Test updates

alteryx · Jul 11, 2023 · 5a0f530 · 5a0f530
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Showing 3 changed files with 1 addition and 18 deletions.
diff --git a/docs/source/start.ipynb b/docs/source/start.ipynb
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 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Start"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "__Note:__ To provide data to EvalML, it is recommended that you initialize a woodwork accessor so that you control how EvalML will treat each feature, such as as a numeric feature, a categorical feature, a text feature or other type of feature. Consult the [the Woodwork project](https://woodwork.alteryx.com/en/stable/) for help on how to do this. Here, `split_data()` returns dataframes with woodwork accessors."
    ]
   },
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    "cell_type": "markdown",
    "metadata": {},
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    "cell_type": "markdown",
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    ]
   },
   {
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We also provide a standalone `search` [method](./autoapi/evalml/automl/index.rst#evalml.automl.search) which does all of the above in a single line, and returns the `AutoMLSearch` instance and data check results. If there were data check errors, AutoML will not be run and no `AutoMLSearch` instance will be returned."
    ]
   },
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diff --git a/evalml/automl/automl_algorithm/default_algorithm.py b/evalml/automl/automl_algorithm/default_algorithm.py
@@ -170,8 +170,6 @@ def default_max_batches(self):
         """Returns the number of max batches AutoMLSearch should run by default."""
         if self.ensembling:
             return 3
-        elif is_time_series(self.problem_type):
-            return 2  # we do not run feature selection for time series
         else:
             return 2
 

diff --git a/evalml/tests/automl_tests/test_automl.py b/evalml/tests/automl_tests/test_automl.py
@@ -1804,7 +1804,7 @@ def test_pipelines_in_batch_return_nan(
         X_train=X,
         y_train=y,
         problem_type="binary",
-        max_batches=2,
+        max_batches=3,
         automl_algorithm="iterative",
         allowed_component_graphs={"Name": [dummy_classifier_estimator_class]},
         n_jobs=1,