Update pipeline docs after API change #2195
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## main #2195 +/- ##
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|
angela97lin
requested review from
dsherry,
bchen1116,
freddyaboulton,
chukarsten,
jeremyliweishih and
ParthivNaresh
| @@ -187,7 +189,8 @@ | |||
| "metadata": {}, | |||
| "source": [ | |||
| "## Pipeline Parameters\n", | |||
| "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", | |||
| "You can also pass in custom parameters by using the `parameters` parameter, 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", | |||
| "with open('pipeline.pkl', 'rb') as f:\n", | ||
| " pickled_pipeline = pickle.load(f)\n", | ||
| "\n", | ||
| "pickled_pipeline.fit(X, y)" |
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This is great. I would add
assert pickled_pipeline == pipeline_to_picklebefore the call to fit, to demonstrate the two pipeline instances are equivalent.
| " - `TimeSeriesMulticlassClassificationPipeline`\n", | ||
| " \n", | ||
| "The class you want to use will depend on your problem type.\n", | ||
| "The only required parameter input for instantiating a pipeline instance is `component_graph`, which is either a list or a dictionary containing a sequence of components to be fit and evaluated.\n", |
| "\n", | ||
| " def __init__(self, parameters, random_seed=0):\n", | ||
| " super().__init__(self.component_graph, parameters=parameters, random_seed=random_seed)\n" | ||
| "component_graph_as_list = ['Imputer', 'Random Forest Classifier']\n", |
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This looks a lot simpler.
| "metadata": { | ||
| "scrolled": true | ||
| }, |
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what is this? lol, does it mean it starts scrolled down?
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Apparently means the cell was scrolled yeah lol I'm gonna delete this 😂
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Just curious: does that change how the docs look on RTD?
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Not that I could tell... 🤔
| " parameters = {\"parameter_1\": parameter_1}\n", | ||
| " super().__init__(parameters=parameters,\n", | ||
| " component_obj=transformer,\n", | ||
| " component_obj=None,\n", |
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Is this no longer the method of making a third party transformer? I think this change is a little confusing because it begs the question "what is the component_obj param?"
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Ahaha previously it didn't use a real third party transformer but it never error-ed because the code wasn't run... now that's no longer the case 😓 I can update this to use a real third party transformer and then comment it as such!
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Actually, we have info on this on our components doc which is linked so I'll just remove this line so it's not a focus.
Closes #2158