Freeze Hyperparameters for AutoMLSearch

[bchen1116]

fix #767

Right now, the implementation assumes that the user includes the 'default' values of the components in the hyperparameter dict. We catch the error that AutoML throws and warn the users if this default value isn't in the provided hyperparameter ranges. Hyperparameter validation would be tracked through this issue.

Since we have no hyperparameter validation as of yet, if a user creates a new pipeline with EvalML components, redefines a hyperparameter range that doesn't include the default value, and calls AutoMLSearch on this pipeline, AutoMLSearch will raise the error that the default values aren't in the search space. This will ideally be addressed in the issue above. Additionally, since we don't do hyperparam validation, we don't catch/throw an error if a user misspells a component name, which will allow AutoMLSearch to run with no errors

custom_hyperparameters = {
        # misspell `Imputer'
        "Inpute": {
            "numeric_impute_strategy": ["mean"]
        }
 }
# will run with no errors
make_pipelines(X, y, estimator, 'binary', custom_hyperparameters)
# will run and compute with original `Imputer` hyperparameters, and quietly return the results
AutoMLSearch(problem_type='binary', allowed_pipelines=[make_pipelines]).search(X, y) 

This can be fixed once hyperparameter validation is implemented in the before-mentioned issue.

Docs here

[codecov]

Codecov Report

Merging #1284 into main will increase coverage by 0.01%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main    #1284      +/-   ##
==========================================
+ Coverage   99.95%   99.95%   +0.01%     
==========================================
  Files         213      213              
  Lines       13857    13928      +71     
==========================================
+ Hits        13850    13921      +71     
  Misses          7        7              

Impacted Files	Coverage Δ
...lml/automl/automl_algorithm/iterative_algorithm.py	100.00% <100.00%> (ø)
evalml/pipelines/utils.py	100.00% <100.00%> (ø)
evalml/tests/automl_tests/test_automl.py	100.00% <100.00%> (ø)
evalml/tests/pipeline_tests/test_pipelines.py	100.00% <100.00%> (ø)

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 1ec2ee4...bbcf93f. Read the comment docs.

[bchen1116]

Only throw the hyperparameter range error if we receive that error, otherwise we can throw the original error

[dsherry]

@bchen1116 looks great! I left a couple asks, and some suggestions on the docs content, but LGTM :D

[dsherry]

@bchen1116 RE the bug you ran into with the docs here: I filed as #1367

We already know one workaround: create a woodwork DataTable before calling make_pipelines, so that the datetime feature has the right type in the pandas DataFrame passed to make_pipelines (datetime64 instead of object). It occurs to me another workaround for you in this PR would be to use a dataset which doesn't have a datetime feature 😆