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    <title>topic Feature Store : for sklearn flavored models, are timestamps fully supported? in Machine Learning</title>
    <link>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30491#M1641</link>
    <description>&lt;P&gt;I have created a feature table (Databricks runtime ML 10.2) that includes a timestamp column as a primary key, that is not used as a feature but as a column to join on.&lt;/P&gt;&lt;P&gt;I have then created a model that trains from this feature table and some additional data, which excludes the primary keys. I tried excluding them, both using the feature store api, and from the sklearn api. The model is being trained fine, but when use the score_batch() method, I get an error claiming that 'TypeError: float() argument must be a string or a number, not 'Timestamp''.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;This error is coming from sklearn, so is there some incompatibility there, or is this a bug in feature store?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Steps to reproduce :&lt;/P&gt;&lt;UL&gt;&lt;LI&gt;create feature table with one column as timestamp type&lt;/LI&gt;&lt;LI&gt;train a model using sklearn that does not use that timestamp column&lt;/LI&gt;&lt;LI&gt;use score_batch() method and visualize results&lt;/LI&gt;&lt;/UL&gt;</description>
    <pubDate>Fri, 28 Jan 2022 13:57:25 GMT</pubDate>
    <dc:creator>thib</dc:creator>
    <dc:date>2022-01-28T13:57:25Z</dc:date>
    <item>
      <title>Feature Store : for sklearn flavored models, are timestamps fully supported?</title>
      <link>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30491#M1641</link>
      <description>&lt;P&gt;I have created a feature table (Databricks runtime ML 10.2) that includes a timestamp column as a primary key, that is not used as a feature but as a column to join on.&lt;/P&gt;&lt;P&gt;I have then created a model that trains from this feature table and some additional data, which excludes the primary keys. I tried excluding them, both using the feature store api, and from the sklearn api. The model is being trained fine, but when use the score_batch() method, I get an error claiming that 'TypeError: float() argument must be a string or a number, not 'Timestamp''.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;This error is coming from sklearn, so is there some incompatibility there, or is this a bug in feature store?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Steps to reproduce :&lt;/P&gt;&lt;UL&gt;&lt;LI&gt;create feature table with one column as timestamp type&lt;/LI&gt;&lt;LI&gt;train a model using sklearn that does not use that timestamp column&lt;/LI&gt;&lt;LI&gt;use score_batch() method and visualize results&lt;/LI&gt;&lt;/UL&gt;</description>
      <pubDate>Fri, 28 Jan 2022 13:57:25 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30491#M1641</guid>
      <dc:creator>thib</dc:creator>
      <dc:date>2022-01-28T13:57:25Z</dc:date>
    </item>
    <item>
      <title>Re: Feature Store : for sklearn flavored models, are timestamps fully supported?</title>
      <link>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30492#M1642</link>
      <description>&lt;P&gt;maybe you can just try to cast timestamp as int&lt;/P&gt;</description>
      <pubDate>Fri, 28 Jan 2022 15:15:19 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30492#M1642</guid>
      <dc:creator>Hubert-Dudek</dc:creator>
      <dc:date>2022-01-28T15:15:19Z</dc:date>
    </item>
    <item>
      <title>Re: Feature Store : for sklearn flavored models, are timestamps fully supported?</title>
      <link>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30493#M1643</link>
      <description>&lt;P&gt;Thanks for your reply Hubert. Yes, casting it to long or int does solve the issue, but it is a workaround and I would like to keep the data as-is, with directly interpretable timestamps, especially when there is no reason why they should trigger an error during the prediction step since it is not being used at that stage.&lt;/P&gt;</description>
      <pubDate>Sat, 29 Jan 2022 13:20:23 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30493#M1643</guid>
      <dc:creator>thib</dc:creator>
      <dc:date>2022-01-29T13:20:23Z</dc:date>
    </item>
    <item>
      <title>Re: Feature Store : for sklearn flavored models, are timestamps fully supported?</title>
      <link>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30495#M1645</link>
      <description>&lt;P&gt;Hi @Thibault Daoulas​&amp;nbsp;,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Did @Kaniz Fatma​&amp;nbsp;response help you to resolved your question? if yes, please mark it as best response. If not, please let us know.&lt;/P&gt;</description>
      <pubDate>Tue, 15 Mar 2022 00:06:04 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30495#M1645</guid>
      <dc:creator>jose_gonzalez</dc:creator>
      <dc:date>2022-03-15T00:06:04Z</dc:date>
    </item>
    <item>
      <title>Re: Feature Store : for sklearn flavored models, are timestamps fully supported?</title>
      <link>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30496#M1646</link>
      <description>&lt;P&gt;Hi, it did not, but at least I know they are not fully supported so a workaround is to avoid timestamps, so I suppose you can mark this as resolved&lt;/P&gt;</description>
      <pubDate>Tue, 15 Mar 2022 07:56:05 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/feature-store-for-sklearn-flavored-models-are-timestamps-fully/m-p/30496#M1646</guid>
      <dc:creator>thib</dc:creator>
      <dc:date>2022-03-15T07:56:05Z</dc:date>
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