internlm/SWE-Fixer-Train-110K huggingface dataset:
Repository: DistrictDataLabs/yellowbrick

Issue:
Argument to PredictionError is overwritten
**Describe the bug**
Argument `is_fitted` is overwritten when Instantiating a `PredictionError` or  `ResidualsPlot`.

**To Reproduce**

```python
from yellowbrick.regressor import PredictionError
from sklearn.linear_models import Lasso

visualizer = PredictionError(Lasso(), "test title", is_fitted=True)
print(visualizer.is_fitted)
```

**Expected behavior**
When `is_fitted` is passed in, I expect it to not be overwritten in the __init__ calls


**Desktop (please complete the following information):**
 - OS: Ubuntu 20.04
 - Python Version 3.8.10
 - Yellowbrick Version :  1.3.post1


Argument to PredictionError is overwritten
**Describe the bug**
Argument `is_fitted` is overwritten when Instantiating a `PredictionError` or  `ResidualsPlot`.

**To Reproduce**

```python
from yellowbrick.regressor import PredictionError
from sklearn.linear_models import Lasso

visualizer = PredictionError(Lasso(), "test title", is_fitted=True)
print(visualizer.is_fitted)
```

**Expected behavior**
When `is_fitted` is passed in, I expect it to not be overwritten in the __init__ calls


**Desktop (please complete the following information):**
 - OS: Ubuntu 20.04
 - Python Version 3.8.10
 - Yellowbrick Version :  1.3.post1




Discussion:
@aramesh7 it does appear that `is_fitted` is defaulting to `auto` but I'm not sure why. We'll have to dig into this a bit more. 
@aramesh7 it does appear that `is_fitted` is defaulting to `auto` but I'm not sure why. We'll have to dig into this a bit more. 

Resolving patch:
diff --git a/yellowbrick/regressor/prediction_error.py b/yellowbrick/regressor/prediction_error.py
--- a/yellowbrick/regressor/prediction_error.py
+++ b/yellowbrick/regressor/prediction_error.py
@@ -121,11 +121,13 @@ def __init__(
         is_fitted="auto",
         **kwargs
     ):
-        # Whether or not to check if the model is already fitted
-        self.is_fitted = is_fitted
 
         # Initialize the visualizer
-        super(PredictionError, self).__init__(estimator, ax=ax, **kwargs)
+        super(PredictionError, self).__init__(
+            estimator,
+            is_fitted=is_fitted,
+            ax=ax,
+            **kwargs)
 
         # Visual arguments
         self.colors = {
diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py
--- a/yellowbrick/regressor/residuals.py
+++ b/yellowbrick/regressor/residuals.py
@@ -154,11 +154,13 @@ def __init__(
         is_fitted="auto",
         **kwargs
     ):
-        # Whether or not to check if the model is already fitted
-        self.is_fitted = is_fitted
 
         # Initialize the visualizer base
-        super(ResidualsPlot, self).__init__(estimator, ax=ax, **kwargs)
+        super(ResidualsPlot, self).__init__(
+            estimator,
+            ax=ax,
+            is_fitted=is_fitted,
+            **kwargs)
 
         # TODO: allow more scatter plot arguments for train and test points
         # See #475 (RE: ScatterPlotMixin)


Test patch:
Repository: DistrictDataLabs/yellowbrick

Issue:
Add description of parameter to docstring
@Haebuk had opened a PR to deal with this but closed it https://github.com/DistrictDataLabs/yellowbrick/pull/1233

We just need to add this line below to the KneeLocator docstring for `y` located in yellowbrick/utils/kneed.py 

"A list of k scores corresponding to each value of k. The type of k scores are determined by the metric parameter from the KElbowVisualizer class. " 

https://github.com/DistrictDataLabs/yellowbrick/blob/092c0ca25187b3cde9f608a1f7bc6d8c2b998f96/yellowbrick/utils/kneed.py#L61-L62


```
class KneeLocator(object):
    """
    Finds the "elbow" or "knee" which is a value corresponding to the point of maximum curvature
    in an elbow curve, using knee point detection algorithm. This point is accessible via the
    `knee` attribute.

    Parameters
    ----------
    x : list
       A list of k values representing the no. of clusters in KMeans Clustering algorithm.

    y : list
       A list of k scores corresponding to each value of k. The type of k scores are determined by the metric parameter from the KElbowVisualizer class.

    S : float, default: 1.0
       Sensitivity parameter that allows us to adjust how aggressive we want KneeLocator to
       be when detecting "knees" or "elbows".

    curve_nature : string, default: 'concave'
       A string that determines the nature of the elbow curve in which "knee" or "elbow" is
       to be found.

    curve_direction : string, default: 'increasing'
       A string that determines tha increasing or decreasing nature of the elbow curve in
       which "knee" or "elbow" is to be found.

    online : bool, default: False
        kneed will correct old knee points if True, will return first knee if False

    Notes
    -----
    The KneeLocator is implemented using the "knee point detection algorithm" which can be read at
    `<https://www1.icsi.berkeley.edu/~barath/papers/kneedle-simplex11.pdf>`
```


Discussion:
@lwgray  Can I please submit a PR on this ?
Sure, make sure it is on the development branch.

And thanks for using yellowbrick

Resolving patch:
diff --git a/yellowbrick/utils/kneed.py b/yellowbrick/utils/kneed.py
--- a/yellowbrick/utils/kneed.py
+++ b/yellowbrick/utils/kneed.py
@@ -59,7 +59,8 @@ class KneeLocator(object):
        A list of k values representing the no. of clusters in KMeans Clustering algorithm.
 
     y : list
-       A list of silhouette score corresponding to each value of k.
+       A list of k scores corresponding to each value of k. The type of k scores are determined 
+       by the metric parameter from the KElbowVisualizer class.
 
     S : float, default: 1.0
        Sensitivity parameter that allows us to adjust how aggressive we want KneeLocator to


Test patch:
Repository: DistrictDataLabs/yellowbrick

Issue:
Matplotlib warning about color usage in Datasaurus
**Describe the bug**
the datasaurus function used to demonstrate Anscombe's quartet is isssuiing the following warning

WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.

**To Reproduce**

```python
from yellowbrick import datasaurus
datasaurus()
```

**Dataset**

**Expected behavior**
Produce the image without warnings


**Desktop (please complete the following information):**
 - OS: [e.g. macOS] Google Colab
 - Python Version [e.g. 2.7, 3.6, miniconda] 3.8.10
 - Yellowbrick Version [e.g. 0.7] 1.5





Discussion:

Resolving patch:
diff --git a/yellowbrick/datasaurus.py b/yellowbrick/datasaurus.py
--- a/yellowbrick/datasaurus.py
+++ b/yellowbrick/datasaurus.py
@@ -1218,7 +1218,7 @@ def datasaurus():
         y = arr[1]
 
         # Draw the points in the scatter plot
-        ax.scatter(x, y, c=color)
+        ax.scatter(x, y, color=color)
 
         # Set the X and Y limits
         ax.set_xlim(0, 100)


Test patch:
Repository: ansible-collections/community.aws

Issue:
Module docs of aws_s3_bucket_info contain something that is parsed as datetime by YAML parser
##### SUMMARY
Module docs of `aws_s3_bucket_info` contain something that is parsed as datetime by YAML parser.

(This triggered ansible/ansible#69031.)

##### ISSUE TYPE
- Bug Report

##### COMPONENT NAME
aws_s3_bucket_info

##### ANSIBLE VERSION
```paste below
2.9
2.10
```

Module return docs of ec2_vpc_vpn contain broken sample
##### SUMMARY
The sample for the `vgw_telemetry` return value contains `datetime(2015, 1, 1)`:
```.yaml
vgw_telemetry:
  type: list
  returned: I(state=present)
  description: The telemetry for the VPN tunnel.
  sample:
    vgw_telemetry: [{
                     'outside_ip_address': 'string',
                     'status': 'up',
                     'last_status_change': datetime(2015, 1, 1),
                     'status_message': 'string',
                     'accepted_route_count': 123
                    }]
```
The inner dict parses as (list of items):
```.py
[('outside_ip_address', 'string'), ('status', 'up'), ('last_status_change', 'datetime(2015'), (1, None), ('1)', None), ('status_message', 'string'), ('accepted_route_count', 123)]
```
When ansible-doc tries to serialize this as JSON, it crashes. Also, the docs are rendered incorrectly, see [here](https://docs.ansible.com/ansible/latest/modules/ec2_vpc_vpn_module.html#return-vgw_telemetry).

(See also ansible/ansible#69031.)

##### ISSUE TYPE
- Bug Report

##### COMPONENT NAME
vgw_telemetry

##### ANSIBLE VERSION
```paste below
2.9
2.10
```



Discussion:

Resolving patch:
diff --git a/plugins/modules/aws_s3_bucket_info.py b/plugins/modules/aws_s3_bucket_info.py
--- a/plugins/modules/aws_s3_bucket_info.py
+++ b/plugins/modules/aws_s3_bucket_info.py
@@ -47,7 +47,7 @@
   description: "List of buckets"
   returned: always
   sample:
-    - creation_date: 2017-07-06 15:05:12 +00:00
+    - creation_date: '2017-07-06 15:05:12 +00:00'
       name: my_bucket
   type: list
 '''
diff --git a/plugins/modules/ec2_vpc_vpn.py b/plugins/modules/ec2_vpc_vpn.py
--- a/plugins/modules/ec2_vpc_vpn.py
+++ b/plugins/modules/ec2_vpc_vpn.py
@@ -288,7 +288,7 @@
     vgw_telemetry: [{
                      'outside_ip_address': 'string',
                      'status': 'up',
-                     'last_status_change': datetime(2015, 1, 1),
+                     'last_status_change': 'datetime(2015, 1, 1)',
                      'status_message': 'string',
                      'accepted_route_count': 123
                     }]


Test patch:
Repository: ansible-collections/community.aws

Issue:
Ansible kinesis stream paginated shards gets stuck in infinite loop
##### SUMMARY
For Kinesis streams with > 100 shards, `client.describe_stream(**params)['StreamDescription']` returns a paginated view, thus https://github.com/ansible-collections/community.aws/blob/master/plugins/modules/kinesis_stream.py#L363 will always be true and we'll be stuck in this infinite while loop since `has_more_shards` is always True. 

##### ISSUE TYPE
- Bug Report

##### COMPONENT NAME
Kinesis Streams

##### ANSIBLE VERSION
```
2.9.9
```

##### STEPS TO REPRODUCE
```
Hard to give you steps to reproduce this issue as it only happens with kinesis streams with > 100 shards. 
```

##### EXPECTED RESULTS
Creating kinesis streams should not cause a timeout!

##### ACTUAL RESULTS
```
Our deployment failed when trying to create a kinesis stream due to a timeout.
TASK [kinesis : create kinesis stream] *****************************************
--
  | fatal: [localhost]: FAILED! => {"changed": true, "msg": "Wait time out reached, while waiting for results", "result": {}, "success": false}
```



Discussion:

Resolving patch:
diff --git a/plugins/modules/kinesis_stream.py b/plugins/modules/kinesis_stream.py
--- a/plugins/modules/kinesis_stream.py
+++ b/plugins/modules/kinesis_stream.py
@@ -362,6 +362,8 @@ def find_stream(client, stream_name, check_mode=False):
                 )
                 shards.extend(results.pop('Shards'))
                 has_more_shards = results['HasMoreShards']
+                if has_more_shards:
+                    params['ExclusiveStartShardId'] = shards[-1]['ShardId']
             results['Shards'] = shards
             num_closed_shards = len([s for s in shards if 'EndingSequenceNumber' in s['SequenceNumberRange']])
             results['OpenShardsCount'] = len(shards) - num_closed_shards


Test patch:
Repository: ansible-collections/community.aws

Issue:
ec2_win_password returns success when it fails to decode the password
### SUMMARY
An unsuccessful decode call returns:

```
ok: [localhost] => {
        "changed": false,
        "invocation": {
            "module_args": {
               [trimmed]
            }
        },
        "win_password": ""
    }
```

I would expect it to return a failure state


Discussion:
Files identified in the description:
None

If these files are inaccurate, please update the `component name` section of the description or use the `!component` bot command.

[click here for bot help](https://github.com/ansible/ansibullbot/blob/master/ISSUE_HELP.md)
<!--- boilerplate: components_banner --->
@fd-davidtulloh: Greetings! Thanks for taking the time to open this issue. In order for the community to handle your issue effectively, we need a bit more information.

Here are the items we could not find in your description:
- issue type
- ansible version
- component name

Please set the description of this issue with this template:
https://raw.githubusercontent.com/ansible/ansible/devel/.github/ISSUE_TEMPLATE/bug_report.md

[click here for bot help](https://github.com/ansible/ansibullbot/blob/master/ISSUE_HELP.md)
<!--- boilerplate: issue_missing_data --->
@fd-davidtulloh I am working on this issue

Resolving patch:
diff --git a/plugins/modules/ec2_win_password.py b/plugins/modules/ec2_win_password.py
--- a/plugins/modules/ec2_win_password.py
+++ b/plugins/modules/ec2_win_password.py
@@ -187,13 +187,13 @@ def ec2_win_password(module):
         decrypted = None
 
     if decrypted is None:
-        module.exit_json(win_password='', changed=False)
+        module.fail_json(msg="unable to decrypt password", win_password='', changed=False)
     else:
         if wait:
             elapsed = datetime.datetime.now() - start
-            module.exit_json(win_password=decrypted, changed=True, elapsed=elapsed.seconds)
+            module.exit_json(win_password=decrypted, changed=False, elapsed=elapsed.seconds)
         else:
-            module.exit_json(win_password=decrypted, changed=True)
+            module.exit_json(win_password=decrypted, changed=False)
 
 
 def main():


Test patch:
Repository: ansible-collections/community.aws

Issue:
Wrong datatype in ecs_taskdefinition.containers 
https://github.com/ansible-collections/community.aws/blob/6eab8b0c0e805fe8881a834f4f1329193396fd0a/plugins/modules/ecs_taskdefinition.py#L324

In current version argument_spec is following:
`containers=dict(required=False, type='list', elements='str'),`

This causes dicts inside container list to be converted to strings.

If we select some example from documentation ...
```
- name: Create task definition
  community.aws.ecs_taskdefinition:
    family: nginx
    containers:
    - name: nginx
      essential: true
      image: "nginx"
      portMappings:
      - containerPort: 8080
        hostPort:      8080
      cpu: 512
      memory: 1024
    state: present
```

... then running it causes following exception:
```
  File "[..]/debug_dir/ansible_collections/community/aws/plugins/modules/ecs_taskdefinition.py", line 353, in main
    for environment in container.get('environment', []):
AttributeError: 'str' object has no attribute 'get'
```

To get examples (and container section) working, argument spec needs to be changed to following:
`containers=dict(required=False, type='list', elements='dict'),`


Discussion:
Yes, it seems to be an issue indeed.
The last time I used it was not in the community edition, but the built-in ecs_taskdefinition.py, in which it worked:
https://github.com/ansible/ansible/blob/stable-2.4/lib/ansible/modules/cloud/amazon/ecs_taskdefinition.py

As you can see from there the elements were not forced to be converted into any data format. Now the formatting is forced and it seems that they shouldn't be forced to be strings, but rather dicts if any as the reporter suggested.

Is there anything that we're missing here?
#251 
https://github.com/ansible-collections/amazon.aws/pull/184

Ah no...
the fix is easy...and not related by  what I've linked.  
`element=str` is just wrong.


Resolving patch:
diff --git a/plugins/modules/ecs_taskdefinition.py b/plugins/modules/ecs_taskdefinition.py
--- a/plugins/modules/ecs_taskdefinition.py
+++ b/plugins/modules/ecs_taskdefinition.py
@@ -47,7 +47,7 @@
             - A list of containers definitions.
         required: False
         type: list
-        elements: str
+        elements: dict
     network_mode:
         description:
             - The Docker networking mode to use for the containers in the task.
@@ -321,7 +321,7 @@ def main():
         family=dict(required=False, type='str'),
         revision=dict(required=False, type='int'),
         force_create=dict(required=False, default=False, type='bool'),
-        containers=dict(required=False, type='list', elements='str'),
+        containers=dict(required=False, type='list', elements='dict'),
         network_mode=dict(required=False, default='bridge', choices=['default', 'bridge', 'host', 'none', 'awsvpc'], type='str'),
         task_role_arn=dict(required=False, default='', type='str'),
         execution_role_arn=dict(required=False, default='', type='str'),


Test patch:
Repository: ansible-collections/community.aws

Issue:
add an MSK broker instance type
### Summary

Broker instances of MSK with the current provisioning type can be selected up to m5.24xlarge, but only m5.4xlarge is available for the ansible module.

### Issue Type

Feature Idea

### Component Name

msk_cluster

### Additional Information

<!--- Paste example playbooks or commands between quotes below -->
```yaml (paste below)
- name: create msk cluster
  msk_cluster:
    name: test-msk-cluster
    state: present
    version: "2.8.1"
    nodes: 2
    ebs_volume_size: 10
    subnets: subnet-temp
    wait: true
    instance_type: "kafka.m5.8xlarge"
```

recevied error message that
`FAILED! => {"changed": false, "msg": "value of instance_type must be one of: kafka.t3.small, kafka.m5.large, kafka.m5.xlarge, kafka.m5.2xlarge, kafka.m5.4xlarge, got: kafka.m5.8xlarge"}`

- https://docs.aws.amazon.com/msk/latest/developerguide/msk-create-cluster.html#broker-instance-types


### Code of Conduct

- [X] I agree to follow the Ansible Code of Conduct


Discussion:

Resolving patch:
diff --git a/plugins/modules/msk_cluster.py b/plugins/modules/msk_cluster.py
--- a/plugins/modules/msk_cluster.py
+++ b/plugins/modules/msk_cluster.py
@@ -54,6 +54,17 @@
             - kafka.m5.xlarge
             - kafka.m5.2xlarge
             - kafka.m5.4xlarge
+            - kafka.m5.8xlarge
+            - kafka.m5.12xlarge
+            - kafka.m5.16xlarge
+            - kafka.m5.24xlarge
+            - kafka.m7g.large
+            - kafka.m7g.xlarge
+            - kafka.m7g.2xlarge
+            - kafka.m7g.4xlarge
+            - kafka.m7g.8xlarge
+            - kafka.m7g.12xlarge
+            - kafka.m7g.16xlarge
         default: kafka.t3.small
         type: str
     ebs_volume_size:
@@ -662,6 +673,17 @@ def main():
                 "kafka.m5.xlarge",
                 "kafka.m5.2xlarge",
                 "kafka.m5.4xlarge",
+                "kafka.m5.8xlarge",
+                "kafka.m5.12xlarge",
+                "kafka.m5.16xlarge",
+                "kafka.m5.24xlarge",
+                "kafka.m7g.large",
+                "kafka.m7g.xlarge",
+                "kafka.m7g.2xlarge",
+                "kafka.m7g.4xlarge",
+                "kafka.m7g.8xlarge",
+                "kafka.m7g.12xlarge",
+                "kafka.m7g.16xlarge",
             ],
             default="kafka.t3.small",
         ),


Test patch:
Repository: ourownstory/neural_prophet

Issue:
benchmark typing error
### Discussed in https://github.com/ourownstory/neural_prophet/discussions/449

<div type='discussions-op-text'>

<sup>Originally posted by **akthammomani** October 23, 2021</sup>
@ourownstory, I went a head and installed the Library again using below in a fresh env: 

```
git clone <copied link from github>
cd neural_prophet
pip install .
```

so when trying below:
```
from neuralprophet import NeuralProphet, set_log_level
from neuralprophet.benchmark import Dataset, NeuralProphetModel, SimpleExperiment, CrossValidationExperiment
```

I started to see a new error as shown below: 

```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_43524/177920820.py in <module>
      1 from neuralprophet import NeuralProphet, set_log_level
----> 2 from neuralprophet.benchmark import Dataset, NeuralProphetModel, SimpleExperiment, CrossValidationExperiment

~\Anaconda3\envs\nprophet_v1\lib\site-packages\neuralprophet\benchmark.py in <module>
    158 
    159 
--> 160 @dataclass
    161 class SimpleBenchmark:
    162     """

~\Anaconda3\envs\nprophet_v1\lib\site-packages\neuralprophet\benchmark.py in SimpleBenchmark()
    171     """
    172 
--> 173     model_classes_and_params: List[tuple[Model, dict]]
    174     datasets: List[Dataset]
    175     metrics: list[str]

TypeError: 'type' object is not subscriptable
```

Please let me know if you have any suggestions.... Thank you very much

BR//Aktham


</div>


Discussion:

Resolving patch:
diff --git a/neuralprophet/benchmark.py b/neuralprophet/benchmark.py
--- a/neuralprophet/benchmark.py
+++ b/neuralprophet/benchmark.py
@@ -1,14 +1,17 @@
 from dataclasses import dataclass, field
 from typing import List, Optional, Tuple, Type
 from abc import ABC, abstractmethod
+import logging
 
 import pandas as pd
 import numpy as np
 from neuralprophet import NeuralProphet
 
-
 NeuralProphetModel = NeuralProphet
 
+log = logging.getLogger("NP.benchmark")
+log.warning("Benchmarking Framework is not covered by tests. Please report any bugs you find.")
+
 
 @dataclass
 class Dataset:
@@ -170,9 +173,9 @@ class SimpleBenchmark:
     >>> results_train, results_val = benchmark.run()
     """
 
-    model_classes_and_params: List[tuple[Model, dict]]
+    model_classes_and_params: List[Tuple[Model, dict]]
     datasets: List[Dataset]
-    metrics: list[str]
+    metrics: List[str]
     test_percentage: float
 
     def setup_experiments(self):


Test patch: