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amaiya committed Jul 15, 2021
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2 changes: 1 addition & 1 deletion README.md
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## Usage

To try out the examples yourself:
To try out the [examples](https://amaiya.github.io/causalnlp/examples.html) yourself:

<a href="https://colab.research.google.com/drive/1hu7j2QCWkVlFsKbuereWWRDOBy1anMbQ?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

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2 changes: 1 addition & 1 deletion docs/index.html
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Expand Up @@ -76,7 +76,7 @@ <h2 id="Usage">Usage<a class="anchor-link" href="#Usage"> </a></h2>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>To try out the examples yourself:</p>
<p>To try out the <a href="https://amaiya.github.io/causalnlp/examples.html">examples</a> yourself:</p>
<p><a href="https://colab.research.google.com/drive/1hu7j2QCWkVlFsKbuereWWRDOBy1anMbQ?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a></p>

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6 changes: 3 additions & 3 deletions docs/meta.utils.html
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Expand Up @@ -356,7 +356,7 @@ <h4 id="gini" class="doc_header"><code>gini</code><a href="https://github.com/am


<div class="output_markdown rendered_html output_subarea ">
<h4 id="regression_metrics" class="doc_header"><code>regression_metrics</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L235" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>regression_metrics</code>(<strong><code>y</code></strong>, <strong><code>p</code></strong>, <strong><code>w</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>{'RMSE': &lt;function rmse at 0x7fe851c36620&gt;, 'sMAPE': &lt;function smape at 0x7fe851c36598&gt;, 'Gini': &lt;function gini at 0x7fe851c366a8&gt;}</code></em>)</p>
<h4 id="regression_metrics" class="doc_header"><code>regression_metrics</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L235" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>regression_metrics</code>(<strong><code>y</code></strong>, <strong><code>p</code></strong>, <strong><code>w</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>{'RMSE': &lt;function rmse at 0x7f2fa5638598&gt;, 'sMAPE': &lt;function smape at 0x7f2fa5638510&gt;, 'Gini': &lt;function gini at 0x7f2fa5638620&gt;}</code></em>)</p>
</blockquote>
<p>Log metrics for regressors.</p>
<p>Args:
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<h4 id="classification_metrics" class="doc_header"><code>classification_metrics</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L279" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>classification_metrics</code>(<strong><code>y</code></strong>, <strong><code>p</code></strong>, <strong><code>w</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>{'AUC': &lt;function roc_auc_score at 0x7fe870bc1ae8&gt;, 'Log Loss': &lt;function logloss at 0x7fe851c367b8&gt;}</code></em>)</p>
<h4 id="classification_metrics" class="doc_header"><code>classification_metrics</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L279" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>classification_metrics</code>(<strong><code>y</code></strong>, <strong><code>p</code></strong>, <strong><code>w</code></strong>=<em><code>None</code></em>, <strong><code>metrics</code></strong>=<em><code>{'AUC': &lt;function roc_auc_score at 0x7f2fc45c7a60&gt;, 'Log Loss': &lt;function logloss at 0x7f2fa5638730&gt;}</code></em>)</p>
</blockquote>
<p>Log metrics for classifiers.</p>
<p>Args:
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<h2 id="MatchOptimizer" class="doc_header"><code>class</code> <code>MatchOptimizer</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L501" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>MatchOptimizer</code>(<strong><code>treatment_col</code></strong>=<em><code>'is_treatment'</code></em>, <strong><code>ps_col</code></strong>=<em><code>'pihat'</code></em>, <strong><code>user_col</code></strong>=<em><code>None</code></em>, <strong><code>matching_covariates</code></strong>=<em><code>['pihat']</code></em>, <strong><code>max_smd</code></strong>=<em><code>0.1</code></em>, <strong><code>max_deviation</code></strong>=<em><code>0.1</code></em>, <strong><code>caliper_range</code></strong>=<em><code>(0.01, 0.5)</code></em>, <strong><code>max_pihat_range</code></strong>=<em><code>(0.95, 0.999)</code></em>, <strong><code>max_iter_per_param</code></strong>=<em><code>5</code></em>, <strong><code>min_users_per_group</code></strong>=<em><code>1000</code></em>, <strong><code>smd_cols</code></strong>=<em><code>['pihat']</code></em>, <strong><code>dev_cols_transformations</code></strong>=<em><code>{'pihat': &lt;function mean at 0x7fe9e4451d90&gt;}</code></em>, <strong><code>dev_factor</code></strong>=<em><code>1.0</code></em>, <strong><code>verbose</code></strong>=<em><code>True</code></em>)</p>
<h2 id="MatchOptimizer" class="doc_header"><code>class</code> <code>MatchOptimizer</code><a href="https://github.com/amaiya/causalnlp/tree/main/causalnlp/meta/utils.py#L501" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>MatchOptimizer</code>(<strong><code>treatment_col</code></strong>=<em><code>'is_treatment'</code></em>, <strong><code>ps_col</code></strong>=<em><code>'pihat'</code></em>, <strong><code>user_col</code></strong>=<em><code>None</code></em>, <strong><code>matching_covariates</code></strong>=<em><code>['pihat']</code></em>, <strong><code>max_smd</code></strong>=<em><code>0.1</code></em>, <strong><code>max_deviation</code></strong>=<em><code>0.1</code></em>, <strong><code>caliper_range</code></strong>=<em><code>(0.01, 0.5)</code></em>, <strong><code>max_pihat_range</code></strong>=<em><code>(0.95, 0.999)</code></em>, <strong><code>max_iter_per_param</code></strong>=<em><code>5</code></em>, <strong><code>min_users_per_group</code></strong>=<em><code>1000</code></em>, <strong><code>smd_cols</code></strong>=<em><code>['pihat']</code></em>, <strong><code>dev_cols_transformations</code></strong>=<em><code>{'pihat': &lt;function mean at 0x7f313866fe18&gt;}</code></em>, <strong><code>dev_factor</code></strong>=<em><code>1.0</code></em>, <strong><code>verbose</code></strong>=<em><code>True</code></em>)</p>
</blockquote>

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