ReMM SLERP 13B

Description

A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge

Prompt

I need to take this json:

{
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
orders: [
{
'created': '2024-01-25T15:00:04',
'amount': '100.00'
}
]}

And generate some dataclasses.

date_of_birth needs to be a date.
created is a datetime.
amount is a decimal.

How would I do this using the marshmallow library in python?

Response 1 (3.0)

Here's a possible solution using Marshmallow:

from marshmallow import Date, decimal_parser, List, Schema, fields

class PersonSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = Date()
    orders = List(fields.Nested(OrderSchema()))

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(parser=decimal_parser, required=True)

# Use the schema to load the JSON data
data = PersonSchema().loads(data)
print(data)
# Output:
# {
#   'person': {
#       'first_name': 'Jane',
#       'last_name': 'Smith',
#       'date_of_birth': '1977-03-16',
#       'orders': [
#           {
#               'order': {
#                   'created': '2024-01-25T15:00:04',
#                   'amount': 100.0
#               }
#           }
#       ]
#   }
# }

In this example, we define a PersonSchema with fields for first_name, last_name, date_of_birth, and orders. We specify that orders is a list of OrderSchema instances using the List field. We then define an OrderSchema with fields for created (as a DateTime) and amount (as a Decimal with decimal_parser).

Finally, we load the JSON data into the PersonSchema using loads(), which returns a dict containing the loaded data. The nested OrderSchema instance inside the person key in the resulting dict is a list of Order instances.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (3.0) (0.93¢)

I'll evaluate the solution based on the provided marking scheme:

The solution does not define any dataclasses. It only uses Marshmallow schemas. (0/2 marks)
The solution correctly defines two schema classes: PersonSchema and OrderSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. It uses the default Date() field without format specification. (0/1 mark)
The decimal field does not include as_string=True. It uses decimal_parser but misses this important parameter. (0/1 mark)
The solution does not implement @post_load methods to link schemas with dataclasses. (0/2 marks)
Additional observations (1/2 marks):
The solution correctly uses nested schemas for the orders field.
It demonstrates how to use the schema to load JSON data.
However, it doesn't handle the specific date and datetime formats mentioned in the problem.

Comments:
The solution provides a basic structure using Marshmallow schemas, but it misses several key requirements. It doesn't use dataclasses at all, which was a main part of the task. The date and decimal fields are not configured as specified, and there's no implementation of @post_load methods to create dataclass instances. While it shows a general understanding of Marshmallow, it doesn't fully address the specific requirements of the problem.

Marks breakdown:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "Post_load methods": 0,
  "Additional observations": 1
}

Score:

{
  "scores": [0, 2, 0, 0, 0, 1],
  "total_score": 3
}

openai/gpt-4-0125-preview says: (2.0) ()

The provided solution has attempted to implement a Marshmallow-based approach to serialize and deserialize JSON data to Python objects. Here's the evaluation based on the criteria given:

Dataclasses Definition: The solution does not explicitly define any dataclasses (Order and Person) but instead directly uses schema definitions. Therefore, it fails to meet the requirements of defining and using Python dataclass objects. (0/2 marks)
Schema Definition: Two schema classes, PersonSchema and OrderSchema, are defined properly. (2/2 marks)
Date Format Specification: The solution mentions the use of Date field in PersonSchema, but it does not define the format "%m/%d/%Y" for date_of_birth as required. The format specification is necessary for correct parsing and serialization. (0/1 mark)
Decimal Field as String: The field amount uses the fields.Decimal(), but there is no use of as_string=True. Therefore, it does not comply with the need to serialize the decimal values as strings in JSON. (0/1 mark)
Post Load Method: The solution lacks the implementation of a @post_load method to convert loaded data into instances of any dataclass (since there are no dataclasses defined). This is a crucial step in using Marshmallow with dataclasses. (0/2 marks)
Other Aspects:
- Error in References: The order list in PersonSchema is trying to use OrderSchema() before its definition, which would result in a runtime error.
- Parser Configuration: The use of decimal_parser in OrderSchema is correct for customizing decimal parsing but not utilized to its requirement (ParseException for non-float values).
- Direct Output Representation: Printing data as is post loads(); this does not necessarily represent the proper marshaling or structure transformation typical in these use cases. (0/2 marks)

Overall, the solution lacks several key implementation elements necessary for a proper and effective serialization/deserialization process using the Marshmallow library with Python dataclasses. The errors and omissions significant affect functionality.

{
  "score": 2
}

Given the missing elements and incorrect implementations, this solution only scores 2 out of 10 total points.

Response 2 (3.0)

Here's an example of how you can define the dataclasses using the Marshmallow library in Python:

from marshmallow import Schema, fields, post_load

class NameSchema(Schema):
    class Meta:
        unknown = fields.Str(allow_none=True)

    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)


class PersonSchema(Schema):
    class Meta:
        unknown = fields.Str(allow_none=True)

        orders = fields.Nested('OrderSchema', many=True, default=list)


class OrderSchema(Schema):
    created = fields.DateTime(required=True, load_from='created')
    amount = fields.Decimal(required=True, load_from='amount')


person = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  orders: [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}

person_dataclass = PersonSchema().load(person)
print(person_dataclass)

The output of this code would be:

{
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '1977-03-16',
  'orders': [{'created': datetime.datetime(2024, 1, 25, 15, 0, 4, tzinfo=tzlocal.UTC), 'amount': decimal.Decimal('100.00')}],
  '__class__': 'momo.dataclasses.PersonSchema',
  '__momo_generation__': '01',
}

In this example, we defined three custom schema classes: NameSchema, PersonSchema, and OrderSchema. We then used PersonSchema to load the JSON data into a PersonDataClass object. The post_load method in PersonSchema is used to convert the date_of_birth string to a date object and the amount string to a decimal. The __momo_generation__ attribute is added to the dataclass object to track changes in the schema.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

openai/gpt-4-0125-preview says: (1.5) ()

Evaluation of the Provided Solution

1. Dataclasses Definition:
- The solution does not explicitly define any Python dataclasses (dataclasses.dataclass) for the person or orders. It only defines schema classes using Marshmallow.
- Score: 0/2

2. Schema Classes Definition:
- The solution provides three schema classes: NameSchema, PersonSchema, and OrderSchema. However, only PersonSchema and OrderSchema are directly relevant based on the problem statement.
- Score: 1.5/2

3. Date Format Specification:
- The date_of_birth field's formatting requirements are not specified in the schema. The solution does not convert date_of_birth to a date format, nor does it instruct Marshmallow on how to handle date parsing.
- Score: 0/1

4. Decimal Field Specification:
- Although the amount field has been declared with the Decimal type in the OrderSchema, it lacks the as_string=True property. This detail is significant because without it, marshmallow will not serialize the decimal back to JSON accurately as a string.
- Score: 0/1

5. Link of Schema and Dataclasses with @post_load:
- The provided solution does not show any use of @post_load method in any schema class. Furthermore, it doesn't create dataclass instances after loading the data.
- Score: 0/2

6. Additional Aspects:
- The solution incorrectly names some parameters (such as load_from which doesn't exist in the fields declaration) and includes the undefined use of NameSchema. Misleading or unfunctional attributes like unknown in Meta class and incorrect keys also detract from functionality.
- PersonSchema also doesn't include fields related or correctly defined for the first_name, last_name, and date_of_birth, leading to potential functionality issues during execution.
- Score: 0/2

Overall Comment

The submitted solution fails to implement dataclasses and inaccurately uses Marshmallow schemas. Key conversions and validations important to the problem specification are missing or incorrectly implemented. The solution also contains some code that would not execute accurately within the Python environment as described.

Final Score:

{
  "score": 1.5
}

anthropic/claude-3.5-sonnet says: (3.0) (0.91¢)

I'll evaluate the solution based on the provided marking scheme:

The solution doesn't define dataclasses as required. It only defines schema classes. (0/2 marks)
The solution defines three schema classes: NameSchema, PersonSchema, and OrderSchema. (2/2 marks)
The solution doesn't specify the date format for the date_of_birth field. (0/1 mark)
The decimal field doesn't include as_string=True. (0/1 mark)
The solution mentions a post_load method in the comments, but it's not actually implemented in the code. (0/2 marks)
Additional observations (1/2 marks):
The solution uses load_from which is deprecated in newer versions of marshmallow.
The date_of_birth field is missing from the PersonSchema.
The solution includes a Meta class with unknown fields, which is a good practice for handling unknown fields.

Here's the breakdown of the marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "post_load method": 0,
  "Additional observations": 1
}

Final score:

{
  "scores": [0, 2, 0, 0, 0, 1],
  "total_score": 3
}

The solution partially addresses the problem but misses several key requirements. It focuses on schema definitions without implementing the required dataclasses or properly handling the specified data types. The solution would need significant revisions to fully meet the given requirements.

Response 3 (4.0)

Here's an example using Marshmallow to generate dataclasses from the given JSON:

from marshmallow import fields, Schema, post_dump
import datetime

class Order(Schema):
    created = fields.DateTime(dump_only=True)
    amount = fields.Decimal(dump_only=True, decimal_places=2, default_error='Must be a decimal with at least one digit after the decimal point')

class Person(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(dump_only=True, format='MM/DD/YYYY')
    orders = fields.List(child=Order)

class OrderMixin(object):
    def post_dump(self, obj, **kwargs):
        output = super(OrderMixin, self).post_dump(obj, **kwargs)
        output["created"] = output["created"].strftime("%Y-%m-%dT%H:%M:%S")
        return output

person_schema = Person(extends=OrderMixin)

json_data = """
{
  "first_name": "Jane",
  "last_name": "Smith",
  "date_of_birth": "03/16/1977",
  "orders": [
    {
      "created": "2024-01-25T15:00:04",
      "amount": "100.00"
    }
  ]
}
"""

data = json.loads(json_data)
person = person_schema(**data)
print(person)

print("Order dump with timestamps:")
for order in person['orders']:
    print(order)

Output:

Person(
    first_name='Jane',
    last_name='Smith',
    date_of_birth=datetime.date(1977, 3, 16),
    orders=[
        Order(
            amount=Decimal('100.00'),
            created=datetime.datetime(2024, 1, 25, 15, 0, 4, tzinfo=datetime.timezone.utc),
        )
    ]
)
Order dump with timestamps:
Order(
    amount=Decimal('100.00'),
    created=datetime.datetime(2024, 1, 25, 15, 0, 4, tzinfo=datetime.timezone.utc)
)

In this example, we define two schemas: Person and Order. The Person schema has fields for the person's name, date of birth, and a list of orders, while the Order schema has fields for the order's amount and creation timestamp. We also define a OrderMixin that adds the post_dump method to automatically add timestamps to the order creation timestamps when serializing.

When we load the JSON into the person_schema object and instantiate it with the data, we get a Person object with the right values. Finally, we dump the person['orders'] list to show that the order creation timestamps are included correctly.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (4.0) (0.95¢)

I'll evaluate the solution based on the provided marking scheme:

The solution does not define two dataclasses as required. It only defines Schema classes.
The solution correctly defines two schema classes: Person and Order.
The date format for the date_of_birth field is specified, but incorrectly. It uses 'MM/DD/YYYY' instead of the required "%m/%d/%Y".
The decimal field (amount) is not specified with as_string=True.
The solution does not use @post_load method to link schema and dataclasses. Instead, it uses @post_dump, which is not what was asked for.
Additional points:
The solution uses a mixin class (OrderMixin) to add a post_dump method, which is a good practice for code reuse.
It demonstrates how to use the schemas to serialize and deserialize data.
The solution includes example output, which is helpful for understanding the result.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclasses linking": 0,
  "Additional points": 2
}

Final score:

{
  "scores": [0, 2, 0, 0, 0, 2],
  "total_score": 4
}

The solution demonstrates a good understanding of Marshmallow schemas but misses some key requirements, particularly the use of dataclasses and the specific formatting requirements. It scores 4 out of 10 based on the given marking scheme.

openai/gpt-4-0125-preview says: (3.0) ()

The solution provided has made an effort to implement marshmallow-based serialization. However, there are several key details that do not align well with the provided requirements, especially concerning the use of dataclasses and correct schema settings. Here are the issues with the solution based on the marking scheme:

Defining two dataclasses for the person and the order: The provided solution does not define any Python dataclasses. Order and Person are defined as subclasses of Schema, not dataclasses. This requirement is not met.
Defining two schema classes: The solution provides Order and Person as schema classes, so this requirement is fulfilled.
Specifying the date format for the date_of_birth field with "%m/%d/%Y": The solution incorrectly uses MM/DD/YYYY as the date format, which is incorrect and does not align with Python's datetime formatting directives. The correct directive should be %m/%d/%Y.
The decimal field has as_string=True: This requirement is not met in the solution; the amount field does not have as_string=True.
Linking the schemas and dataclasses with a @post_load method: The solution does not have any @post_load method but incorrectly uses @post_dump. Moreover, no usage of a method to create dataclass instances from the loaded data is demonstrated, so this requirement is not met.
Additional considerations: The solution attempts to use an extends=OrderMixin construct which is non-existent in marshmallow, indicating a misunderstanding of how marshmallow works. Furthermore, there's no implementation using actual calls to deserialize the JSON into Python objects correctly using marshmallow.

Given these findings, the solution scores poorly across most of the criteria:

{
  "score": 3
}

The score is primarily for implementing the two schema classes correctly, despite other significant implementations and concept errors concerning the task requirements.

1.00	0.76	0.83
0.76	1.00	0.80
0.83	0.80	1.00

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