Delete finding docs update

site/_quarto.yml

@@ -244,8 +244,8 @@ website:
           contents:
           - text: "View and filter findings"
             file: guide/model-validation/view-filter-model-findings.qmd
-          - text: "Add and update findings"
-            file: guide/model-validation/add-update-model-findings.qmd
+          - text: "Add and manage findings"
+            file: guide/model-validation/add-manage-model-findings.qmd
         - guide/model-validation/view-reports.qmd
         - guide/model-documentation/export-documentation.qmd
         - text: "---"

site/guide/model-validation/_assess-compliance-assess.qmd

@@ -20,7 +20,7 @@
 
    ![](/guide/model-validation/assess-compliance.png){width=60% fig-alt="A screenshot of the validation report section 2.1.1. that shows a compliance assessment with the option to link to finding"}
 
-1. On the **Link Finding to Report** page that opens, select from the list of available findings, or create a new finding.^[[Create new finding](/guide/model-validation/add-update-model-findings.qmd)] 
+1. On the **Link Finding to Report** page that opens, select from the list of available findings, or create a new finding.^[[Create new finding](/guide/model-validation/add-manage-model-findings.qmd)] 
 
 1. Under **Risk Assessment Notes**, add any relevant notes that explain your assessment further. 
 
@@ -51,7 +51,7 @@ A compliance summary gets shown for each subsection under **2. Validation** and
 
    ![](/guide/model-validation/assess-compliance.png){width=60% fig-alt="A screenshot of the validation report section 2.1.1. that shows a compliance assessment with the option to link to finding"}
 
-1. On the **Link Finding to Report** page that opens, select from the list of available findings, or [create a new finding](/guide/model-validation/add-update-model-findings.qmd). 
+1. On the **Link Finding to Report** page that opens, select from the list of available findings, or [create a new finding](/guide/model-validation/add-manage-model-findings.qmd). 
 
 1. Under **Risk Assessment Notes**, add any relevant notes that explain your assessment further. 
 

site/guide/model-validation/_assess-compliance-link-finding.qmd

@@ -13,7 +13,7 @@
 
    ![](/guide/model-validation/link-finding.png){width=60% fig-alt="A screenshot of the validation report section 2.1.1. that shows a compliance assessment with the option to link to finding"}
 
-1. On the **Link Finding to Report** page that opens, select from the list of available findings, or create a new finding.^[[Create new finding](/guide/model-validation/add-update-model-findings.qmd)] 
+1. On the **Link Finding to Report** page that opens, select from the list of available findings, or create a new finding.^[[Create new finding](/guide/model-validation/add-manage-model-findings.qmd)] 
 
 1. Click **Update Linked Findings**.
 
@@ -34,7 +34,7 @@ The newly linked-to finding now gets shown under **Findings**.
 
    ![](/guide/model-validation/link-finding.png){width=60% fig-alt="A screenshot of the validation report section 2.1.1. that shows a compliance assessment with the option to link to finding"}
 
-1. On the **Link Finding to Report** page that opens, select from the list of available findings, or [create a new finding](/guide/model-validation/add-update-model-findings.qmd). 
+1. On the **Link Finding to Report** page that opens, select from the list of available findings, or [create a new finding](/guide/model-validation/add-manage-model-findings.qmd). 
 
 1. Click **Update Linked Findings**.
 

site/guide/model-validation/add-update-model-findings.qmd → site/guide/model-validation/add-manage-model-findings.qmd

@@ -1,9 +1,9 @@
 ---
-title: "Add and update model findings"
+title: "Add and manage model findings"
 date: last-modified
 ---
 
-Add findings within the {{< var validmind.platform >}} at the model or documentation section level, and update your findings to include proposed remediation plans and supporting documentation.
+Add findings within the {{< var validmind.platform >}} at the model or documentation section level, update your findings to include proposed remediation plans and supporting documentation, or delete findings that no longer need to be tracked.
 
 ::: {.prereq}
 
@@ -120,6 +120,22 @@ Uploaded files must be less than 50 MB in size.
 
 ![](delete-finding-attachment.gif){width=80% fig-alt="An animated gif demonstrating how to delete an attachment from a finding"}
 
+## Delete model findings
+
+::: {.callout-important title="Finding deletion is permanent."}
+While finding deletion will be logged under your Model Activity,[^8] deleted findings cannot be retrieved.
+:::
+
+If you logged a finding in error or otherwise no longer need to track that finding, you can delete it: 
+
+1. Locate the finding you want to delete.[^9]
+
+2. On the finding's detail page, click **{{< fa trash-can>}} Delete Finding** in the right sidebar.
+
+   Once you confirm, the finding will be permanently deleted. 
+
+   ![](delete-model-finding.gif){width=80% fig-alt="An animated gif demonstrating how to delete a finding"}
+
 
 <!-- FOOTNOTES -->
 
@@ -135,4 +151,8 @@ Uploaded files must be less than 50 MB in size.
 
 [^6]: [View and filter model findings](view-filter-model-findings.qmd)
 
-[^7]: [Manage supporting documentation](#manage-supporting-documentation)
+[^7]: [Manage supporting documentation](#manage-supporting-documentation)
+
+[^8]: [View documentation activity](/guide/model-documentation/view-documentation-activity.qmd)
+
+[^9]: [View and filter model findings](view-filter-model-findings.qmd)

site/guide/model-validation/view-filter-model-findings.qmd

@@ -123,8 +123,8 @@ Filters can be removed by clicking on the {{< fa xmark >}} next to them on the m
 
 [^4]: [Filter model findings](#filter-model-findings)
 
-[^5]: [Manage supporting documentation](add-update-model-findings.qmd#manage-supporting-documentation)
+[^5]: [Manage supporting documentation](add-manage-model-findings.qmd#manage-supporting-documentation)
 
 [^6]: [Working with the model inventory](/guide/model-inventory/working-with-model-inventory.qmd#search-filter-and-sort-models)
 
-[^7]: [Manage supporting documentation](add-update-model-findings.qmd#manage-supporting-documentation)
+[^7]: [Manage supporting documentation](add-manage-model-findings.qmd#manage-supporting-documentation)

site/guide/model-validation/working-with-model-findings.qmd

@@ -10,7 +10,7 @@ listing:
     fields: [title, description]
     contents:
       - view-filter-model-findings.qmd
-      - add-update-model-findings.qmd
+      - add-manage-model-findings.qmd
 filters:
   - tachyons
 ---

site/python-docs/validmind.html

@@ -150,7 +150,7 @@ <h1 class="modulename">
                 <section id="__version__">
                     <div class="attr variable">
             <span class="name">__version__</span>        =
-<span class="default_value">&#39;2.5.7&#39;</span>
+<span class="default_value">&#39;2.5.13&#39;</span>
 
 
     </div>
@@ -685,7 +685,7 @@ <h6 id="arguments">Arguments:</h6>
 
 <ul>
 <li><strong>results (list):</strong>  A list of ThresholdTestResults objects</li>
-<li><strong>inputs (list):</strong>  A list of input keys (names) that were used to run the test</li>
+<li><strong>inputs (list):</strong>  A list of input IDs that were used to run the test</li>
 </ul>
 
 <h6 id="raises">Raises:</h6>

site/python-docs/validmind/tests/data_validation.html

@@ -34,13 +34,10 @@ <h3>Submodules</h3>
             <ul>
                     <li><a href="data_validation/ACFandPACFPlot.html">ACFandPACFPlot</a></li>
                     <li><a href="data_validation/ADF.html">ADF</a></li>
-                    <li><a href="data_validation/ANOVAOneWayTable.html">ANOVAOneWayTable</a></li>
                     <li><a href="data_validation/AutoAR.html">AutoAR</a></li>
                     <li><a href="data_validation/AutoMA.html">AutoMA</a></li>
                     <li><a href="data_validation/AutoSeasonality.html">AutoSeasonality</a></li>
                     <li><a href="data_validation/AutoStationarity.html">AutoStationarity</a></li>
-                    <li><a href="data_validation/BivariateFeaturesBarPlots.html">BivariateFeaturesBarPlots</a></li>
-                    <li><a href="data_validation/BivariateHistograms.html">BivariateHistograms</a></li>
                     <li><a href="data_validation/BivariateScatterPlots.html">BivariateScatterPlots</a></li>
                     <li><a href="data_validation/ChiSquaredFeaturesTable.html">ChiSquaredFeaturesTable</a></li>
                     <li><a href="data_validation/ClassImbalance.html">ClassImbalance</a></li>
@@ -51,7 +48,6 @@ <h3>Submodules</h3>
                     <li><a href="data_validation/Duplicates.html">Duplicates</a></li>
                     <li><a href="data_validation/EngleGrangerCoint.html">EngleGrangerCoint</a></li>
                     <li><a href="data_validation/FeatureTargetCorrelationPlot.html">FeatureTargetCorrelationPlot</a></li>
-                    <li><a href="data_validation/HeatmapFeatureCorrelations.html">HeatmapFeatureCorrelations</a></li>
                     <li><a href="data_validation/HighCardinality.html">HighCardinality</a></li>
                     <li><a href="data_validation/HighPearsonCorrelation.html">HighPearsonCorrelation</a></li>
                     <li><a href="data_validation/IQROutliersBarPlot.html">IQROutliersBarPlot</a></li>
@@ -61,7 +57,6 @@ <h3>Submodules</h3>
                     <li><a href="data_validation/LaggedCorrelationHeatmap.html">LaggedCorrelationHeatmap</a></li>
                     <li><a href="data_validation/MissingValues.html">MissingValues</a></li>
                     <li><a href="data_validation/MissingValuesBarPlot.html">MissingValuesBarPlot</a></li>
-                    <li><a href="data_validation/MissingValuesRisk.html">MissingValuesRisk</a></li>
                     <li><a href="data_validation/PearsonCorrelationMatrix.html">PearsonCorrelationMatrix</a></li>
                     <li><a href="data_validation/PhillipsPerronArch.html">PhillipsPerronArch</a></li>
                     <li><a href="data_validation/RollingStatsPlot.html">RollingStatsPlot</a></li>

site/python-docs/validmind/tests/data_validation/ACFandPACFPlot.html

@@ -75,27 +75,29 @@ <h1 class="modulename">
             <div class="docstring"><p>Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to
 reveal trends and correlations.</p>
 
-<p><strong>Purpose</strong>: The ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) plot test is employed
-to analyze time series data in machine learning models. It illuminates the correlation of the data over time by
-plotting the correlation of the series with its own lags (ACF), and the correlations after removing effects already
-accounted for by earlier lags (PACF). This information can identify trends, such as seasonality, degrees of
-autocorrelation, and inform the selection of order parameters for AutoRegressive Integrated Moving Average (ARIMA)
-models.</p>
+<h3 id="purpose">Purpose</h3>
 
-<p><strong>Test Mechanism</strong>: The <code><a href="#ACFandPACFPlot">ACFandPACFPlot</a></code> test accepts a dataset with a time-based index. It first confirms the
-index is of a datetime type, then handles any NaN values. The test subsequently generates ACF and PACF plots for
-each column in the dataset, producing a subplot for each. If the dataset doesn't include key columns, an error is
-returned.</p>
+<p>The ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) plot test is employed to analyze
+time series data in machine learning models. It illuminates the correlation of the data over time by plotting the
+correlation of the series with its own lags (ACF), and the correlations after removing effects already accounted
+for by earlier lags (PACF). This information can identify trends, such as seasonality, degrees of autocorrelation,
+and inform the selection of order parameters for AutoRegressive Integrated Moving Average (ARIMA) models.</p>
 
-<p><strong>Signs of High Risk</strong>:</p>
+<h3 id="test-mechanism">Test Mechanism</h3>
+
+<p>The <code><a href="#ACFandPACFPlot">ACFandPACFPlot</a></code> test accepts a dataset with a time-based index. It first confirms the index is of a datetime
+type, then handles any NaN values. The test subsequently generates ACF and PACF plots for each column in the
+dataset, producing a subplot for each. If the dataset doesn't include key columns, an error is returned.</p>
+
+<h3 id="signs-of-high-risk">Signs of High Risk</h3>
 
 <ul>
 <li>Sudden drops in the correlation at a specific lag might signal a model at high risk.</li>
 <li>Consistent high correlation across multiple lags could also indicate non-stationarity in the data, which may
 suggest that a model estimated on this data won't generalize well to future, unknown data.</li>
 </ul>
 
-<p><strong>Strengths</strong>:</p>
+<h3 id="strengths">Strengths</h3>
 
 <ul>
 <li>ACF and PACF plots offer clear graphical representations of the correlations in time series data.</li>
@@ -105,7 +107,7 @@ <h1 class="modulename">
 parameters.</li>
 </ul>
 
-<p><strong>Limitations</strong>:</p>
+<h3 id="limitations">Limitations</h3>
 
 <ul>
 <li>ACF and PACF plots are exclusively for time series data and hence, can't be applied to all ML models.</li>

site/python-docs/validmind/tests/data_validation/ADF.html

@@ -81,39 +81,43 @@ <h1 class="modulename">
 
             <div class="docstring"><p>Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test.</p>
 
-<p><strong>Purpose</strong>: The Augmented Dickey-Fuller (ADF) test metric is used here to determine the order of integration,
-i.e., the stationarity of a given time series data. The stationary property of data is pivotal in many machine
-learning models as it impacts the reliability and effectiveness of predictions and forecasts.</p>
+<h3 id="purpose">Purpose</h3>
 
-<p><strong>Test Mechanism</strong>: The ADF test starts by executing the ADF function from the statsmodels library on every feature
-of the dataset. Multiple outputs are generated for each run, including the ADF test statistic and p-value, count of
-lags used, the number of observations factored into the test, critical values at various confidence levels, and the
-maximized information criterion. These results are stored for each feature for subsequent analysis.</p>
+<p>The Augmented Dickey-Fuller (ADF) test metric is used to determine the order of integration, i.e., the stationarity
+of a given time series dataset. The stationary property of data is pivotal in many machine learning models as it
+impacts the reliability and effectiveness of predictions and forecasts.</p>
 
-<p><strong>Signs of High Risk</strong>:</p>
+<h3 id="test-mechanism">Test Mechanism</h3>
+
+<p>The ADF test is executed using the <code>adfuller</code> function from the <code>statsmodels</code> library on each feature of the
+dataset. Multiple outputs are generated for each run, including the ADF test statistic and p-value, count of lags
+used, the number of observations considered in the test, critical values at various confidence levels, and the
+information criterion. These results are stored for each feature for subsequent analysis.</p>
+
+<h3 id="signs-of-high-risk">Signs of High Risk</h3>
 
 <ul>
-<li>An inflated ADF statistic and high p-value (generally above 0.05) insinuate a high risk to the model's
-performance due to the presence of a unit root indicating non-stationarity.</li>
-<li>Such non-stationarity might result in untrustworthy or insufficient forecasts.</li>
+<li>An inflated ADF statistic and high p-value (generally above 0.05) indicate a high risk to the model's performance
+due to the presence of a unit root indicating non-stationarity.</li>
+<li>Non-stationarity might result in untrustworthy or insufficient forecasts.</li>
 </ul>
 
-<p><strong>Strengths</strong>:</p>
+<h3 id="strengths">Strengths</h3>
 
 <ul>
-<li>The ADF test is robust to more sophisticated correlation within the data, which empowers it to be deployed in
-settings where data might display complex stochastic behavior.</li>
-<li>The ADF test provides explicit outputs like test statistics, critical values, and information criterion, thereby
-enhancing our understanding and transparency of the model validation process.</li>
+<li>The ADF test is robust to sophisticated correlations within the data, making it suitable for settings where data
+displays complex stochastic behavior.</li>
+<li>It provides explicit outputs like test statistics, critical values, and information criterion, enhancing
+understanding and transparency in the model validation process.</li>
 </ul>
 
-<p><strong>Limitations</strong>:</p>
+<h3 id="limitations">Limitations</h3>
 
 <ul>
 <li>The ADF test might demonstrate low statistical power, making it challenging to differentiate between a unit root
-and near-unit-root processes causing false negatives.</li>
-<li>The test assumes the data follows an autoregressive process, which might not be the case all the time.</li>
-<li>The ADF test finds it demanding to manage time series data with structural breaks.</li>
+and near-unit-root processes, potentially causing false negatives.</li>
+<li>It assumes the data follows an autoregressive process, which might not always be the case.</li>
+<li>The test struggles with time series data that have structural breaks.</li>
 </ul>
 </div>
 

site/python-docs/validmind/tests/data_validation/AutoAR.html

@@ -77,42 +77,42 @@ <h1 class="modulename">
 
             <div class="docstring"><p>Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria.</p>
 
-<p><strong>Purpose</strong>:</p>
+<h3 id="purpose">Purpose</h3>
 
 <p>The AutoAR test is intended to automatically identify the Autoregressive (AR) order of a time series by utilizing
 the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC). AR order is crucial in forecasting
 tasks as it dictates the quantity of prior terms in the sequence to use for predicting the current term. The
 objective is to select the most fitting AR model that encapsulates the trend and seasonality in the time series
 data.</p>
 
-<p><strong>Test Mechanism</strong>:</p>
+<h3 id="test-mechanism">Test Mechanism</h3>
 
 <p>The test mechanism operates by iterating through a possible range of AR orders up to a defined maximum. An AR model
 is fitted for each order, and the corresponding BIC and AIC are computed. BIC and AIC statistical measures are
 designed to penalize models for complexity, preferring simpler models that fit the data proficiently. To verify the
-stationarity of the time series, the Augmented Dickey-Fuller test is executed. The AR order, BIC, and AIC findings,
+stationarity of the time series, the Augmented Dickey-Fuller test is executed. The AR order, BIC, and AIC findings
 are compiled into a dataframe for effortless comparison. Then, the AR order with the smallest BIC is established as
 the desirable order for each variable.</p>
 
-<p><strong>Signs of High Risk</strong>:</p>
+<h3 id="signs-of-high-risk">Signs of High Risk</h3>
 
 <ul>
 <li>An augmented Dickey Fuller test p-value &gt; 0.05, indicating the time series isn't stationary, may lead to
 inaccurate results.</li>
 <li>Problems with the model fitting procedure, such as computational or convergence issues.</li>
-<li>Continuous selection of the maximum specified AR order may suggest insufficient set limit.</li>
+<li>Continuous selection of the maximum specified AR order may suggest an insufficient set limit.</li>
 </ul>
 
-<p><strong>Strengths</strong>:</p>
+<h3 id="strengths">Strengths</h3>
 
 <ul>
 <li>The test independently pinpoints the optimal AR order, thereby reducing potential human bias.</li>
 <li>It strikes a balance between model simplicity and goodness-of-fit to avoid overfitting.</li>
-<li>Has the capability to account for stationarity in a time series, an essential aspect for dependable AR modelling.</li>
-<li>The results are aggregated into an comprehensive table, enabling an easy interpretation.</li>
+<li>Has the capability to account for stationarity in a time series, an essential aspect for dependable AR modeling.</li>
+<li>The results are aggregated into a comprehensive table, enabling an easy interpretation.</li>
 </ul>
 
-<p><strong>Limitations</strong>:</p>
+<h3 id="limitations">Limitations</h3>
 
 <ul>
 <li>The tests need a stationary time series input.</li>