Improve Documentation and Add Getting Started Tutorial

README.md

@@ -1 +1,23 @@
-# rtichoke_python
+# rtichoke
+
+`rtichoke` is a Python library for visualizing the performance of predictive models. It provides a flexible and intuitive way to create a variety of common evaluation plots, including:
+
+*   **ROC Curves**
+*   **Precision-Recall Curves**
+*   **Gains and Lift Charts**
+*   **Decision Curves**
+
+The library is designed to be easy to use, while still offering a high degree of control over the final plots.
+
+## Key Features
+
+*   **Simple API**: Create complex visualizations with just a few lines of code.
+*   **Time-to-Event Analysis**: Native support for models with time-dependent outcomes, including censoring and competing risks.
+*   **Interactive Plots**: Built on Plotly for interactive, publication-quality figures.
+*   **Flexible Data Handling**: Works seamlessly with NumPy and Polars.
+
+## Documentation
+
+For a complete guide to the library, including a "Getting Started" tutorial and a full API reference, please see the **[official documentation](https://your-documentation-url.com)**.
+
+*(Note: The documentation URL will need to be updated once the website is deployed.)*

docs/_quarto.yml

@@ -1,30 +1,30 @@
 project:
   type: website
 
-metadata-files:
-  - _sidebar.yml
-
 website:
   title: "rtichoke"
-  navbar:
-    left:
-      - href: reference/
-        text: Reference
+  sidebar:
+    - id: user-guide
+      title: "User Guide"
+      style: "docked"
+      contents:
+        - text: "Getting Started"
+          href: tutorials/getting_started.qmd
+    - id: api-reference
+      title: "API Reference"
+      style: "docked"
+      contents:
+        - href: reference/index.qmd
+          text: "Reference"
 
 quartodoc:
-  # the name used to import the package you want to create reference docs for
   package: rtichoke
-  sidebar: "_sidebar.yml"
   sections:
     - title: Performance Data
       desc: Functions for creating performance data.
       contents:
         - prepare_performance_data
         - prepare_performance_data_times
-    # - title: Calibration
-      # desc: Functions for Calibration.
-      # contents:
-        # - create_calibration_curve
     - title: Discrimination
       desc: Functions for Discrimination.
       contents:
@@ -40,4 +40,4 @@ quartodoc:
       desc: Functions for Utility.
       contents:
         - create_decision_curve
-        - plot_decision_curve
+        - plot_decision_curve

docs/index.qmd

@@ -0,0 +1,13 @@
+---
+title: "rtichoke Documentation"
+---
+
+Welcome to the official documentation for `rtichoke`, a Python library for visualizing the performance of predictive models.
+
+## Getting Started
+
+If you're new to `rtichoke`, the best place to start is the **[Getting Started Tutorial](./tutorials/getting_started.qmd)**. It will walk you through the basics of installing the library, preparing your data, and creating your first plot.
+
+## API Reference
+
+For detailed information on the functions and classes provided by `rtichoke`, please refer to the **[API Reference](./reference/index.qmd)**.

docs/tutorials/getting_started.qmd

@@ -0,0 +1,62 @@
+---
+title: "Getting Started with Rtichoke"
+---
+
+This tutorial provides a basic introduction to the `rtichoke` library. We'll walk through the process of preparing data, creating a decision curve, and visualizing the results.
+
+## 1. Import Libraries
+
+First, let's import the necessary libraries. We'll need `numpy` for data manipulation and `rtichoke` for the core functionality.
+
+```python
+import numpy as np
+import rtichoke as rk
+```
+
+## 2. Prepare Your Data
+
+`rtichoke` expects data in a specific format. You'll need two main components:
+
+*   **Probabilities (`probs`)**: A dictionary where keys are model names and values are NumPy arrays of predicted probabilities.
+*   **Real Outcomes (`reals`)**: A NumPy array containing the true binary outcomes (0 or 1).
+
+Let's create some sample data for two different models:
+
+```python
+# Sample data from the dcurves_example.py script
+probs_dict = {
+    "Marker": np.array([
+        0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.1, 0.2, 0.3, 0.4, 0.5,
+        0.6, 0.7, 0.8, 0.9, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
+    ]),
+    "Marker2": np.array([
+        0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.1, 0.2, 0.3, 0.4, 0.5,
+        0.6, 0.7, 0.8, 0.9, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
+    ])
+}
+reals = np.array([
+    1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1
+])
+```
+
+## 3. Create a Decision Curve
+
+Now that we have our data, we can create a decision curve. This is a simple one-liner with `rtichoke`:
+
+```python
+fig = rk.create_decision_curve(
+    probs=probs_dict,
+    reals=reals,
+)
+```
+
+## 4. Show the Plot
+
+Finally, let's display the plot. Since `rtichoke` uses Plotly under the hood, you can show the figure just like any other Plotly object.
+
+```python
+# To display the plot in an interactive environment (like a Jupyter notebook)
+fig.show()
+```
+
+And that's it! You've created your first decision curve with `rtichoke`. From here, you can explore the other curve types and options that the library has to offer.

src/rtichoke/discrimination/gains.py

@@ -42,39 +42,33 @@ def create_gains_curve(
         "#585123",
     ],
 ) -> Figure:
-    """Create Gains Curve.
+    """Creates a Gains curve.
+
+    A Gains curve is a marketing and business analytics tool that evaluates
+    the performance of a predictive model. It shows the percentage of
+    positive outcomes (the "gain") that can be captured by targeting a
+    certain percentage of the population, sorted by predicted probability.
 
     Parameters
     ----------
     probs : Dict[str, np.ndarray]
-        Dictionary mapping a label or group name to an array of predicted
-        probabilities for the positive class.
+        A dictionary mapping model or dataset names to 1-D numpy arrays of
+        predicted probabilities.
     reals : Union[np.ndarray, Dict[str, np.ndarray]]
-        Ground-truth binary labels (0/1) as a single array, or a dictionary
-        mapping the same label/group keys used in ``probs`` to arrays of
-        ground-truth labels.
+        The true binary labels (0 or 1).
     by : float, optional
-        Resolution for probability thresholds when computing the curve
-        (step size). Default is 0.01.
+        The step size for the probability thresholds. Defaults to 0.01.
     stratified_by : Sequence[str], optional
-        Sequence of column names to stratify the performance data by.
-        Default is ["probability_threshold"].
+        Variables for stratification. Defaults to ``["probability_threshold"]``.
     size : int, optional
-        Plot size in pixels (width and height). Default is 600.
+        The width and height of the plot in pixels. Defaults to 600.
     color_values : List[str], optional
-        List of color hex strings to use for the plotted lines. If not
-        provided, a default palette is used.
+        A list of hex color strings for the plot lines.
 
     Returns
     -------
     Figure
-        A Plotly ``Figure`` containing the Gains curve(s).
-
-    Notes
-    -----
-    The function delegates computation and plotting to
-    ``_create_rtichoke_plotly_curve_binary`` and returns the resulting
-    Plotly figure.
+        A Plotly ``Figure`` object representing the Gains curve.
     """
     fig = _create_rtichoke_plotly_curve_binary(
         probs,
@@ -93,30 +87,27 @@ def plot_gains_curve(
     stratified_by: Sequence[str] = ["probability_threshold"],
     size: int = 600,
 ) -> Figure:
-    """Plot Gains curve from performance data.
+    """Plots a Gains curve from pre-computed performance data.
+
+    This function is useful for plotting a Gains curve directly from a
+    DataFrame that already contains the necessary performance metrics.
 
     Parameters
     ----------
     performance_data : pl.DataFrame
-        A Polars DataFrame containing performance metrics for the Gains curve.
-        Expected columns include (but may not be limited to)
-        ``probability_threshold`` and gains-related metrics, plus any
-        stratification columns.
+        A Polars DataFrame with performance metrics. It must include columns
+        for the percentage of the population targeted and the corresponding
+        gain, along with any stratification variables.
     stratified_by : Sequence[str], optional
-        Sequence of column names used for stratification in the
-        ``performance_data``. Default is ["probability_threshold"].
+        The columns in `performance_data` used for stratification. Defaults to
+        ``["probability_threshold"]``.
     size : int, optional
-        Plot size in pixels (width and height). Default is 600.
+        The width and height of the plot in pixels. Defaults to 600.
 
     Returns
     -------
     Figure
-        A Plotly ``Figure`` containing the Gains plot.
-
-    Notes
-    -----
-    This function wraps ``_plot_rtichoke_curve_binary`` to produce a
-    ready-to-render Plotly figure from precomputed performance data.
+        A Plotly ``Figure`` object representing the Gains curve.
     """
     fig = _plot_rtichoke_curve_binary(
         performance_data,
@@ -163,7 +154,37 @@ def create_gains_curve_times(
         "#585123",
     ],
 ) -> Figure:
-    """Create time-dependent Lift Curve."""
+    """Creates a time-dependent Gains curve.
+
+    Generates a Gains curve for time-to-event models, which is evaluated at
+    specified time horizons and handles censored data and competing risks.
+
+    Parameters
+    ----------
+    probs : Dict[str, np.ndarray]
+        A dictionary of predicted probabilities.
+    reals : Union[np.ndarray, Dict[str, np.ndarray]]
+        The true event statuses.
+    times : Union[np.ndarray, Dict[str, np.ndarray]]
+        The event or censoring times.
+    fixed_time_horizons : list[float]
+        A list of time points for performance evaluation.
+    heuristics_sets : list[Dict], optional
+        Specifies how to handle censored data and competing events.
+    by : float, optional
+        The step size for probability thresholds. Defaults to 0.01.
+    stratified_by : Sequence[str], optional
+        Variables for stratification. Defaults to ``["probability_threshold"]``.
+    size : int, optional
+        The width and height of the plot in pixels. Defaults to 600.
+    color_values : List[str], optional
+        A list of hex color strings for the plot lines.
+
+    Returns
+    -------
+    Figure
+        A Plotly ``Figure`` object for the time-dependent Gains curve.
+    """
 
     fig = _create_rtichoke_plotly_curve_times(
         probs,