Noromaid 20B

Description

A collab between IkariDev and Undi. This merge is suitable for RP, ERP, and general knowledge.

#merge #uncensored

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?

You can use the Marshmallow library to automatically generate dataclasses from your given JSON by performing the following steps:

  1. Install marshmallow by running pip install marshmallow.
  2. Import it like so:
from marshmallow import Schema, ValidationError
from marshmallow.utils import dump, load
  1. Define your Order schema:
class OrderSchema(Schema):
    class Meta:
        strict = True

        dump_only_defaults = True
        exclude_none = True

    created = DateTime()
    amount = Decimal(precision=2, places=2)
  1. Define your User schema:
class UserSchema(Schema):
    class Meta:
        strict = True
        dump_only_defaults = True
        exclude_none = True
    first_name = str
    last_name = str
    date_of_birth = Date(format='%m/%d/%Y')
    orders = OrderSchema(many=True)
  1. Dump the JSON into a Python object using the load function:
user_json = '''
{
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  orders: [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}'''

user_obj = load(user_json)

If you encounter any errors during

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) (0.96¢)

Let's evaluate this solution based on the provided marking scheme:

  1. Defining two dataclasses:
    The solution doesn't define any dataclasses. It only defines schemas.

  2. Defining two schema classes:
    The solution correctly defines two schema classes: OrderSchema and UserSchema.

  3. Specifying date format for date_of_birth:
    The solution correctly specifies the date format for date_of_birth as Date(format='%m/%d/%Y').

  4. Decimal field with as_string=True:
    The solution defines the amount field as a Decimal, but doesn't include the as_string=True parameter.

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't include any @post_load methods to link schemas with dataclasses.

  6. Other observations:

  7. The solution imports dump and load from marshmallow.utils, which is incorrect. These should be methods on the Schema instances.
  8. The strict parameter in Meta classes is deprecated in newer versions of marshmallow.
  9. The solution attempts to use load function directly on a JSON string, which is not the correct usage in marshmallow.

Here's the breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal as_string": 0,
  "post_load method": 0,
  "Other aspects": 1
}

Final score:

{
  "scores": [0, 2, 1, 0, 0, 1],
  "total_score": 4
}

The solution demonstrates some understanding of marshmallow schemas but misses several key requirements, particularly the use of dataclasses and the correct implementation of marshmallow features. It also contains some errors in importing and using marshmallow functions. Overall, it provides a partial solution to the problem but needs significant improvements to fully meet the requirements.

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

The provided solution evaluation against the marking scheme is as follows:

  1. Dataclasses Definition: The solution fails to define actual Python dataclasses for User and Order. It uses schemas (which are different from dataclasses), so it does not meet the criteria. (0/2)

  2. Schema Classes Definition: The solution includes definitions for UserSchema and OrderSchema, meeting this requirement. (2/2)

  3. Date Format Specification: The UserSchema incorrectly specifies the date format directly in the Date field declaration, but it should be associated with the field correctly such as fields.Date(format='%m/%d/%Y'). The correct functionality is aimed at but not properly implemented due to syntax errors. (0.5/1)

  4. Decimal as String: The solution does not include the as_string=True parameter in the Decimal field declaration for the amount. This is essential for ensuring that the decimal amount is handled as a string. (0/1)

  5. @post_load Method for Linking Schemas and Dataclasses: The solution does not contain any post_load methods. Such methods are crucial for creating dataclass instances from the schema load operations. (0/2)

  6. Additional Considerations: Other elements like imports have errors (dump and load aren't used correctly from marshmallow.utils—they should be used from the specific schema instance). It also lacks comprehensive error handling (such as ValidationError handling) which would align with pragmatic usage. The solution tries to load JSON string improperly, leading to potential syntax issues with single quotes in JSON and Python dictionary syntax confusion. (0.5/2)

Overall, whilst the solution attempts to leverage the key features of Marshmallow for schema design and ties in important data types, it's plagued by syntax and conceptual errors which make it non-functional as is.

{
  "score": 3
}

This score reflects partial success in schema creation, but failures in defining dataclasses, correctly linking schemas to data types, and missing or incorrect implementation of @post_load, date handling, and proper JSON parsing.

I'm happy to help you with that! Here's a sample implementation using marshmallow for your specificJSON:

from marshmallow import schema, attach_many, Field, int, date, decimal, date_range, mapper, Sequence


class CustomerDessertOrder(mapper.Model):
    created = mapper. DateTime()
    amount = mapper. Float()


class Customer(mapper.Model):
    first_name = Field(required=True, meta=mapper.orm_unique_constraint())
    last_name = Field(required=True, meta=mapper.orm_unique_constraint())
    date_of_birth = date_range(lower=date(year=1970, month=1, day=1), upper=date(year=2040, month=12, day=31))
    orders = attach_many(CustomerDessertOrder, 'created', many=True)

You'll first define two classes:
- Customer is the main class that holds all the attributes of your JSON object. It includes the required first_name, last_name, and date_of_birth fields as properties. The date_of_birth field uses a date range to check for validity (minimum and maximum year, month, and day).
- CustomerDessertOrder is a class that represents each order made by a customer. It has the attributes created (a datetime) and amount (a decimal) fields.

Then, you'll tell marshmallow how to parse and serialize the JSON data using mappers. mapper.Model is a base class for all of your models. The `__

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: (2.0) (1.00¢)

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

  • The solution defines two classes, Customer and CustomerDessertOrder, which could be considered as dataclasses. However, they are not using Python's @dataclass decorator, so this is only partially correct. (1 mark out of 2)

  • The solution does not explicitly define schema classes. It uses mapper.Model, which might be a custom implementation, but it's not the standard marshmallow approach. (0 marks out of 2)

  • The solution does not specify the date format for the date_of_birth field as required (format="%m/%d/%Y"). Instead, it uses a date_range validator. (0 marks out of 1)

  • The solution does not use as_string=True for the decimal field. It uses mapper.Float() instead of Decimal. (0 marks out of 1)

  • The solution does not include @post_load methods to link schema and dataclasses. (0 marks out of 2)

  • For additional points:

  • The solution attempts to use some form of ORM-like setup with mapper.Model and constraints.
  • It includes a date range validator for date_of_birth.
  • It uses attach_many for the orders relationship.
    (1 mark out of 2)

Comments:
The solution provided doesn't fully align with the requirements. It seems to be using a custom mapper implementation rather than standard marshmallow. It misses key elements like explicit schema definitions, proper field types (especially for decimal and datetime), and the required date format. The approach taken might be valid for some scenarios, but it doesn't meet the specific requirements outlined in the problem statement.

Here's the breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 0,
  "Date format": 0,
  "Decimal field": 0,
  "Post_load method": 0,
  "Additional features": 1
}

Final score:

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

openai/gpt-4-0125-preview says: (0.0) ()

This solution that attempts to use marshmallow to load and validate a JSON data structure does not align well with the prescribed task. Let's evaluate it based on the marking scheme:

  1. Definition of two dataclasses, one for the person and one for the order:

  2. The solution incorrectly uses mapper.Model which is not a standard feature of marshmallow nor a part of Python's dataclasses. The appropriate setup would involve Python's dataclasses.dataclass decorator. (0/2)

  3. Definition of two schema classes, one for the person and one for the order:

  4. The solution makes no use of marshmallow's Schema class. It instead incorrectly uses a fictitious mapper.Model. (0/2)

  5. Date format specification for the date_of_birth field:

  6. The solution defines a range using date_range but does not format date_of_birth as required with %m/%d/%Y. (0/1)

  7. Decimal field with as_string=True:

  8. The solution treats amount as a float and does not utilize marshmallow's fields.Decimal with an as_string=True attribute, which would ensure the amount is serialized as a string. (0/1)

  9. Linking schema and dataclasses with a @post_load method on the schema classes:

  10. There is no Schema class and no use of @post_load method in the given solution. Thus, the linkage between serialization and data classes is absent. (0/2)

  11. Additional elements:

  12. The misunderstanding of marshmallow's correct usage and incorrect references to non-existent classes/methods like mapper.Model and attach_many imply a lack of understanding of both Python data handling and the marshmallow library. (0/2)

Total Score:

{
  "score": 0
}

The solution provided falls short on all evaluation criteria mainly due to the confusion around the usage of non-existent marshmallow functionalities and the lack of understanding of how to implement the required features using the library and Python's built-in capabilities.

Sure, here's the code using the marshmallow library to create the desired dataclasses:

from marshmallow import *
from marshmallow import fields
from marshmallow_date

class Person(Resource):
first_name = Schemaweet(str)
last_name = Schemaweet(str)
date_of_birth = Schemaweet(Date(floating=True))
orders = Schemaweet(Order[], required=True)

class Order(Resource):
created = Schemaweet(Date(floating=True))
amount = Schemaweet(decimal)

class PersonDTO(Person):
class Meta:
strict = True
smart_key = True
marshal_postprocess = complain

    dump_postprocess = badger_dumper
    marshmallow_class_only = True

class OrderDTO(Order):
class Meta:
marshmallow_class_only = True

schema = Schema(Person=PersonDTO, Order=OrderDTO, fields_dict=True)

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: (1.0) (0.92¢)

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

  1. The solution defines two classes (Person and Order), but they are not dataclasses. They appear to be custom classes using a "Resource" base class, which is not standard for dataclasses. (0/2 marks)

  2. The solution defines two schema-like classes (PersonDTO and OrderDTO), but they are not proper marshmallow Schema classes. (0/2 marks)

  3. The solution does not specify the date format for the date_of_birth field. (0/1 mark)

  4. The decimal field is not properly defined with as_string=True. (0/1 mark)

  5. There is no @post_load method on the schema classes to link them with dataclasses. (0/2 marks)

  6. Other observations (1/2 marks):

  7. The solution attempts to use some custom decorators or classes (Schemaweet, Resource) which are not standard marshmallow components.
  8. There's an attempt to define metadata for the DTO classes, but it's not using marshmallow's standard approach.

Here's the breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 0,
  "Date format": 0,
  "Decimal field": 0,
  "Post load method": 0,
  "Other observations": 1
}

Score:

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

Comments:
The provided solution shows an attempt to solve the problem, but it deviates significantly from the expected use of marshmallow and dataclasses. It introduces custom classes and decorators that are not part of the standard marshmallow library, making it difficult to evaluate against the given criteria. The solution doesn't properly implement dataclasses, marshmallow schemas, or the required field specifications. It would need substantial revision to meet the requirements of the problem statement.

openai/gpt-4-0125-preview says: (0.0) ()

The solution provided attempts to use the Marshmallow library to define dataclasses and their respective schemas for JSON data processing. Let's evaluate the solution against the provided criteria.

  1. Defining two dataclasses:
  2. The solution defines classes Person and Order, but they are incorrectly derived from Resource, which is not a standard part of Python dataclasses or Marshmallow schema. There's also a misuse of Schemaweet (non-existent class or method) for field definitions.
  3. Correct Approach: Use Python's dataclasses.dataclass to define these structures.

  4. Points: 0/2

  5. Defining two schema classes:

  6. The classes PersonDTO and OrderDTO are created but inherit directly from Person and Order. This isn't the correct use of Marshmallow, which typically uses separate Schema classes for defining how to serialize and deserialize the data.

  7. Points: 0/2

  8. Date format specification for date_of_birth:

  9. The date_of_birth field in Person class uses Schemaweet(Date(floating=True)), which doesn't properly specify the date format and has typographical errors.
  10. Correct Field: fields.Date(format="%m/%d/%Y")

  11. Points: 0/1

  12. Decimal field needs as_string=True:

  13. The amount field in Order is defined as Schemaweet(decimal), which is not syntactically correct in Python and does not use the as_string=True parameter.
  14. Correct Field: fields.Decimal(as_string=True)

  15. Points: 0/1

  16. Linking schema and dataclasses with @post_load method:

  17. There's no use of the @post_load method in the provided schemas to properly construct dataclass instances from the parsed data.

  18. Points: 0/2

  19. Additional considerations:

  20. There are many syntactical and conceptual errors such as the use of non-existent classes (Resource, Schemaweet), incorrect inheritance, and lack of real Marshmallow syntax usage (Schema, proper field types). The solution is fundamentally flawed and lacks understanding of Python's dataclasses and Marshmallow library usage.
  21. Points: 0/2

Overall, the provided solution does not meet the requirements and uses incorrect syntax and concepts from the Marshmallow library. There are critical errors in basic understanding and implementation.

{
  "score": 0
}
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