet"), + ], +) +def test_infer_format(source, fmt, expected): + assert infer_format(source, fmt) == expected + + +def test_infer_format_uses_file_like_name(): + buf = io.BytesIO(b"") + buf.name = "upload.parquet" + assert infer_format(buf) == "parquet" + + +@pytest.mark.parametrize("source", ["notes.txt", "no_suffix", io.BytesIO(b"")]) +def test_infer_format_rejects_unknown(source): + with pytest.raises(ValueError, match="Unsupported"): + infer_format(source) + + +# --------------------------------------------------------------------------- # +# read_events / load_events +# --------------------------------------------------------------------------- # +@pytest.fixture(params=["parquet", "csv"]) +def saved_events(request, tmp_path: Path, raw_events: pd.DataFrame) -> Path: + path = tmp_path / f"events.{request.param}" + if request.param == "parquet": + raw_events.to_parquet(path, index=False) + else: + raw_events.to_csv(path, index=False) + return path + + +def test_read_events_from_path_drops_pii(saved_events: Path, raw_events: pd.DataFrame): + df = read_events(saved_events) + assert len(df) == len(raw_events) + assert "firstName" not in df.columns + assert "lastName" not in df.columns + + +def test_read_events_from_bytes_and_file_like(saved_events: Path): + content = saved_events.read_bytes() + fmt = saved_events.suffix + from_bytes = read_events(content, fmt=fmt) + buf = io.BytesIO(content) + buf.name = saved_events.name + from_buffer = read_events(buf) + assert len(from_bytes) == len(from_buffer) == 13 + + +def test_load_events_round_trip_matches_in_memory_normalisation(saved_events: Path, events: pd.DataFrame): + loaded = load_events(saved_events) + cols = ["userId", "page", "sessionId", "level", "device", "state"] + pd.testing.assert_frame_equal( + loaded[cols].astype(str).reset_index(drop=True), events[cols].astype(str).reset_index(drop=True) + ) + assert (loaded["time"].to_numpy() == events["time"].to_numpy()).all() + + +def test_read_events_derives_time_from_ts(tmp_path: Path, raw_events: pd.DataFrame): + path = tmp_path / "ts_only.parquet" + raw_events.drop(columns=["time"]).to_parquet(path) + df = read_events(path) + assert (df["time"].to_numpy() == raw_events["time"].to_numpy()).all() + + +def test_read_events_date_range_is_inclusive_by_day(saved_events: Path): + df = read_events(saved_events, start=dt.date(2018, 10, 2), end=dt.date(2018, 10, 10)) + times = pd.to_datetime(df["time"]) + assert times.min() == pd.Timestamp("2018-10-02 20:00:00") + # the whole of the end day is included + assert times.max() == pd.Timestamp("2018-10-10 08:01:10") + assert len(df) == 8 + + +def test_read_events_missing_required_parquet_columns(tmp_path: Path, raw_events: pd.DataFrame): + path = tmp_path / "bad.parquet" + raw_events.drop(columns=["page"]).to_parquet(path) + with pytest.raises(SchemaError, match="page"): + read_events(path) + + +def test_load_events_missing_time_and_ts(tmp_path: Path, raw_events: pd.DataFrame): + path = tmp_path / "no_time.csv" + raw_events.drop(columns=["time", "ts"]).to_csv(path, index=False) + with pytest.raises(SchemaError, match="time"): + load_events(path) + + +@pytest.mark.parametrize("fraction", [0, -0.1, 1.5]) +def test_read_events_rejects_bad_fraction(saved_events: Path, fraction: float): + with pytest.raises(ValueError, match="user_fraction"): + read_events(saved_events, user_fraction=fraction) + + +def test_user_sampling_is_deterministic_and_keeps_whole_users(tmp_path: Path, sample_raw: pd.DataFrame): + path = tmp_path / "sample.parquet" + sample_raw.to_parquet(path) + a = read_events(path, user_fraction=0.3, batch_rows=10_000) # many batches + b = read_events(path, user_fraction=0.3, batch_rows=10**9) # one batch + assert set(a["userId"]) == set(b["userId"]) + kept = set(a["userId"]) + assert 0.15 < len(kept) / sample_raw["userId"].nunique() < 0.45 + # every event of a sampled user is kept + assert len(a) == sample_raw["userId"].isin(kept).sum() + + +def test_user_bucket_is_stable(): + ids = pd.Series(["1749042", "1465194", "1749042"]) + buckets = user_bucket(ids) + assert buckets[0] == buckets[2] + assert ((buckets >= 0) & (buckets < 10_000)).all() + assert (user_bucket(ids) == buckets).all() + + +# --------------------------------------------------------------------------- # +# validation / normalisation +# --------------------------------------------------------------------------- # +def test_validate_columns_lists_everything_missing(): + with pytest.raises(SchemaError) as exc: + validate_columns(["userId", "page"]) + message = str(exc.value) + assert "sessionId" in message + assert "level" in message + assert "time (or ts)" in message + + +def test_validate_columns_accepts_ts_instead_of_time(): + validate_columns(["userId", "sessionId", "page", "level", "ts"]) + + +def test_normalize_drops_anonymous_and_unparseable_rows(raw_events: pd.DataFrame): + raw = raw_events.copy() + raw["time"] = raw["time"].astype(str) + raw.loc[0, "time"] = "not a date" + out = normalize_events(raw) + assert "" not in set(out["userId"]) + assert len(out) == len(raw) - 2 + + +def test_normalize_types_sorting_and_derived_columns(events: pd.DataFrame): + assert pd.api.types.is_datetime64_any_dtype(events["time"]) + assert pd.api.types.is_datetime64_any_dtype(events["registration"]) + for col in ("page", "level", "auth", "gender", "device", "state"): + assert isinstance(events[col].dtype, pd.CategoricalDtype), col + assert "userAgent" not in events.columns + assert list(events["userId"].unique()) == ["1", "2", "3"] + assert events.groupby("userId")["time"].apply(lambda t: t.is_monotonic_increasing).all() + by_user = events.groupby("userId", observed=True)[["device", "state"]].first().astype(str) + assert by_user.loc["1"].tolist() == ["Windows", "TX"] + assert by_user.loc["2"].tolist() == ["iPhone", "NY"] + assert by_user.loc["3"].tolist() == ["Mac", "WA"] + + +def test_normalize_handles_timezone_aware_and_epoch_times(raw_events: pd.DataFrame): + aware = raw_events.assign(time=raw_events["time"].dt.tz_localize("UTC")) + epoch = raw_events.drop(columns=["time"]) + a, b = normalize_events(aware), normalize_events(epoch) + assert a["time"].dt.tz is None + assert (a["time"].to_numpy() == b["time"].to_numpy()).all() + + +def test_normalize_turns_float_user_ids_back_into_integer_strings(raw_events: pd.DataFrame): + raw = raw_events.assign(userId=pd.to_numeric(raw_events["userId"].replace("", None))) + assert pd.api.types.is_float_dtype(raw["userId"]) + assert list(normalize_events(raw)["userId"].unique()) == ["1", "2", "3"] + + +def test_normalize_does_not_mutate_input(raw_events: pd.DataFrame): + before = raw_events.copy() + normalize_events(raw_events) + pd.testing.assert_frame_equal(raw_events, before) + + +@pytest.mark.parametrize( + ("agent", "expected"), + [ + ("Mozilla/5.0 (iPhone; CPU iPhone OS 7_1_2 like Mac OS X)", "iPhone"), + ("Mozilla/5.0 (iPad; CPU OS 7_1_2 like Mac OS X)", "iPad"), + ("Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_4)", "Mac"), + ("Mozilla/5.0 (Windows NT 6.1; WOW64)", "Windows"), + ("Mozilla/5.0 (Linux; Android 4.4)", "Android"), + ("Mozilla/5.0 (X11; Ubuntu; Linux x86_64)", "Linux"), + ("curl/7.0", "Other"), + (None, "Other"), + ], +) +def test_detect_device(agent, expected): + assert detect_device(pd.Series([agent], dtype="object")).iloc[0] == expected + + +def test_parse_state(): + locations = pd.Series(["Dallas-Fort Worth-Arlington, TX", "New York-Newark-Jersey City, NY-NJ-PA", None]) + assert parse_state(locations).tolist() == ["TX", "NY", "Unknown"] diff --git a/tests/test_features.py b/tests/test_features.py new file mode 100644 index 0000000..9003f10 --- /dev/null +++ b/tests/test_features.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +import pandas as pd +import pytest + +from churn_app.data import churn_labels +from churn_app.features import ( + build_user_features, + churn_rate_by, + daily_activity, + page_distribution, + user_timeline, +) + + +def test_user_features_counts(events): + f = build_user_features(events) + assert list(f.index) == ["1", "2", "3"] + u1 = f.loc["1"] + assert u1["n_events"] == 5 + assert u1["n_sessions"] == 2 + assert u1["active_days"] == 2 + assert u1["songs_played"] == 2 + assert u1["thumbs_up"] == 1 + assert u1["thumbs_down_ratio"] == pytest.approx(0.5) + assert u1["is_paid_now"] == 1 + assert u1["is_male"] == 1 + assert u1["listening_hours"] == pytest.approx(360 / 3600) + assert f.loc["2", "http_404_rate"] == pytest.approx(1 / 3) + assert (f.dtypes == "float64").all() + + +def test_user_features_never_use_cancellation_events(events): + f = build_user_features(events) + # user 2 has 5 events, 2 of which are Cancel / Cancellation Confirmation + assert f.loc["2", "n_events"] == 3 + assert f.loc["2", "adverts"] == 1 + assert f.loc["2", "errors"] == 1 + + +def test_user_features_respect_reference_time(events): + f = build_user_features(events, reference=pd.Timestamp("2018-10-05")) + assert set(f.index) == {"1", "2"} # user 3 has no events yet + assert f.loc["1", "n_events"] == 3 + assert f.loc["1", "days_since_last_event"] == pytest.approx( + (pd.Timestamp("2018-10-05") - pd.Timestamp("2018-10-01 10:04")).total_seconds() / 86_400 + ) + assert f.loc["1", "tenure_days"] == pytest.approx(34.0) + + +def test_user_features_empty_when_no_events_before_reference(events): + f = build_user_features(events, reference=pd.Timestamp("2018-01-01")) + assert f.empty + assert f.index.name == "userId" + + +def test_features_are_identical_with_or_without_future_events(sample_events): + cutoff = pd.Timestamp("2018-11-01") + full = build_user_features(sample_events, cutoff) + past_only = build_user_features(sample_events[sample_events["time"] < cutoff], cutoff) + pd.testing.assert_frame_equal(full, past_only) + + +def test_daily_activity(events): + daily = daily_activity(events) + assert daily["events"].sum() == len(events) + row = daily.set_index("day").loc[pd.Timestamp("2018-10-10")] + assert row["events"] == 3 + assert row["active_users"] == 1 + assert daily_activity(events.iloc[0:0]).empty + + +def test_page_distribution(events): + pages = page_distribution(events) + assert pages.iloc[0].tolist()[:2] == ["NextSong", 4] + assert pages["share"].sum() == pytest.approx(1.0) + + +def test_churn_rate_by_binary_and_quantiles(sample_events): + features = build_user_features(sample_events) + labels = churn_labels(sample_events) + binary = churn_rate_by(features, labels, "is_paid_now") + assert set(binary["bucket"]) == {"0", "1"} + assert binary["users"].sum() == len(features) + buckets = churn_rate_by(features, labels, "n_events", bins=4) + assert len(buckets) == 4 + assert buckets["churn_rate"].between(0, 1).all() + + +def test_churn_rate_by_without_overlap(events): + labels = pd.DataFrame({"userId": ["x"], "churn": [1]}) + assert churn_rate_by(build_user_features(events), labels, "n_events").empty + + +def test_user_timeline(events): + tl = user_timeline(events, "1") + assert tl["events"].sum() == 5 + assert set(tl["page"]) == {"NextSong", "Thumbs Up", "Home", "Thumbs Down"} + assert user_timeline(events, "nope").empty diff --git a/tests/test_filtering.py b/tests/test_filtering.py new file mode 100644 index 0000000..bd336d3 --- /dev/null +++ b/tests/test_filtering.py @@ -0,0 +1,140 @@ +"""Tests for EventFilter / filter_events / filter_options / churn_labels.""" + +from __future__ import annotations + +import datetime as dt + +import pandas as pd +import pytest + +from churn_app.data import EventFilter, churn_labels, filter_events, filter_options + + +def test_empty_filter_keeps_everything(events: pd.DataFrame): + out = filter_events(events, EventFilter()) + assert len(out) == len(events) + assert out.index.equals(pd.RangeIndex(len(events))) + + +def test_filter_does_not_mutate_input(events: pd.DataFrame): + before = events.copy() + filter_events(events, EventFilter(levels=("paid",), exclude_leakage=True)) + pd.testing.assert_frame_equal(events, before) + + +def test_start_after_end_is_rejected(): + with pytest.raises(ValueError, match="must not be after"): + EventFilter(start=dt.date(2018, 11, 1), end=dt.date(2018, 10, 1)) + + +@pytest.mark.parametrize( + ("start", "end", "expected_rows"), + [ + (dt.date(2018, 10, 1), dt.date(2018, 10, 1), 3), # a single day, fully included + (dt.date(2018, 10, 5), None, 7), + (None, dt.date(2018, 10, 4), 5), + (dt.date(2018, 11, 15), dt.date(2018, 11, 15), 1), # 23:59:59 still on the end day + (dt.date(2018, 12, 1), None, 0), + ], +) +def test_date_filter_is_inclusive_by_calendar_day(events, start, end, expected_rows): + assert len(filter_events(events, EventFilter(start=start, end=end))) == expected_rows + + +@pytest.mark.parametrize( + ("flt", "expected_users"), + [ + (EventFilter(levels=("free",)), {"2"}), + (EventFilter(genders=("M",)), {"1", "3"}), + (EventFilter(devices=("iPhone", "Mac")), {"2", "3"}), + (EventFilter(states=("TX",)), {"1"}), + (EventFilter(pages=("Help",)), {"3"}), + (EventFilter(user_ids=("1", "3")), {"1", "3"}), + (EventFilter(levels=("paid",), states=("WA",)), {"3"}), # dimensions combine with AND + (EventFilter(levels=("free",), states=("TX",)), set()), + ], +) +def test_categorical_filters(events, flt, expected_users): + assert set(filter_events(events, flt)["userId"]) == expected_users + + +def test_filter_values_within_a_dimension_are_ored(events): + out = filter_events(events, EventFilter(pages=("NextSong", "Help"))) + assert set(out["page"].astype(str)) == {"NextSong", "Help"} + assert len(out) == 5 + + +def test_exclude_leakage_drops_cancellation_rows(events): + out = filter_events(events, EventFilter(exclude_leakage=True)) + assert not out["page"].astype(str).isin(["Cancel", "Cancellation Confirmation"]).any() + assert not (out["auth"].astype(str) == "Cancelled").any() + assert len(out) == len(events) - 2 + + +def test_filter_on_missing_column_raises(events): + with pytest.raises(KeyError, match="gender"): + filter_events(events.drop(columns=["gender"]), EventFilter(genders=("F",))) + + +def test_filter_options(events): + opts = filter_options(events) + assert opts["min_date"] == dt.date(2018, 10, 1) + assert opts["max_date"] == dt.date(2018, 11, 19) + assert opts["level"] == ["free", "paid"] + assert opts["device"] == ["Mac", "Windows", "iPhone"] + assert "Cancellation Confirmation" in opts["page"] + + +def test_filter_options_on_empty_and_partial_frames(events): + opts = filter_options(events.iloc[0:0].drop(columns=["state"])) + assert opts["min_date"] is None + assert opts["state"] == [] + + +# --------------------------------------------------------------------------- # +# churn_labels +# --------------------------------------------------------------------------- # +def _as_dict(labels: pd.DataFrame) -> dict[str, int]: + return dict(zip(labels["userId"], labels["churn"], strict=True)) + + +def test_labels_ever_mode(events): + labels = churn_labels(events) + assert _as_dict(labels) == {"1": 0, "2": 1, "3": 0} + assert labels.loc[labels["userId"] == "2", "churn_time"].iloc[0] == pd.Timestamp("2018-10-10 08:01:10") + assert labels.loc[labels["userId"] == "1", "churn_time"].isna().all() + + +def test_labels_horizon_mode_positive_inside_window(events): + # user 3 is first seen after the cutoff and is therefore excluded + labels = churn_labels(events, cutoff=dt.date(2018, 10, 8), horizon_days=10) + assert _as_dict(labels) == {"1": 0, "2": 1} + + +def test_labels_horizon_mode_negative_outside_window(events): + labels = churn_labels(events, cutoff=dt.date(2018, 10, 3), horizon_days=5) # window ends 10-08 + assert _as_dict(labels) == {"1": 0, "2": 0} + + +def test_labels_horizon_mode_drops_users_already_churned(events): + labels = churn_labels(events, cutoff=pd.Timestamp("2018-10-11"), horizon_days=10) + assert _as_dict(labels) == {"1": 0} + + +def test_labels_horizon_window_end_is_inclusive(events): + churn_at = pd.Timestamp("2018-10-10 08:01:10") + labels = churn_labels(events, cutoff=churn_at - pd.Timedelta(days=2), horizon_days=2) + assert _as_dict(labels)["2"] == 1 + + +@pytest.mark.parametrize("horizon", [0, -3]) +def test_labels_reject_non_positive_horizon(events, horizon): + with pytest.raises(ValueError, match="horizon_days"): + churn_labels(events, cutoff=dt.date(2018, 10, 8), horizon_days=horizon) + + +def test_labels_on_sample_data_match_cancellation_events(sample_events): + labels = churn_labels(sample_events) + cancelled = set(sample_events.loc[sample_events["page"] == "Cancellation Confirmation", "userId"]) + assert set(labels.loc[labels["churn"] == 1, "userId"]) == cancelled + assert labels["userId"].is_unique diff --git a/tests/test_model.py b/tests/test_model.py new file mode 100644 index 0000000..9e0dc3b --- /dev/null +++ b/tests/test_model.py @@ -0,0 +1,78 @@ +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +from churn_app.data import churn_labels +from churn_app.features import build_user_features +from churn_app.model import best_threshold, make_model, score_users, train_churn_model + + +@pytest.fixture(scope="module") +def horizon_data(sample_events): + cutoff = pd.Timestamp("2018-11-01") + return build_user_features(sample_events, cutoff), churn_labels(sample_events, cutoff, 10) + + +def test_best_threshold_separable(): + y = np.array([0, 0, 0, 1, 1]) + proba = np.array([0.1, 0.2, 0.3, 0.7, 0.9]) + threshold, score = best_threshold(y, proba) + assert score == 1.0 + assert 0.3 < threshold <= 0.7 + assert threshold == pytest.approx(0.5) # ties broken towards 0.5 + + +def test_best_threshold_custom_grid(): + y = np.array([0, 1, 1, 0]) + proba = np.array([0.2, 0.8, 0.6, 0.4]) + assert best_threshold(y, proba, grid=np.array([0.5, 0.9])) == (0.5, 1.0) + + +@pytest.mark.parametrize("model_type", ["gradient_boosting", "logistic_regression"]) +def test_train_churn_model(horizon_data, model_type): + features, labels = horizon_data + result = train_churn_model(features, labels, model_type, importance_repeats=2) + m = result.metrics + assert 0.5 <= m["balanced_accuracy"] <= 1 + assert 0.5 <= m["roc_auc"] <= 1 + assert m["n_train"] + m["n_val"] == len(features.join(labels.set_index("userId"), how="inner")) + assert result.confusion.to_numpy().sum() == m["n_val"] + assert set(result.importance["feature"]) == set(features.columns) + assert result.validation["pred"].isin([0, 1]).all() + + +def test_training_is_reproducible(horizon_data): + features, labels = horizon_data + a = train_churn_model(features, labels, seed=3, importance_repeats=2) + b = train_churn_model(features, labels, seed=3, importance_repeats=2) + assert a.metrics == b.metrics + pd.testing.assert_frame_equal(a.validation, b.validation) + + +def test_score_users_sorted_and_aligned(horizon_data): + features, labels = horizon_data + result = train_churn_model(features, labels, "logistic_regression", importance_repeats=1) + scores = score_users(result, features) + assert set(scores.index) == set(features.index) + assert scores["churn_probability"].is_monotonic_decreasing + assert (scores["predicted_churn"] == (scores["churn_probability"] >= result.threshold)).all() + + +def test_train_rejects_single_class(horizon_data): + features, labels = horizon_data + with pytest.raises(ValueError, match="at least 4"): + train_churn_model(features, labels.assign(churn=0)) + + +def test_train_rejects_no_overlap(horizon_data): + features, _ = horizon_data + labels = pd.DataFrame({"userId": ["nobody"], "churn": [1]}) + with pytest.raises(ValueError, match="No overlap"): + train_churn_model(features, labels) + + +def test_unknown_model_type(): + with pytest.raises(ValueError, match="Unknown model_type"): + make_model("svm") # type: ignore[arg-type] diff --git a/tests/test_sample_data.py b/tests/test_sample_data.py new file mode 100644 index 0000000..61e0a9c --- /dev/null +++ b/tests/test_sample_data.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from pathlib import Path + +import pandas as pd +import pytest + +from churn_app.data import load_events +from churn_app.sample_data import generate_events, main + +KAGGLE_COLUMNS = { + "status", "gender", "firstName", "level", "lastName", "userId", "ts", "auth", "page", "sessionId", + "location", "itemInSession", "userAgent", "method", "length", "song", "artist", "time", "registration", +} # fmt: skip + + +def test_schema_matches_kaggle(sample_raw): + assert set(sample_raw.columns) == KAGGLE_COLUMNS + assert str(sample_raw["time"].dtype) == "datetime64[us]" + assert sample_raw["time"].min() >= pd.Timestamp("2018-10-01") + assert sample_raw["time"].max() < pd.Timestamp("2018-11-20") + + +def test_generation_is_deterministic(): + pd.testing.assert_frame_equal(generate_events(40, seed=1), generate_events(40, seed=1)) + assert not generate_events(40, seed=1).equals(generate_events(40, seed=2)) + + +def test_cancellation_is_each_churners_last_event(sample_raw): + ordered = sample_raw.sort_values(["userId", "ts"], kind="mergesort") + last = ordered.groupby("userId").tail(1) + churners = set(sample_raw.loc[sample_raw["page"] == "Cancellation Confirmation", "userId"]) + assert churners, "sample should contain churners" + assert set(last.loc[last["page"] == "Cancellation Confirmation", "userId"]) == churners + assert (sample_raw.loc[sample_raw["page"] == "Cancellation Confirmation", "auth"] == "Cancelled").all() + + +def test_rejects_non_positive_users(): + with pytest.raises(ValueError): + generate_events(0) + + +@pytest.mark.parametrize("suffix", [".parquet", ".csv"]) +def test_cli_writes_loadable_file(tmp_path: Path, capsys, suffix): + out = tmp_path / f"nested/sample{suffix}" + main(["--users", "30", "--seed", "3", "--out", str(out)]) + assert "Wrote" in capsys.readouterr().out + assert load_events(out)["userId"].nunique() == 30 diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..762c1c7 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1058 @@ +version = 1 +revision = 3 +requires-python = ">=3.12, <3.14" +resolution-markers = [ + "sys_platform == 'win32'", + "sys_platform == 'emscripten'", + "sys_platform != 'emscripten' and sys_platform != 'win32'", +] + +[[package]] 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