Pipeline user guide: include description of how to access the component instances and features

docs/source/release_notes.rst

@@ -26,6 +26,7 @@ Release Notes
         * Added a step to our release process for pushing our latest version to conda-forge :pr:`1118`
         * Added warning for missing ipywidgets dependency for using `PipelineSearchPlots` on Jupyterlab :pr:`1145`
         * Updated README.md example to load demo dataset :pr:`1151`
+        * Included description of how to access the component instances and features for pipeline user guide :pr:`1163`
         * Swapped mapping of breast cancer targets in `model_understanding.ipynb` :pr:`1170`
     * Testing Changes
         * Added test confirming `TextFeaturizer` never outputs null values :pr:`1122`

docs/source/user_guide/pipelines.ipynb

@@ -18,7 +18,7 @@
    "metadata": {},
    "source": [
     "## Class Definition\n",
-    "Pipeline definitions must inherit from the proper pipeline base class, `RegressionPipeline`, `BinaryClassificationPipeline` or `MulticlassClassificationPipeline`. They must also include a `component_graph` list as a class variable containing the sequence of components to be fit and evaluated. Each component in the graph can be provided as either a string name or as a reference to the component class."
+    "Pipeline definitions must inherit from the proper pipeline base class, `RegressionPipeline`, `BinaryClassificationPipeline` or `MulticlassClassificationPipeline`. They must also include a `component_graph` list as a class variable containing the sequence of components to be fit and evaluated. The `component_graph` list is used to determine the ordered list of components that should be instantiated when a pipeline instance is created. Each component in `component_graph` can be provided as either a string name or as a reference to the component class. "
    ]
   },
   {
@@ -174,7 +174,9 @@
    "metadata": {},
    "source": [
     "## Pipeline Parameters\n",
-    "You can also pass in custom parameters. The parameters dictionary needs to be in the format of a two-layered dictionary where the first key-value pair is the component name and component parameters dictionary. The component parameters dictionary consists of a key value pair of parameter name and parameter values. An example will be shown below and component parameters can be found [here](../api_reference.rst#components)."
+    "You can also pass in custom parameters, which will then be used when instantiating each component in `component_graph`. The parameters dictionary needs to be in the format of a two-layered dictionary where the key-value pairs are the component name and corresponding component parameters dictionary. The component parameters dictionary consists of (parameter name, parameter values) key-value pairs.\n",
+    "\n",
+    "An example will be shown below. The API reference for component parameters can also be found [here](../api_reference.rst#components)."
    ]
   },
   {
@@ -229,6 +231,85 @@
    "source": [
     "cp.describe()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Component Graph\n",
+    "\n",
+    "You can use the pipeline's `component_graph` attribute to access a component at a specific index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp.component_graph[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can use `pipeline.get_component(name)` and provide the component name instead (API reference [here](../generated/methods/evalml.pipelines.PipelineBase.get_component.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp.get_component('One Hot Encoder')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pipeline Estimator\n",
+    "\n",
+    "EvalML enforces that the last component of a pipeline is an estimator. You can access this estimator directly by using either `pipeline.component_graph[-1]` or `pipeline.estimator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp.component_graph[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp.estimator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Input Feature Names\n",
+    "\n",
+    "After a pipeline is fitted, you can access a pipeline's `input_feature_names` attribute to obtain a dictionary containing a list of feature names passed to each component of the pipeline. This could be especially useful for debugging where a feature might have been dropped or detecting unexpected behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline.input_feature_names"
+   ]
   }
  ],
  "metadata": {
@@ -247,7 +328,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.8"
   }
  },
  "nbformat": 4,