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<a id="madewithml.utils"></a>
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<div class="doc doc-contents first">
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<div class="doc doc-children">
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<div class="doc doc-object doc-function">
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<h2 id="madewithml.utils.collate_fn" class="doc doc-heading">
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<code class="highlight language-python">collate_fn(batch)</code>
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||
|
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</h2>
|
||
|
||
|
||
<div class="doc doc-contents ">
|
||
|
||
<p>Convert a batch of numpy arrays to tensors (with appropriate padding).</p>
|
||
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
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<col class="field-name" />
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<col class="field-body" />
|
||
</colgroup>
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<tbody valign="top">
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||
<tr class="field">
|
||
<th class="field-name">Parameters:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
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||
<li>
|
||
<b><code>batch</code></b>
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||
(<code><span title="typing.Dict">Dict</span>[str, <span title="numpy.ndarray">ndarray</span>]</code>)
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||
–
|
||
<div class="doc-md-description">
|
||
<p>input batch as a dictionary of numpy arrays.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
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</tbody>
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</table>
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<table class="field-list">
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<colgroup>
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<col class="field-name" />
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<col class="field-body" />
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||
</colgroup>
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<tbody valign="top">
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<tr class="field">
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<th class="field-name">Returns:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<code><span title="typing.Dict">Dict</span>[str, <span title="torch.Tensor">Tensor</span>]</code>
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||
–
|
||
<div class="doc-md-description">
|
||
<p>Dict[str, torch.Tensor]: output batch as a dictionary of tensors.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
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||
</tbody>
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||
</table>
|
||
<details class="quote">
|
||
<summary>Source code in <code>madewithml/utils.py</code></summary>
|
||
<pre class="highlight"><code class="language-python">def collate_fn(batch: Dict[str, np.ndarray]) -> Dict[str, torch.Tensor]: # pragma: no cover, air internal
|
||
"""Convert a batch of numpy arrays to tensors (with appropriate padding).
|
||
|
||
Args:
|
||
batch (Dict[str, np.ndarray]): input batch as a dictionary of numpy arrays.
|
||
|
||
Returns:
|
||
Dict[str, torch.Tensor]: output batch as a dictionary of tensors.
|
||
"""
|
||
batch["ids"] = pad_array(batch["ids"])
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||
batch["masks"] = pad_array(batch["masks"])
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||
dtypes = {"ids": torch.int32, "masks": torch.int32, "targets": torch.int64}
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||
tensor_batch = {}
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||
for key, array in batch.items():
|
||
tensor_batch[key] = torch.as_tensor(array, dtype=dtypes[key], device=get_device())
|
||
return tensor_batch</code></pre>
|
||
</details>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div class="doc doc-object doc-function">
|
||
|
||
|
||
|
||
|
||
<h2 id="madewithml.utils.dict_to_list" class="doc doc-heading">
|
||
<code class="highlight language-python">dict_to_list(data, keys)</code>
|
||
|
||
</h2>
|
||
|
||
|
||
<div class="doc doc-contents ">
|
||
|
||
<p>Convert a dictionary to a list of dictionaries.</p>
|
||
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Parameters:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<b><code>data</code></b>
|
||
(<code><span title="typing.Dict">Dict</span></code>)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>input dictionary.</p>
|
||
</div>
|
||
</li>
|
||
<li>
|
||
<b><code>keys</code></b>
|
||
(<code><span title="typing.List">List</span>[str]</code>)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>keys to include in the output list of dictionaries.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Returns:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<code><span title="typing.List">List</span>[<span title="typing.Dict">Dict</span>[str, <span title="typing.Any">Any</span>]]</code>
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>List[Dict[str, Any]]: output list of dictionaries.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<details class="quote">
|
||
<summary>Source code in <code>madewithml/utils.py</code></summary>
|
||
<pre class="highlight"><code class="language-python">def dict_to_list(data: Dict, keys: List[str]) -> List[Dict[str, Any]]:
|
||
"""Convert a dictionary to a list of dictionaries.
|
||
|
||
Args:
|
||
data (Dict): input dictionary.
|
||
keys (List[str]): keys to include in the output list of dictionaries.
|
||
|
||
Returns:
|
||
List[Dict[str, Any]]: output list of dictionaries.
|
||
"""
|
||
list_of_dicts = []
|
||
for i in range(len(data[keys[0]])):
|
||
new_dict = {key: data[key][i] for key in keys}
|
||
list_of_dicts.append(new_dict)
|
||
return list_of_dicts</code></pre>
|
||
</details>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div class="doc doc-object doc-function">
|
||
|
||
|
||
|
||
|
||
<h2 id="madewithml.utils.get_run_id" class="doc doc-heading">
|
||
<code class="highlight language-python">get_run_id(experiment_name, trial_id)</code>
|
||
|
||
</h2>
|
||
|
||
|
||
<div class="doc doc-contents ">
|
||
|
||
<p>Get the MLflow run ID for a specific Ray trial ID.</p>
|
||
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Parameters:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<b><code>experiment_name</code></b>
|
||
(<code>str</code>)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>name of the experiment.</p>
|
||
</div>
|
||
</li>
|
||
<li>
|
||
<b><code>trial_id</code></b>
|
||
(<code>str</code>)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>id of the trial.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Returns:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<b><code>str</code></b>( <code>str</code>
|
||
) –
|
||
<div class="doc-md-description">
|
||
<p>run id of the trial.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<details class="quote">
|
||
<summary>Source code in <code>madewithml/utils.py</code></summary>
|
||
<pre class="highlight"><code class="language-python">def get_run_id(experiment_name: str, trial_id: str) -> str: # pragma: no cover, mlflow functionality
|
||
"""Get the MLflow run ID for a specific Ray trial ID.
|
||
|
||
Args:
|
||
experiment_name (str): name of the experiment.
|
||
trial_id (str): id of the trial.
|
||
|
||
Returns:
|
||
str: run id of the trial.
|
||
"""
|
||
trial_name = f"TorchTrainer_{trial_id}"
|
||
run = mlflow.search_runs(experiment_names=[experiment_name], filter_string=f"tags.trial_name = '{trial_name}'").iloc[0]
|
||
return run.run_id</code></pre>
|
||
</details>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div class="doc doc-object doc-function">
|
||
|
||
|
||
|
||
|
||
<h2 id="madewithml.utils.load_dict" class="doc doc-heading">
|
||
<code class="highlight language-python">load_dict(path)</code>
|
||
|
||
</h2>
|
||
|
||
|
||
<div class="doc doc-contents ">
|
||
|
||
<p>Load a dictionary from a JSON's filepath.</p>
|
||
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Parameters:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<b><code>path</code></b>
|
||
(<code>str</code>)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>location of file.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Returns:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<b><code>Dict</code></b>( <code><span title="typing.Dict">Dict</span></code>
|
||
) –
|
||
<div class="doc-md-description">
|
||
<p>loaded JSON data.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<details class="quote">
|
||
<summary>Source code in <code>madewithml/utils.py</code></summary>
|
||
<pre class="highlight"><code class="language-python">def load_dict(path: str) -> Dict:
|
||
"""Load a dictionary from a JSON's filepath.
|
||
|
||
Args:
|
||
path (str): location of file.
|
||
|
||
Returns:
|
||
Dict: loaded JSON data.
|
||
"""
|
||
with open(path) as fp:
|
||
d = json.load(fp)
|
||
return d</code></pre>
|
||
</details>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div class="doc doc-object doc-function">
|
||
|
||
|
||
|
||
|
||
<h2 id="madewithml.utils.pad_array" class="doc doc-heading">
|
||
<code class="highlight language-python">pad_array(arr, dtype=np.int32)</code>
|
||
|
||
</h2>
|
||
|
||
|
||
<div class="doc doc-contents ">
|
||
|
||
<p>Pad an 2D array with zeros until all rows in the
|
||
2D array are of the same length as a the longest
|
||
row in the 2D array.</p>
|
||
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Parameters:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<b><code>arr</code></b>
|
||
(<code><span title="numpy.array">array</span></code>)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>input array</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Returns:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<code><span title="numpy.ndarray">ndarray</span></code>
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>np.array: zero padded array</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<details class="quote">
|
||
<summary>Source code in <code>madewithml/utils.py</code></summary>
|
||
<pre class="highlight"><code class="language-python">def pad_array(arr: np.ndarray, dtype=np.int32) -> np.ndarray:
|
||
"""Pad an 2D array with zeros until all rows in the
|
||
2D array are of the same length as a the longest
|
||
row in the 2D array.
|
||
|
||
Args:
|
||
arr (np.array): input array
|
||
|
||
Returns:
|
||
np.array: zero padded array
|
||
"""
|
||
max_len = max(len(row) for row in arr)
|
||
padded_arr = np.zeros((arr.shape[0], max_len), dtype=dtype)
|
||
for i, row in enumerate(arr):
|
||
padded_arr[i][: len(row)] = row
|
||
return padded_arr</code></pre>
|
||
</details>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div class="doc doc-object doc-function">
|
||
|
||
|
||
|
||
|
||
<h2 id="madewithml.utils.save_dict" class="doc doc-heading">
|
||
<code class="highlight language-python">save_dict(d, path, cls=None, sortkeys=False)</code>
|
||
|
||
</h2>
|
||
|
||
|
||
<div class="doc doc-contents ">
|
||
|
||
<p>Save a dictionary to a specific location.</p>
|
||
|
||
|
||
|
||
<table class="field-list">
|
||
<colgroup>
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
</colgroup>
|
||
<tbody valign="top">
|
||
<tr class="field">
|
||
<th class="field-name">Parameters:</th>
|
||
<td class="field-body">
|
||
<ul class="first simple">
|
||
<li>
|
||
<b><code>d</code></b>
|
||
(<code><span title="typing.Dict">Dict</span></code>)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>data to save.</p>
|
||
</div>
|
||
</li>
|
||
<li>
|
||
<b><code>path</code></b>
|
||
(<code>str</code>)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>location of where to save the data.</p>
|
||
</div>
|
||
</li>
|
||
<li>
|
||
<b><code>cls</code></b>
|
||
(<code>optional</code>, default:
|
||
<code>None</code>
|
||
)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>encoder to use on dict data. Defaults to None.</p>
|
||
</div>
|
||
</li>
|
||
<li>
|
||
<b><code>sortkeys</code></b>
|
||
(<code>bool</code>, default:
|
||
<code>False</code>
|
||
)
|
||
–
|
||
<div class="doc-md-description">
|
||
<p>whether to sort keys alphabetically. Defaults to False.</p>
|
||
</div>
|
||
</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<details class="quote">
|
||
<summary>Source code in <code>madewithml/utils.py</code></summary>
|
||
<pre class="highlight"><code class="language-python">def save_dict(d: Dict, path: str, cls: Any = None, sortkeys: bool = False) -> None:
|
||
"""Save a dictionary to a specific location.
|
||
|
||
Args:
|
||
d (Dict): data to save.
|
||
path (str): location of where to save the data.
|
||
cls (optional): encoder to use on dict data. Defaults to None.
|
||
sortkeys (bool, optional): whether to sort keys alphabetically. Defaults to False.
|
||
"""
|
||
directory = os.path.dirname(path)
|
||
if directory and not os.path.exists(directory): # pragma: no cover
|
||
os.makedirs(directory)
|
||
with open(path, "w") as fp:
|
||
json.dump(d, indent=2, fp=fp, cls=cls, sort_keys=sortkeys)
|
||
fp.write("\n")</code></pre>
|
||
</details>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div class="doc doc-object doc-function">
|
||
|
||
|
||
|
||
|
||
<h2 id="madewithml.utils.set_seeds" class="doc doc-heading">
|
||
<code class="highlight language-python">set_seeds(seed=42)</code>
|
||
|
||
</h2>
|
||
|
||
|
||
<div class="doc doc-contents ">
|
||
|
||
<p>Set seeds for reproducibility.</p>
|
||
|
||
<details class="quote">
|
||
<summary>Source code in <code>madewithml/utils.py</code></summary>
|
||
<pre class="highlight"><code class="language-python">def set_seeds(seed: int = 42):
|
||
"""Set seeds for reproducibility."""
|
||
np.random.seed(seed)
|
||
random.seed(seed)
|
||
torch.manual_seed(seed)
|
||
torch.cuda.manual_seed(seed)
|
||
eval("setattr(torch.backends.cudnn, 'deterministic', True)")
|
||
eval("setattr(torch.backends.cudnn, 'benchmark', False)")
|
||
os.environ["PYTHONHASHSEED"] = str(seed)</code></pre>
|
||
</details>
|
||
</div>
|
||
|
||
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|
||
|
||
|
||
|
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