diff --git a/examples/walkthrough.ipynb b/examples/walkthrough.ipynb new file mode 100644 index 0000000..245a181 --- /dev/null +++ b/examples/walkthrough.ipynb @@ -0,0 +1,618 @@ +{ + "cells": [ + { + "attachments": { + "ci-proto-banner.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "14382bc5", + "metadata": {}, + "source": [ + "\n", + "\n", + "In this notebook, we’ll use ci-proto to forecast bike rentals. We’ll add calendar covariates\n", + "to improve the forecasts, explore their uncertainty, and see how the model handles missing data.\n", + "\n", + "Run the cells in order, as later examples reuse the model, data and helpers from earlier ones.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e82fef8c", + "metadata": {}, + "outputs": [], + "source": [ + "# In a fresh environment, install the package and the libraries this notebook uses:\n", + "# %pip install continuousintelligence matplotlib scikit-learn pyarrow\n", + "\n", + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import torch\n", + "\n", + "import continuousintelligence as ci\n", + "\n", + "warnings.filterwarnings(\"ignore\", message=\".*symlinks.*\") # Hugging Face cache notice on Windows\n", + "warnings.filterwarnings(\"ignore\", message=\"IProgress not found\") # tqdm without ipywidgets\n", + "warnings.filterwarnings(\"ignore\", message=\"Mismatch dtype between input and weight\") # CPU bfloat16\n", + "CI_NAVY = \"#173446\"\n", + "CI_TEXT = \"#FAFBFB\"\n", + "CI_MUTED = \"#96A3A9\"\n", + "CI_TEAL = \"#65BBAA\"\n", + "CI_HIGHLIGHT = \"#E34F5A\"\n", + "CI_LINE = \"#324C5C\" # 12% CI_TEXT over CI_NAVY\n", + "\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.figsize\": (12, 3.6),\n", + " \"figure.dpi\": 600,\n", + " \"savefig.dpi\": 600,\n", + " \"figure.facecolor\": CI_NAVY,\n", + " \"axes.facecolor\": CI_NAVY,\n", + " \"savefig.facecolor\": CI_NAVY,\n", + " \"text.color\": CI_TEXT,\n", + " \"axes.labelcolor\": CI_TEXT,\n", + " \"axes.titlecolor\": CI_TEXT,\n", + " \"axes.edgecolor\": CI_LINE,\n", + " \"xtick.color\": CI_MUTED,\n", + " \"ytick.color\": CI_MUTED,\n", + " \"grid.color\": CI_LINE,\n", + " \"legend.labelcolor\": CI_TEXT,\n", + " \"axes.prop_cycle\": plt.cycler(\n", + " color=[\n", + " CI_TEAL,\n", + " CI_HIGHLIGHT,\n", + " CI_TEXT,\n", + " CI_MUTED,\n", + " \"#A3D6CC\",\n", + " \"#EE959C\",\n", + " \"#3E786F\",\n", + " \"#AC3D4A\",\n", + " ]\n", + " ),\n", + " \"axes.spines.top\": True,\n", + " \"axes.spines.right\": True,\n", + " \"axes.grid\": False,\n", + " \"grid.alpha\": 1.0,\n", + " \"legend.frameon\": False,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "999ee048", + "metadata": {}, + "source": [ + "## Load the model\n", + "\n", + "`ci.load_model` downloads the weights and calibration settings from the\n", + "Hugging Face Hub on first use, then reuses the local cache. `device=\"auto\"` picks a CUDA GPU when\n", + "one is available and falls back to CPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d787ef6", + "metadata": {}, + "outputs": [], + "source": [ + "model = ci.load_model(\"ContinuousIntelligence/ci-proto\", device=\"auto\")\n", + "levels = model.quantiles # the probability level of each output quantile\n", + "\n", + "print(f\"{len(levels)} quantile levels: {levels[0]:.2f}, {levels[1]:.2f}, …, {levels[-1]:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0f594d25", + "metadata": {}, + "source": [ + "A few helpers keep the rest of the notebook short:\n", + "\n", + "- `qi(level)` finds the index of a quantile level, such as `0.9`.\n", + "- `scores` reports three accuracy measures for one forecast:\n", + " - **MAE**: mean absolute error of the median forecast, in the target's own units.\n", + " - **WQL**: weighted quantile loss over all 9 quantiles, a single number for probabilistic accuracy. Lower is better.\n", + " - **80% coverage**: the share of actual values that fall inside the 10–90% band. A calibrated forecast is close to 0.8.\n", + "- `plot_forecast` draws history, the 10–90% and 25–75% bands, the median and the actual values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c480f42", + "metadata": {}, + "outputs": [], + "source": [ + "def qi(level: float) -> int:\n", + " # Index of the quantile level closest to `level`.\n", + " return int((levels - level).abs().argmin())\n", + "\n", + "\n", + "def scores(prediction: torch.Tensor, truth) -> dict[str, float]:\n", + " # Accuracy of one series' forecast: prediction [quantiles, horizon], truth [horizon].\n", + " truth = torch.tensor(np.array(truth, dtype=np.float32))\n", + " seen = ~torch.isnan(truth)\n", + " prediction, truth = prediction[:, seen], truth[seen]\n", + " error = truth - prediction\n", + " pinball = torch.maximum(levels[:, None] * error, (levels[:, None] - 1) * error)\n", + " inside = (truth >= prediction[qi(0.1)]) & (truth <= prediction[qi(0.9)])\n", + " return {\n", + " \"MAE\": (truth - prediction[qi(0.5)]).abs().mean().item(),\n", + " \"WQL\": (2 * pinball.sum(1).mean() / truth.abs().sum()).item(),\n", + " \"80% coverage\": inside.float().mean().item(),\n", + " }\n", + "\n", + "\n", + "def plot_forecast(\n", + " ax, past, prediction, future_index, truth=None, color=CI_TEAL, label=\"Median\", bands=True\n", + "):\n", + " # past: Series of recent history; prediction: [quantiles, horizon] for one series.\n", + " if past is not None:\n", + " ax.plot(past.index, past.to_numpy(), color=CI_MUTED, lw=1, label=\"History\")\n", + " for low, high, alpha in ((0.1, 0.9, 0.18), (0.25, 0.75, 0.32)) if bands else ():\n", + " ax.fill_between(\n", + " future_index,\n", + " prediction[qi(low)],\n", + " prediction[qi(high)],\n", + " color=color,\n", + " alpha=alpha,\n", + " lw=0,\n", + " label=f\"{low:.0%}–{high:.0%}\",\n", + " )\n", + " ax.plot(future_index, prediction[qi(0.5)], color=color, lw=1.6, label=label)\n", + " if truth is not None:\n", + " ax.plot(future_index, np.asarray(truth), color=CI_TEXT, lw=1, ls=\"--\", label=\"Actual\")\n", + " ax.legend(loc=\"upper left\", ncol=5, fontsize=8)" + ] + }, + { + "cell_type": "markdown", + "id": "541b6822", + "metadata": {}, + "source": [ + "## The data: hourly bike rentals\n", + "\n", + "The OpenML copy of the UCI Bike Sharing dataset has one row per hour, with the rental `count`, the\n", + "weather (`temp`, `feel_temp`, `humidity`, `windspeed`, and a `weather` category) and calendar flags\n", + "(`holiday`, `workingday`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0bc1af3", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import fetch_openml\n", + "\n", + "# parser=\"liac-arff\" avoids an incompatibility between scikit-learn's default parser and pandas 3.\n", + "raw = fetch_openml(data_id=42712, as_frame=True, parser=\"liac-arff\").frame\n", + "raw.head()" + ] + }, + { + "cell_type": "markdown", + "id": "9f0627ce", + "metadata": {}, + "source": [ + "This copy has no date column, so we rebuild it. Some hours are missing from the source data.We put\n", + "the data on a complete hourly grid, by replacing missing values by `NaN` which the model accepss as\n", + "\"not observed\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bcae385", + "metadata": {}, + "outputs": [], + "source": [ + "day = np.r_[0, np.diff(raw[\"weekday\"].to_numpy()) % 7].cumsum()\n", + "observed_at = (\n", + " pd.Timestamp(\"2011-01-01\")\n", + " + pd.to_timedelta(day, unit=\"D\")\n", + " + pd.to_timedelta(raw[\"hour\"].to_numpy(), unit=\"h\")\n", + ")\n", + "assert (observed_at.month == raw[\"month\"].to_numpy()).all()\n", + "assert (observed_at.year - 2011 == raw[\"year\"].to_numpy()).all()\n", + "\n", + "grid = pd.date_range(observed_at[0], observed_at[-1], freq=\"h\")\n", + "bikes = raw.set_index(observed_at).reindex(grid)\n", + "\n", + "# Calendar facts are known for every hour, including the missing ones, so rebuild them from dates.\n", + "holiday_dates = observed_at[(raw[\"holiday\"] == \"True\").to_numpy()].normalize().unique()\n", + "bikes[\"holiday\"] = grid.normalize().isin(holiday_dates).astype(float)\n", + "bikes[\"workingday\"] = ((grid.day_of_week < 5) & (bikes[\"holiday\"] == 0)).astype(float)\n", + "workingday = (raw[\"workingday\"] == \"True\").to_numpy()\n", + "assert (bikes.loc[observed_at, \"workingday\"].to_numpy() == workingday).all()\n", + "\n", + "# Categorical inputs are passed as numeric codes.\n", + "weather_codes = {\"clear\": 0, \"misty\": 1, \"rain\": 2, \"heavy_rain\": 3}\n", + "bikes[\"weather\"] = bikes[\"weather\"].astype(object).map(weather_codes).astype(float)\n", + "\n", + "print(\n", + " f\"{len(grid):,} hours from {grid[0]:%d %b %Y} to {grid[-1]:%d %b %Y}; \"\n", + " f\"{bikes['count'].isna().sum()} hours missing\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0133238c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (top, bottom) = plt.subplots(2, 1, figsize=(12, 6))\n", + "bikes[\"count\"].resample(\"D\").sum().plot(ax=top, lw=0.8, color=CI_TEAL)\n", + "top.set(\n", + " title=\"Daily rentals: growth, yearly seasonality and weather shocks\",\n", + " xlabel=\"\",\n", + " ylabel=\"rentals/day\",\n", + ")\n", + "week = bikes.loc[\"2012-06-04\":\"2012-06-10\", \"count\"]\n", + "week.plot(ax=bottom, lw=1, color=CI_TEAL)\n", + "bottom.set(\n", + " title=\"One week, hourly: commuter peaks on weekdays, a single midday hump at weekends\",\n", + " xlabel=\"\",\n", + " ylabel=\"rentals/hour\",\n", + ")\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "02aa0615", + "metadata": {}, + "source": [ + "We forecast the week starting **Sunday 1 July 2012**, one week of hourly steps (`H = 168`).\n", + "The week contains the **4 July** public holiday, a Wednesday, when the\n", + "weekday commuting peaks disappear.\n", + "\n", + "All earlier data is history. `history` is a `[variables, time]` tensor, so a single series has one\n", + "row. The actual values for the forecast week are used only for scoring." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17b7b54a", + "metadata": {}, + "outputs": [], + "source": [ + "ORIGIN = pd.Timestamp(\"2012-07-01\")\n", + "H = 168\n", + "past = bikes[bikes.index < ORIGIN]\n", + "future = bikes[bikes.index >= ORIGIN].iloc[:H]\n", + "truth = future[\"count\"].to_numpy()\n", + "\n", + "history = torch.tensor(past[\"count\"].to_numpy(), dtype=torch.float32)[None] # [1, T]\n", + "print(f\"history: {tuple(history.shape)} ({history.shape[1] / 24:.0f} days)\")" + ] + }, + { + "cell_type": "markdown", + "id": "443d1753", + "metadata": {}, + "source": [ + "## 1. A first forecast from history alone\n", + "\n", + "`model.forecast(history, horizon)` returns a `[variables, quantiles, horizon]` tensor: 9 quantiles\n", + "for each future hour, in the original units." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0334543", + "metadata": {}, + "outputs": [], + "source": [ + "plain = model.forecast(history, horizon=H)\n", + "print(f\"forecast: {tuple(plain.shape)}\")\n", + "\n", + "fig, ax = plt.subplots()\n", + "plot_forecast(ax, past[\"count\"].iloc[-7 * 24 :], plain[0], future.index, truth)\n", + "ax.set(title=\"History only: one week ahead\", ylabel=\"rentals/hour\")\n", + "pd.DataFrame({\"history only\": scores(plain[0], truth)}).T.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "258d5ee2", + "metadata": {}, + "source": [ + "Without any extra inputs, the model picks up the daily and weekly shape from history.\n", + "It expects commuter peaks on 4 July, though, because it has no way of knowing it is a holiday." + ] + }, + { + "cell_type": "markdown", + "id": "dbfa2a1e", + "metadata": {}, + "source": [ + "## 2. Covariates: known-future inputs\n", + "\n", + "Covariates are extra inputs that the model conditions on. It doesn't forecast them.\n", + "Pass values known in advance for the forecast week with `future_covariates`, shaped\n", + "`[known, T + H]`.\n", + "\n", + "We compare two ways to describe the calendar:\n", + "\n", + "1. **Calendar flags.** `holiday` and `workingday` are known for the forecast week.\n", + "2. **A categorical day type × hour code.** Categorical rows hold numeric codes and are listed in\n", + " `categorical_future`. The model replaces each code with statistics of the\n", + " target for that category, computed from the history. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5e048ba", + "metadata": {}, + "outputs": [], + "source": [ + "def rows(frame: pd.DataFrame, columns: list[str]) -> torch.Tensor:\n", + " # Covariate tensor [len(columns), len(frame)] from DataFrame columns.\n", + " return torch.tensor(frame[columns].to_numpy().T, dtype=torch.float32)\n", + "\n", + "\n", + "window = pd.concat([past, future]) # history plus the forecast week: [T + H] rows\n", + "window[\"daytype_hour\"] = window.index.hour + 24 * window[\"workingday\"] # 48 categories\n", + "\n", + "flags = model.forecast(\n", + " history, horizon=H, future_covariates=rows(window, [\"holiday\", \"workingday\"])\n", + ")\n", + "daytype = model.forecast(\n", + " history,\n", + " horizon=H,\n", + " future_covariates=rows(window, [\"daytype_hour\"]),\n", + " categorical_future=(0,),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "971bc4c5", + "metadata": {}, + "outputs": [], + "source": [ + "# A simple benchmark: repeat last week's value at the same hour.\n", + "seasonal_naive = past[\"count\"].iloc[-H:].to_numpy()\n", + "\n", + "results = pd.DataFrame(\n", + " {\n", + " \"seasonal naive (last week)\": {\"MAE\": np.nanmean(np.abs(truth - seasonal_naive))},\n", + " \"history only\": scores(plain[0], truth),\n", + " \"+ holiday & working-day flags\": scores(flags[0], truth),\n", + " \"+ day type × hour (categorical)\": scores(daytype[0], truth),\n", + " }\n", + ").T\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "days = (future.index >= \"2012-07-03\") & (future.index < \"2012-07-06\")\n", + "plot_forecast(\n", + " ax,\n", + " None,\n", + " plain[0][:, days],\n", + " future.index[days],\n", + " color=CI_MUTED,\n", + " label=\"history only\",\n", + " bands=False,\n", + ")\n", + "plot_forecast(\n", + " ax,\n", + " None,\n", + " daytype[0][:, days],\n", + " future.index[days],\n", + " truth[days],\n", + " color=CI_TEAL,\n", + " label=\"+ day type × hour\",\n", + ")\n", + "ax.axvspan(\n", + " pd.Timestamp(\"2012-07-04\"),\n", + " pd.Timestamp(\"2012-07-05\"),\n", + " color=CI_HIGHLIGHT,\n", + " alpha=0.18,\n", + " label=\"4 July public holiday\",\n", + ")\n", + "ax.set(title=\"3–5 July, with 4 July shaded\", ylabel=\"rentals/hour\")\n", + "ax.legend(\n", + " loc=\"lower left\",\n", + " bbox_to_anchor=(0, 1.12, 1, 0),\n", + " mode=\"expand\",\n", + " ncol=6,\n", + " fontsize=8,\n", + " borderaxespad=0,\n", + ")\n", + "fig.tight_layout()\n", + "results.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "5b42955d", + "metadata": {}, + "source": [ + "The calendar flags help, and the categorical day-type code helps much more. With it, the model\n", + "drops the commuter peaks on 4 July and forecasts a weekend-like day instead. Holidays are rare in\n", + "the history, about one a month, so handing the model a target-encoded category is more effective\n", + "than a bare 0/1 flag." + ] + }, + { + "cell_type": "markdown", + "id": "136c6bef", + "metadata": {}, + "source": [ + "## 3. Using the quantiles\n", + "\n", + "The 9 quantiles describe a full predictive distribution for every hour, which supports several\n", + "practical uses:\n", + "\n", + "- **Intervals** for planning ranges, such as the 20–80% band.\n", + "- **Exceedance probabilities**, such as the chance that demand exceeds a capacity. We read them off\n", + " the quantiles by interpolation.\n", + "- **Calibration checks**: a nominal 80% interval should contain about 80% of the actual values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e149098", + "metadata": {}, + "outputs": [], + "source": [ + "peak = pd.Timestamp(\"2012-07-02 17:00\") # Monday evening rush hour\n", + "step = future.index.get_loc(peak)\n", + "summary = {\n", + " f\"P{round(level * 100)}\": plain[0, qi(level), step].item()\n", + " for level in (0.1, 0.3, 0.5, 0.7, 0.9)\n", + "}\n", + "print(f\"{peak:%a %d %b %H:%M}: actual {truth[step]:.0f} rentals\")\n", + "pd.Series(summary, name=\"forecast\").to_frame().T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a6aa3a3", + "metadata": {}, + "outputs": [], + "source": [ + "CAPACITY = 600 # an illustrative threshold, e.g. bikes available at docks across the network\n", + "\n", + "\n", + "def exceedance(prediction_row: torch.Tensor, threshold: float) -> np.ndarray:\n", + " # P(value > threshold) for each step, interpolated from the quantiles [quantiles, horizon].\n", + " values = prediction_row.numpy()\n", + " cdf = [\n", + " np.interp(threshold, values[:, h], levels.numpy(), left=0.0, right=1.0)\n", + " for h in range(values.shape[1])\n", + " ]\n", + " return 1 - np.array(cdf)\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.bar(future.index, exceedance(plain[0], CAPACITY), width=1 / 24, color=CI_HIGHLIGHT)\n", + "ax.set(\n", + " title=f\"Probability that hourly demand exceeds {CAPACITY} rentals\",\n", + " ylabel=\"probability\",\n", + " ylim=(0, 1),\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "332c08f2", + "metadata": {}, + "outputs": [], + "source": [ + "rows = {}\n", + "for low, high in ((0.3, 0.7), (0.1, 0.9)):\n", + " inside = (truth >= plain[0, qi(low)].numpy()) & (truth <= plain[0, qi(high)].numpy())\n", + " rows[f\"{high - low:.0%} interval\"] = {\n", + " \"nominal\": high - low,\n", + " \"observed this week\": inside.mean(),\n", + " }\n", + "pd.DataFrame(rows).T.round(2)" + ] + }, + { + "cell_type": "markdown", + "id": "a3657c73", + "metadata": {}, + "source": [ + "## 4. Missing data\n", + "\n", + "Real data has gaps. Mark missing observations as `NaN` in `history` (or any covariate), with no need\n", + "to impute. The history used so far already contains the dataset's own missing hours. Here we make it\n", + "much harder: we delete a random 30% of hours **and** add a three-day outage that ends two days\n", + "before the forecast origin." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54ee2ac1", + "metadata": {}, + "outputs": [], + "source": [ + "generator = torch.Generator().manual_seed(0)\n", + "gappy = history.clone()\n", + "gappy[torch.rand(gappy.shape, generator=generator) < 0.3] = torch.nan\n", + "gappy[:, -5 * 24 : -2 * 24] = torch.nan\n", + "print(f\"observed: {(~gappy.isnan()).float().mean():.0%} of hours\")\n", + "\n", + "degraded = model.forecast(gappy, horizon=H)\n", + "\n", + "fig, ax = plt.subplots()\n", + "plot_forecast(\n", + " ax,\n", + " pd.Series(gappy[0, -7 * 24 :].numpy(), past.index[-7 * 24 :]),\n", + " degraded[0],\n", + " future.index,\n", + " truth,\n", + ")\n", + "ax.set(\n", + " title=\"Forecasting through gaps: 30% of hours removed plus a three-day outage\",\n", + " ylabel=\"rentals/hour\",\n", + ")\n", + "pd.DataFrame(\n", + " {\"complete history\": scores(plain[0], truth), \"with gaps\": scores(degraded[0], truth)}\n", + ").T.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "621ae209", + "metadata": {}, + "source": [ + "