OpenAI: GPT-4 Turbo (older v1106)

Description

The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling.

Training data: up to April 2023.

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?

To generate dataclasses and serialize/deserialize JSON data with specific types using the marshmallow library in Python, you need to follow these steps:

  1. Define your dataclasses with appropriate type annotations.
  2. Create corresponding marshmallow Schema classes to define how serialization and deserialization should work for each dataclass.
  3. Use marshmallow fields to specify custom serialization/deserialization behavior for specific fields like dates, datetimes, and decimals.

Below is an example of how you would do this:

First, install the marshmallow library if you haven't already:

pip install marshmallow
pip install marshmallow-dataclass

Now, create the dataclasses and corresponding marshmallow schemas:

from dataclasses import dataclass
from datetime import datetime, date
from decimal import Decimal
from marshmallow import Schema, fields, post_load
from marshmallow_dataclass import class_schema

# Define the dataclasses
@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

# Define corresponding marshmallow Schemas
OrderSchema = class_schema(Order)

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

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

# Convert the example JSON data to the format expected by marshmallow
input_json = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '1977-03-16',
  'orders': [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}

# Deserialize the JSON data to dataclass instances
person_schema = PersonSchema()
person = person_schema.load(input_json)

print(person)
# You should now have a Person instance with the attributes properly typed

Notes:
- Marshmallow will automatically deserialize the ISO 8601 formatted datetime string to a datetime object in the created field and will parse the date string to a date object for the date_of_birth field.
- The Decimal field will be loaded from the JSON string representation of the decimal number automatically as well.
- I fixed the JSON structure to have string keys which is the correct JSON format ('orders' instead of orders without quotes). (Note that the JSON specification requires dictionary keys to be strings. Nevertheless, Python's json library can parse non-string dictionary keys as strings.)
- The date_of_birth field in the input JSON must be formatted as 'YYYY-MM-DD' to match the expected format for Python's date objects.

Remember that the fed JSON data keys must match the marshmallow schema fields exactly, or marshmallow won't be able to deserialize the JSON properly.

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: (7.0) (0.97¢)

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

  1. The solution defines two dataclasses, Order and Person. (2 marks)

  2. The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field as "%m/%d/%Y". Instead, it uses the default ISO format. (0 marks)

  4. The solution does not include as_string=True for the decimal field. (0 marks)

  5. The solution links the schema and dataclasses with a @post_load method on the PersonSchema class. However, it's missing for the OrderSchema. (1 mark)

  6. Additional points:

  7. The solution uses marshmallow-dataclass library, which simplifies the creation of schemas for dataclasses.
  8. It provides a complete example with imports and usage demonstration.
  9. It includes helpful notes and explanations.
    (2 marks)

Breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "Post-load method": 1,
  "Additional points": 2
}

Score:

{
  "scores": [2, 2, 0, 0, 1, 2],
  "total_score": 7
}

The solution demonstrates a good understanding of using marshmallow with dataclasses, but misses some specific requirements mentioned in the problem statement. The total score is 7 out of 10.

anthropic/claude-3-haiku says: (10.0) (0.07¢)

Let's evaluate the provided solution:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Score Breakdown:

  1. The solution defines two dataclasses, one for the person and one for the order, as required. (2 marks)
  2. The solution defines two schema classes, one for the person and one for the order, as required. (2 marks)
  3. The solution specifies the date format for the date_of_birth field using the fields.Date(format="%m/%d/%Y") constructor, which is correct. (1 mark)
  4. The solution uses as_string=True for the Decimal field, which is correct. (1 mark)
  5. The solution links the schema and dataclasses using the @post_load method on the schema classes, as required. (2 marks)
  6. The solution also fixes the JSON structure to have string keys, which is a good additional step. (2 marks)
{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The provided solution is complete and correctly implements the required functionality using the marshmallow library. It meets all the criteria specified in the marking scheme.

openai/gpt-3.5-turbo-0125 says: (7.0) (0.09¢)

Overall, the solution provided is well-structured and covers all the necessary steps to achieve the desired outcome using the marshmallow library in Python. The author correctly defines the dataclasses, schema classes, and links them using the @post_load method. Additionally, the solution specifies the date format for the date_of_birth field and includes the necessary parameter for decimal fields.

The author also made a note about the JSON structure requiring string keys, which is important for proper JSON formatting.

However, the provided solution lacks a few minor details such as error handling during deserialization or any potential edge cases that might arise. Including these aspects would have made the solution more robust.

Score Breakdown:
1. Defines dataclasses and corresponding schemas: 2/2
2. Specifies date format and decimal field parameter: 2/2
3. Links schema and dataclasses with @post_load method: 2/2
4. Additional considerations: 1/2

Total Score: 7/8

{
  "scores": [2, 2, 2, 1],
  "total_score": 7
}

openai/gpt-4-0125-preview says: (8.0) ()

The provided solution outlines the process of using the marshmallow library in combination with dataclasses in Python to perform serialization and deserialization of JSON data with specific requirements for date, datetime, and decimal types. Let's evaluate the solution according to the provided marking scheme:

  1. Definition of dataclasses (2 marks):
  2. The person has defined dataclasses for Person and Order, with appropriate type annotations (datetime, date, Decimal).

  3. Full marks: 2/2

  4. Definition of schema classes (2 marks):

  5. A PersonSchema is defined using the marshmallow.Schema class.
  6. An OrderSchema is automatically derived using class_schema(Order).

  7. Full marks: 2/2

  8. Specification of date format for 'date_of_birth' (1 mark):

  9. The solution uses the default date handling of marshmallow, which works with ISO date formats ('YYYY-MM-DD'), and does not specifically adjust the format to match the problem statement ("%m/%d/%Y").

  10. Marks lost due to not specifying the required date format: 0/1

  11. The decimal field configuration with as_string=True (1 marks):

  12. There is no explicit configuration (as_string=True) in the Decimal field to ensure serialization back to string format in JSON.

  13. Marks lost as the Decimal handling wasn't configured as specified: 0/1

  14. Linkage of schema and dataclasses via @post_load method (2 marks):

  15. The PersonSchema includes a @post_load method, successfully connecting the schema to the dataclass by instantiating Person with the deserialized data.

  16. Full marks: 2/2

  17. Additional considerations (2 marks):

  18. The solution addresses miscaptions in the JSON format (usage of non-string keys) and thus adapts it correctly for use.
  19. The proper handling of automatic deserialization provided by marshmallow without additional conversions or settings for datetime and correcting JSON key formatting represents a valuable inclusion.
  20. Good documentation and explanation steps included.
  21. Full marks: 2/2

Overall Score:

{
  "score": 8
}

While the solution is generally well-crafted and functional, the lack of adherence to the specific date format ("%m/%d/%Y") and handling of the Decimal field (as_string=True) as specified in the requirements has led to points deduction.

openai/gpt-4-1106-preview says: (3.0) (2.70¢)

{
  "marks 1": 1,
  "marks 2": 1,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 1,
  "marks 6": 0,
  "total_score": 3
}

Evaluation:

  1. Two dataclasses for the person and order were defined, but because the amount is Decimal and not specified as a string in the Order dataclass, it will not seamlessly deserialize from JSON. Half credit is given for the creation of dataclasses but failing to address that JSON decoding needs Decimal(as_string=True) for correct deserialization (0.5).
  2. Two schema classes are somewhat defined, the OrderSchema is automatically done by class_schema from the dataclass, but for the PersonSchema more customization is made, so half credit for defining schema classes (0.5).
  3. The given solution does not specify the date format for the date_of_birth field using format="%m/%d/%Y" in the schema. This field's format is not correctly specified to match the requirements of the problem (0).
  4. The decimal field does not have as_string=True in the solution, so when loading from JSON, it would not load as a string and therefore cannot be converted correctly to a Decimal. The marshmallow schema does not define decimals correctly (0).
  5. There is a @post_load method to make a person, which is correct, but it is unnecessary for the Order data class since it can be created by class_schema in the line above. So, half marks (0.5).
  6. The solution has some issues: a) the amount field in the Order schema should have been defined to enforce as_string=True, b) the date format for date_of_birth is not set as required, c) the provided JSON data is altered to match ISO 8601 date format which is not asked for in the problem statement, d) created should use DateTime with correct formatting (not provided), e) the input JSON in the example should not have been corrected (keys of 'orders' should not require quotation marks in Python, although it's fine in JSON). For that reason, I will award half marks because the solution has multiple issues but does mention the correct JSON format for keys (0.5).

Final score:

{
  "scores": [
    0.5,
    0.5,
    0,
    0,
    0.5,
    0.5
  ],
  "total_score": 3
}

To work with marshmallow to serialize and deserialize the given JSON data into dataclasses, you would first need to define the appropriate dataclasses with their respective types. You would then create custom Field classes or use marshmallow provided fields for date, datetime, and Decimal to match your requirements. Here's a step-by-step procedure:

  1. Install marshmallow if you haven't already:
pip install marshmallow
  1. Define the dataclasses for your data:
from dataclasses import dataclass
from typing import List
from datetime import date, datetime
from decimal import Decimal

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order]
  1. Define the marshmallow schemas to serialize and deserialize the dataclasses:
from marshmallow import Schema, fields, post_load
from marshmallow.validate import Range

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(as_string=True, validate=Range(min_value=Decimal('0.00')))

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

class PersonSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True, format='%m/%d/%Y')
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)
  1. Now you can use the schemas to load (deserialize) your JSON data:
import json
from marshmallow import ValidationError

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

# Parse the JSON string into a Python dictionary
data = json.loads(json_data)

# Deserialize
person_schema = PersonSchema()
try:
    person = person_schema.load(data)
    print(person)  # You will get the Person object with proper types
except ValidationError as err:
    print(err.messages)  # If validation fails, you will see error messages

This will create a Person object with the date_of_birth as a date object, orders as a list of Order objects with datetime and Decimal for created and amount, respectively.

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: (10.0) (0.97¢)

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

  1. The solution defines two dataclasses, Order and Person. (2 marks)

  2. The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)

  3. The date format for the date_of_birth field is correctly specified as format='%m/%d/%Y'. (1 mark)

  4. The amount field in OrderSchema is correctly defined with as_string=True. (1 mark)

  5. Both schema classes are linked to their respective dataclasses using @post_load decorators. (2 marks)

  6. Additional points:

  7. The solution imports necessary modules.
  8. It includes a validation for the amount field using Range.
  9. It provides an example of how to use the schemas to deserialize JSON data.
  10. Error handling is demonstrated using a try-except block for ValidationError.
    (2 marks)

Here's the breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 1,
  "Decimal field specification": 1,
  "Schema and dataclass linking": 2,
  "Additional features": 2
}

Final score:

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The solution covers all the requirements specified in the marking scheme and includes additional helpful features. It demonstrates a good understanding of both dataclasses and the marshmallow library, providing a comprehensive solution to the problem. The total score is 10 out of 10.

anthropic/claude-3-haiku says: (10.0) (0.09¢)

The provided solution is a good implementation using the marshmallow library to deserialize the given JSON data into dataclasses. It addresses all the requirements specified in the problem statement. Let's break down the evaluation:

{
  "marks 1": 2,
  "marks 2": 2
}

The solution correctly defines two dataclasses, Order and Person, as per the requirement.
The solution also defines two schema classes, OrderSchema and PersonSchema, to handle the serialization and deserialization of the data.

{
  "marks 3": 1
}

The solution specifies the date format for the date_of_birth field using the format='%m/%d/%Y' parameter in the fields.Date definition.

{
  "marks 4": 1
}

The solution uses as_string=True for the amount field in the OrderSchema, which ensures that the Decimal value is serialized and deserialized as a string.

{
  "marks 5": 2
}

The solution uses the @post_load method on both the OrderSchema and PersonSchema to link the schemas with their respective dataclasses, ensuring a seamless conversion between the JSON data and the dataclass objects.

{
  "marks 6": 2
}

The solution also includes a complete example of how to use the defined schemas to deserialize the given JSON data, which demonstrates the overall functionality of the implementation.

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The provided solution is a comprehensive and well-structured implementation that meets all the requirements specified in the problem statement. The solution effectively leverages the marshmallow library to handle the serialization and deserialization of the JSON data into dataclasses with the required data types.

openai/gpt-3.5-turbo-0125 says: (10.0) (0.08¢)

The provided solution is well-structured and follows the correct approach for utilizing the marshmallow library in Python to parse the given JSON data into dataclasses with the specified types. Here is the breakdown of the marks:

  • Two dataclasses defined: 2 marks
  • Two schema classes defined: 2 marks
  • Date format specified for date_of_birth field: 1 mark
  • Decimal field with as_string=True: 1 mark
  • Linking schema and dataclasses with @post_load method: 2 marks
  • Overall clarity and correctness of the solution: 2 marks

Therefore, the total score for this solution would be:

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

The solution scored full marks (10 out of 10) based on the evaluation criteria provided.

openai/gpt-4-0125-preview says: (10.0) ()

The solution provided defines two dataclasses (Order and Person) and two corresponding schema classes (OrderSchema and PersonSchema), which matches the requirements specified in the problem statement. The implementation used with marshmallow shows a clear understanding of how to use dataclasses together with serializers for JSON data and custom field mappings.

Marking breakdown:

  1. Dataclasses for person and order: Both dataclasses are defined correctly. (2 marks)
  2. Schema classes for person and order: Both schema classes are well-defined, utilizing marshmallow's validation and custom fields correctly. (2 marks)
  3. Date format for date_of_birth: The format for date_of_birth is explicitly provided as '%m/%d/%Y', which is correct per the requirements. (1 mark)
  4. Decimal field with as_string=True: The amount in OrderSchema is specified as a Decimal and is correctly set to output as a string. (1 mark)
  5. Linkage of schemas and dataclasses via @post_load method: Both schema classes use the @post_load method to create instances of the dataclasses (Person and Order). This is precisely according to the requirement. (2 marks)
  6. Additional elements:
  7. Installation instructions and usage of the schemas to deserialize JSON are also provided, ensuring clarity and completeness of the example.
  8. Validation and error handling during deserialization showcases real world use.

These additional elements increase the practical application value of the solution. (2 marks)

Overall Evaluation:

The supplied solution efficiently meets all the requirements laid out in the problem statement, showing both comprehension of the marshmallow library for serialization/deserialization and adeptness in applying it with Python dataclasses. The submission is clean, correctly addresses the format transformations required for each field, and incorporates validation and error checking.

JSON formatted score:

{
  "score": 10
}

openai/gpt-4-1106-preview says: (9.0) (1.91¢)

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 0,
  "marks 5": 2,
  "marks 6": 2,
  "scores": [2, 2, 1, 0, 2, 2],
  "total_score": 9
}

Comments on the solution:

  • The solution correctly defines two dataclasses, one for Person and one for Order, adhering to the necessary requirements provided in the JSON structure.
  • Two schema classes are also defined correctly to match the respective dataclasses.
  • The solution specifies the date format for the date_of_birth field correctly using the format="%m/%d/%Y" parameter.
  • The solution does not meet the requirement of specifying the as_string=True parameter for the amount field, which should be a decimal. This is a common requirement when dealing with monetary values in order to prevent loss of precision during serialization and deserialization.
  • The @post_load decorator is correctly used in both schema classes, ensuring that once the data is loaded, it is then instantiated into the respective dataclasses.
  • Two extra marks are given for correctness, code clarity, and the correct use of marshmallow.

The solution missed only one requirement regarding the as_string=True parameter for the Decimal field. Therefore, the total score is 9 out of 10.

To generate dataclasses and use the marshmallow library to serialize and deserialize the given JSON data, you'll first need to define dataclasses that represent your data structure, and then create corresponding marshmallow schemas that will handle the conversion of field types.

Here's an example of what this might look like:

from dataclasses import dataclass
from datetime import datetime, date
from decimal import Decimal
from marshmallow import Schema, fields, post_load
from marshmallow_dataclass import add_schema

@add_schema
@dataclass
class Order:
    created: datetime
    amount: Decimal

    class Meta:
        ordered = True

@Order.Schema()
class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(as_string=True)

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

@add_schema
@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

    class Meta:
        ordered = True

@Person.Schema()
class PersonSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

# Usage

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

schema = PersonSchema()
result = schema.load(input_json)
person = result
print(person)

In this code:

  1. Two dataclasses (Order and Person) are defined to represent the structure of the JSON data.

  2. Corresponding marshmallow schemas (OrderSchema and PersonSchema) are defined where we map each JSON field to the desired Python type using marshmallow fields:

  3. fields.DateTime() for datetime fields.

  4. fields.Decimal(as_string=True) for decimal fields. Setting as_string=True ensures amounts are deserialized to Decimal from string.

  5. fields.Date() for date fields.

  6. fields.List(fields.Nested(OrderSchema)) is used to define a list of Order objects within the Person schema.

  7. The @post_load decorator is used to instantiate the dataclasses after loading the data with the schema.

When you run the code, it will deserialize the JSON into a Person object with the proper types (datetime, decimal, and date). If you want to serialize a Person object back to JSON, you can call the dump method of the schema:

person_json = schema.dump(person)
print(person_json)

Remember to handle any possible exceptions for invalid input data, such as incorrect date formats or invalid data types. Marshmallow will throw validation errors if the input doesn't match the specified 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)

anthropic/claude-3.5-sonnet says: (9.0) (1.06¢)

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

  1. The solution defines two dataclasses, Order and Person. (2 marks)

  2. The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0 marks)

  4. The decimal field correctly uses as_string=True. (1 mark)

  5. The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)

  6. Additional points:

  7. The solution uses @add_schema decorator, which is a nice touch for automatic schema generation.
  8. It includes usage examples and explanations.
  9. The solution uses marshmallow_dataclass, which wasn't explicitly required but is a good addition.
    (2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [2, 2, 0, 1, 2, 2],
  "total_score": 9
}

Comments:
The solution is generally well-structured and covers most of the requirements. It demonstrates a good understanding of marshmallow and dataclasses. The main omission is the lack of a specified date format for the date_of_birth field, which was explicitly required in the marking scheme. Despite this, the solution includes some additional features like the use of @add_schema and marshmallow_dataclass, which add value to the implementation. Overall, it's a strong solution with room for a minor improvement.

openai/gpt-3.5-turbo-0125 says: (8.0) (0.09¢)

Overall, the solution provides a clear and structured approach to using the marshmallow library to handle serialization and deserialization of the given JSON data into dataclasses. The approach covers defining the necessary dataclasses, marshmallow schemas, field types, and post-load methods to properly convert the data types.

The solution covers all the required aspects mentioned in the problem statement:

  • Dataclasses and schema classes are defined for both Order and Person structures. (2 marks)
  • The date format for the date_of_birth field is specified as "%m/%d/%Y". (1 mark)
  • The as_string=True attribute is used for the decimal field. (1 mark)
  • Post-load methods are defined in the schema classes to link them with their corresponding dataclasses. (2 marks)

The solution also provides an example of how to utilize the defined schema for deserialization and shows a clear understanding of the marshmallow library.

Therefore, the solution deserves a score of 8 out of 10.

{
  "marks 1": 2,
  "marks 2": 6
}
{
  "scores": [8],
  "total_score": 8
}

openai/gpt-4-0125-preview says: (9.0) ()

The provided solution demonstrates how to use the marshmallow library along with dataclasses to serialize and deserialize JSON data to and from customized Python objects. Here is the evaluation based on the specified marking scheme:

  1. Dataclasses Definitions: The solution defines two dataclasses, Order and Person, that appropriately represent the structure of the JSON data. Each dataclass uses types such as datetime, Decimal, and date that align with the requirements. (2 marks)

  2. Schema Classes Definitions: Two schema classes, OrderSchema and PersonSchema, are defined corresponding to the dataclasses. These schemas handle the conversion of JSON fields to the required Python types, such as DateTime, Decimal, and Date. (2 marks)

  3. Date Format Specification: The solution uses fields.Date() for the date_of_birth field without explicitly setting the format to "%m/%d/%Y" as specified in the problem. However, marshmallow's default behavior might handle common date formats correctly, though this doesn't fully meet the specification's request. (0.5 marks)

  4. Decimal Field Specifics: The amount field in the OrderSchema is handled with fields.Decimal(as_string=True) ensuring that the decimal field is read as a string and converted accurately to Decimal. (1 mark)

  5. Linking via @post_load: Both OrderSchema and PersonSchema utilize the @post_load decorator to link schema loading directly to the instantiation of the corresponding dataclasses, thereby effectively linking the schemas and dataclasses correctly. (2 marks)

  6. Additional Considerations: The solution efficiently utilizes dataclass and schema features such as nested schemas and list handling via fields.List(fields.Nested(OrderSchema)), ensuring a thorough and nested conversion of JSON data into Python objects. Exception handling for invalid data types or data formats is recommended, which is noted but not actually implemented in the code. (1.5 marks)

Final JSON Score Output

{
  "score": 9
}

Summary: The solution effectively uses marshmallow and dataclasses for serialization and deserialization, missing only a few fine details such as explicit date formatting. It mentions the importance of handling exceptions but does not implement this in the code. Overall, the solution is robust and nearly meets all specified criteria.

anthropic/claude-3-haiku says: (10.0) (0.07¢)

The provided solution is comprehensive and covers the key aspects of the problem, including:

  1. Defining two dataclasses, one for the person and one for the order.
  2. Defining two schema classes, one for the person and one for the order.
  3. Specifying the date format for the date_of_birth field.
  4. Setting as_string=True for the decimal field.
  5. Linking the schema and dataclasses using the @post_load method on the schema classes.
  6. Demonstrating the usage of the solution.

Here's the breakdown of the marks:

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 2
}

Total score: 10/10

The solution provided is complete and addresses all the requirements specified in the problem statement.

{
  "scores": [2, 2, 1, 1, 2, 2],
  "total_score": 10
}

openai/gpt-4-1106-preview says: (8.0) (2.16¢)

{
  "marks 1": 1,
  "marks 2": 1,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 6,
  "total_score": 8
}

Comments on the solution:

  • The solution properly defines two dataclasses, Person and Order, in line with the requirement (2 marks for "marks 1").
  • Two corresponding marshmallow schema classes, PersonSchema and OrderSchema, are also defined, satisfying the condition (2 marks for "marks 2").
  • However, the date_format for date_of_birth is not specified using format="%m/%d/%Y" as instructed, so no marks are awarded for "marks 3".
  • The amount field is correctly set with as_string=True, which earns the solution a point (1 mark for "marks 4").
  • The solution suggests the use of @post_load method, but it does not correctly implement the @post_load method to instantiate the dataclasses; the meta class of both dataclasses seems to be incorrectly included and the make_order and make_person methods are not necessary with marshmallow_dataclass as marshmallow_dataclass.add_schema accomplishes the conversion (no marks for "marks 5").
  • Since additional information, such as proper error handling advice, and correct usage of serializers and deserializers are included, I would award 6 points for completeness and thorough explanation (6 marks for "marks 6").

This brings the total score to 8 out of 10. Please note that this score is subjective to my interpretation of the marking scheme and could differ depending on the evaluator.

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