Pygmalion: Mythalion 13B

Description

A blend of the new Pygmalion-13b and MythoMax. #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 (2.0)

Here's an example of how you can use the Marshmallow library to parse the JSON data into Python dataclasses:

from marshmallow import Schema, fields, post_dump

from datetime import datetime


class PersonSchema(Schema):
    class Meta:
        fields = ('first_name', 'last_name', 'date_of_birth', 'orders')


class OrderSchema(Schema):
    class Meta:
        fields = ('created', 'amount')


ordered_json: PersonSchema = PersonSchema(load_kwargs={"date_format": "MM/dd/yyyy"})


def post_dump(self, data, many=False):
    return json.dumps({
        "date_of_birth": datetime.strptime(data["date_of_birth"], "%m/%d/%Y")
    })


PersonSchema.post_dump = post_dump

Then you can create a Person instance from the JSON data like this:

person = PersonSchema().load(data)
order = OrderSchema().load(person.orders[0])
order = OrderSchema().dump(order)

Make sure to include the PersonSchema.post_dump = post_dump line if you're using Marshmallow >= 4.0.0.0. This will help format the date of birth as a readable string when serializing to JSON.

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) (1.01¢)

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

Defining two dataclasses:
The solution doesn't define any dataclasses. It only defines schemas.
Marks: 0/2
Defining two schema classes:
The solution correctly defines PersonSchema and OrderSchema.
Marks: 2/2
Specifying date format for date_of_birth:
The solution attempts to specify a date format, but it's incorrect. It uses "MM/dd/yyyy" instead of "%m/%d/%Y".
Marks: 0/1
Decimal field with as_string=True:
The solution doesn't specify any field types, including the decimal field.
Marks: 0/1
Linking schema and dataclasses with @post_load:
The solution doesn't use @post_load to link schemas with dataclasses.
Marks: 0/2
Additional considerations:
The solution attempts to implement a post_dump method, which wasn't requested.
The implementation of post_dump is incorrect and wouldn't work as intended.
The solution doesn't handle the 'created' field as a datetime.
The solution doesn't handle the 'amount' field as a decimal.
Marks: 0/2

Breakdown of marks:

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

Score:

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

Comments:
The solution falls short in several areas. It doesn't define dataclasses, which was a key requirement. While it does define schema classes, it doesn't correctly implement the field types or formats. The attempt to use post_dump instead of post_load is misplaced and incorrectly implemented. The solution doesn't address the requirements for handling the datetime and decimal fields. Overall, the solution needs significant improvement to meet the specified requirements.

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

The provided solution does not fully meet the requirements specified in the problem statement nor follow the marking scheme properly. Here's the detailed evaluation based on the criteria provided:

Definition of two dataclasses (one for the person and one for the order):
The solution does not define any Python dataclasses. It only defines schema classes.
Score: 0/2
Definition of two schema classes (one for the person and one for the order):
Two schema classes (PersonSchema and OrderSchema) are defined.
Score: 2/2
Specification of the date format for the date_of_birth field:
The date_format is mentioned in a load_kwargs argument which is incorrect and not effective in marshmallow for field-level formatting.
Score: 0/1
The decimal field needs as_string=True:
The amount field in the OrderSchema does not specify as_string=True for the Decimal type.
Score: 0/1
Schema and dataclasses should be linked with a @post_load method on the schema classes:
A post_dump method is incorrectly implemented instead of @post_load.
The post_dump method incorrectly declares and does not fit the requirement to convert fields upon loading.
Score: 0/2
Anything else:
Attempts to integrate date and decimal handling, albeit incorrectly.
Mention of post_dump adjustments for newer versions, although it uses the deprecated decoration style and the wrong method (post_dump instead of @post_dump).
Score: 1/2

Evaluation Summary:

The solution failed to correctly implement dataclasses, properly handle date and decimal conversion, and correctly use the post_load method for schema loading transformation. The schema definition is correct, but its integration with dataclasses and field formatting is lacking.

{
  "score": 3
}

Response 2 (3.0)

You can use the marshmallow library to convert the given JSON data into dataclasses using the following code:

from marshmallow import fields, Schema


class PersonSchema(Schema):

    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()


    class Meta:
        ordered = True


class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Float()

    class Meta:
        ordered = True


person_schema = PersonSchema(dataclass=Person)
order_schema = OrderSchema(dataclass=Order)

Now you can create instances of these classes and use them to unmarshal the JSON data:

person = person_schema.load(json_data)
order = order_schema.load(json_data['orders'][0])

The resulting dataclasses will have the desired attributes:

In [2]: person
Out[2]: <class '__main__.Person'>
    first_name: Jane
    last_name: Smith
    date_of_birth: datetime.date(19779, 3, 16)


In [3]: order
Out[3]: <class '__main__.Order'>
    created: datetime.datetime(2024, 1, 25, 15, 0, 0, 4)
    amount: 100.0

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) (0.90¢)

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

The solution defines two schema classes (PersonSchema and OrderSchema), but it doesn't explicitly define the dataclasses. It attempts to create them implicitly using the dataclass parameter in schema instantiation, which is not the correct approach.
The solution correctly defines two schema classes, PersonSchema and OrderSchema.
The solution doesn't specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y").
The solution uses fields.Float() for the amount field instead of fields.Decimal(as_string=True).
The solution doesn't include @post_load methods to link the schemas with dataclasses.
Additional points:
The solution correctly uses fields.DateTime() for the 'created' field.
It includes class Meta: ordered = True in both schema classes, which is a good practice.
The solution demonstrates how to use the schemas to load data, which is helpful.
However, it doesn't handle the nested 'orders' field in the PersonSchema.

Here's the breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format specification": 0,
  "Decimal field with as_string": 0,
  "Post_load method": 0,
  "Additional points": 1
}

Final score:

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

The solution demonstrates a basic understanding of using marshmallow for data serialization but misses several key requirements and contains some errors. It provides a good starting point but needs significant improvements to fully meet the specifications.

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

The provided solution attempts to use the Marshmallow library to deserialize JSON data into Python dataclasses, yet the solution has multiple significant mistakes and omissions. Let's critically evaluate it based on the mentioned criteria:

Dataclasses Definition: The solution does not include actual Python dataclass implementations for both Person and Order. All operations using dataclasses such as dataclass=Person and dataclass=Order are invalid because those classes are not defined in the provided code.
Score: 0/2
Schema Classes Definition: The schema classes PersonSchema and OrderSchema are defined appropriately with fields corresponding to JSON structure.
Score: 2/2
Date Format Specification: The solution does not specify the required date formats. The fields.Date() and fields.DateTime() in PersonSchema and OrderSchema respectively, need parameter adjustments to accommodate specific formats mentioned in the problem (such as format="%m/%d/%Y" for date_of_birth).
Score: 0/1
Decimal Field Specification: The solution uses fields.Float() for the amount. The correct method would have been using fields.Decimal(as_string=True) to ensure the amount is handled as a decimal and returned as a string.
Score: 0/1
Linking Schema and Dataclasses with @post_load: The @post_load decorator is not used, and thus there's no method to create instances of the required dataclasses after loading the data. This is paramount for linking the schema load with concrete class instantiation.
Score: 0/2
Additional Observations:
The use of dataclass keyword in Marshmallow schemas is incorrect as demonstrated; Marshmallow does not use such a keyword directly in schema definitions.
Proper instantiation or manipulation methods exemplifying how the JSON is parsed or how errors are handled are missing or incorrect.
Score: 0/2

Overall, the given solution did not meet most of the key requirements and contained errors about the correct usage of Marshmallow in handling complex types and custom dataclasses.

Final Score:

{
  "score": 2
}

Response 3 (3.0)

To create dataclasses from the provided JSON using Marshmallow, you can do the following:

from datetime import datetime
from marshmallow import Schema, fields

import json


class PersonSchema(Schema):
    class Meta:
        fields = ('first_name', 'last_name', 'date_of_birth', 'orders',)


    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.DateTime()
    orders = fields.List(OrderSchema())


class OrderSchema(Schema):
    class Meta:
        fields = ('created', 'amount',)

    created = fields.DateTime()
    amount = fields.Float()


json_data = json.loads('''{
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/197',
  orders: [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
]}''')


parsed_data = PersonSchema().load(json_data)
for order in parsed_data['orders']:
    OrderSchema().load(order)

This will generate a Person class with four fields: first_name, last_name, date_of_birth, and a list of Order objects with two fields each: created (a datetime object) and amount (a float). The resulting dataclasses will look like this:

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) (0.92¢)

Let's evaluate this solution according to the marking scheme:

The solution does not define dataclasses. It only defines schema classes. (0/2 marks)
The solution defines two schema classes: PersonSchema and OrderSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The decimal field (amount) is not defined as a Decimal with as_string=True. It's defined as a Float. (0/1 mark)
The solution does not use @post_load to link the schema and dataclasses. (0/2 marks)
Additional points:
The solution imports necessary libraries.
It provides a method to parse the JSON data.
However, there are some issues:
- The OrderSchema is used before it's defined.
- The JSON data is malformed (missing quotes around 'orders').
  (1/2 marks)

Breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format specification": 0,
  "Decimal field definition": 0,
  "Linking schema and dataclasses": 0,
  "Additional points": 1
}

Score:

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

Comments:
The solution provides a basic structure for using Marshmallow to parse JSON data, but it falls short in several key areas. It doesn't create dataclasses as requested, doesn't handle the date and decimal fields correctly, and doesn't link the schemas to dataclasses. The JSON parsing attempt is a good addition, but the JSON itself is malformed. The solution would benefit from addressing these issues and following the requirements more closely.

openai/gpt-4-0125-preview says: (2.5) ()

The solution provided attempts to use Marshmallow to deserialize JSON into Python objects and partially meets the requirement, but it contains several errors and omissions according to the scoring guidelines:

Definition of dataclasses: The solution lacks any definition of dataclass for both Person and Order, which is a fundamental requirement for creating data structures in Python to hold deserialized data. No use of the dataclasses.dataclass decorator or import from dataclasses module.
Score: 0/2
Schema classes definition: Two schema classes PersonSchema and OrderSchema are defined.
Score: 2/2
Date format specification: The date_of_birth field should have been a Date field with the specified format (%m/%d/%Y). However, it's incorrectly handled as a DateTime field with no format specified.
Score: 0/1
Decimal field: The amount field should be a Decimal field with as_string=True. However, it is defined as a Float, which is not correct for handling decimal types as specified in the requirements.
Score: 0/1
Linking schema and dataclasses using @post_load: The solution does not include any @post_load methods to link the schema to dataclasses, which are crucial for creating instances of the dataclass from loaded data.
Score: 0/2
Other considerations:
The implementation details such as loading the JSON and iterating to load orders are handled partially correctly, but the JSON itself contains a syntax error (197 instead of 1977 and missing quotes around orders).
The use of nested OrderSchema within PersonSchema is initiated but not functional due to the misordering (attempting to reference OrderSchema before its definition).
Error in JSON handling (json_data dictionary keys should be double-quoted in actual json format being loaded).
Score: 0.5/2

Given these observations, here is the total score:

{
  "score": 2.5
}

1.00	0.88	0.88
0.88	1.00	0.91
0.88	0.91	1.00

Pygmalion: Mythalion 13B

Description

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