OpenAI: GPT-4 Turbo

Description

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

Training data: up to December 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?

Response 1 (9.0)

To achieve your goal of converting the given JSON structure into data classes using the Python marshmallow library, we'll first need to define suitable data classes and then create corresponding Marshmallow schemas that handle the conversion of JSON data types to Python data types such as datetime.date, datetime.datetime, and decimal.Decimal. Here’s how to do it step-by-step:

Step 1: Install `marshmallow`

If not already installed, you need to first install the marshmallow library. You can do this using pip:

pip install marshmallow

Step 2: Define Data Classes

Although marshmallow works with plain classes or dictionaries, using Python's dataclasses module can tidy up the model definition:

from dataclasses import dataclass
from datetime import datetime, date
from decimal import Decimal
from typing import List

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

Step 3: Create Marshmallow Schemas

Next, you create marshmallow schemas to convert JSON data to and from instances of these data classes.

from marshmallow import Schema, fields, post_load

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(as_string=True)  # Keeps the decimal as a string on serialization

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

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

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

Step 4: Deserialize JSON to Data Classes

You can now use the schema to parse JSON data into the data classes.

import json

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

# Load JSON to a dictionary
data = json.loads(json_input)

# Deserialize JSON to data class instances
person_schema = PersonSchema()
result = person_schema.load(data)
print(result)

This setup should correctly parse the date_of_birth into a Python date object, the created field into a Python datetime object, and the amount field into a decimal.Decimal object. And the output will be instances of the defined data classes (Person containing a list of Order instances) which are ready to use in your application.

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) (1.05¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, PersonSchema and OrderSchema. (2 marks)
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)
The decimal field correctly uses as_string=True. (1 mark)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules.
It provides a step-by-step explanation.
It includes a sample of how to use the schemas to deserialize JSON data.
(2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution is generally well-structured and covers most of the required elements. It correctly defines dataclasses and schema classes, uses the @post_load decorator to link schemas with dataclasses, and handles the decimal field correctly.

However, it misses specifying the date format for the date_of_birth field, which is a crucial point in the requirements. This oversight might lead to parsing errors when dealing with the provided JSON data.

Despite this omission, the solution provides a comprehensive approach to solving the problem, including additional helpful information such as installation instructions and a usage example, which adds value to the overall solution.

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

Overall, the solution provided is well-structured and covers all the necessary steps to achieve the desired outcome. Here is the breakdown of the evaluation:

The solution defines two data classes - one for the person and one for the order. (2 marks)
The solution defines two schema classes - one for the person and one for the order. (2 marks)
The solution specifies the date format for the date_of_birth field as format="%m/%d/%Y". (1 mark)
The decimal field in the solution uses as_string=True. (1 mark)
The schema and data classes are linked with a @post_load method on the schema classes. (2 marks)
The solution provides clear and detailed guidance on how to implement the solution. (2 marks)

Final Score:

8 out of 10

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

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

openai/gpt-4-0125-preview says: (7.0) ()

The provided solution does well in several aspects of using marshmallow to handle JSON data describing a person and their associated orders, converting this data into Python objects with specified types. Let's review the solution based on the marking scheme:

Defining Data Classes: The Solution defines two appropriate data classes, Order and Person, using Python's dataclasses module. Order correctly handles datetime and Decimal data types, and Person includes a list of Order. (2 marks)
Defining Schema Classes: It defines two Marshmallow schema classes, OrderSchema and PersonSchema. These schemas are designed to serialize and deserialize Order and Person data classes respectively. (2 marks)
Date Format Specification: The solution does not explicitly specify the date format for date_of_birth in the schema with (format="%m/%d/%Y"). It rather relies on default parsing which might not always handle different date formats correctly. (0 marks)
Handling Decimal Field: The solution sets the amount field in OrderSchema to be handled as a string with as_string=True. This is correctly implemented. (1 mark)
Linking Schema and Data Classes with @post_load Method: Both schema classes use the @post_load method to create instances of their respective data classes from the loaded data, which is correctly implemented. (2 marks)
Additional Considerations: The solution includes correct imports and a full example of how to deserialize the JSON input to Python data class instances, adding clarity and completeness to the explanation. Furthermore, it outlines the steps needed for installation and provides a test case. (2 marks)

In summary, the solution effectively demonstrates how to use Marshmallow with Python data classes to handle complex JSON data structures but misses explicitly specifying the date format which is crucial for consistent data handling especially when different formats might be encountered.

Final score:

{
  "score": 7
}

anthropic/claude-3-haiku says: (10.0) (0.10¢)

The provided solution is a comprehensive and well-structured approach to solving the given problem using the marshmallow library in Python. Let's evaluate the solution based on the marking scheme:

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

The solution should define two dataclasses, one for the person and one for the order (2 marks):
The solution correctly defines two dataclasses, Person and Order, which meet the requirements of the problem statement.
The solution should define two schema classes, one for the person and one for the order (2 marks):
The solution defines two schema classes, PersonSchema and OrderSchema, which are used to handle the serialization and deserialization of the data.
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark):
The solution correctly specifies the date format for the date_of_birth field as "%m/%d/%Y" in the PersonSchema.
The decimal field needs as_string=True. (1 mark):
The solution correctly sets the as_string=True option for the amount field in the OrderSchema, which ensures that the Decimal value is serialized and deserialized as a string.
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks):
The solution correctly uses the @post_load decorator on the make_person and make_order methods in the PersonSchema and OrderSchema respectively, which link the schema classes to the dataclasses.
Anything else: (2 marks):
The solution includes additional steps, such as installing the marshmallow library, and provides a complete example of how to deserialize the JSON data into the defined data classes. This extra detail and completeness of the solution earns the full 2 marks.

Overall, the provided solution is a high-quality and well-structured implementation that meets all the requirements of the problem statement. The solution scores a total of 10 out of 10 marks.

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

openai/gpt-4-1106-preview says: (6.0) (2.16¢)

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

Comments on the solution:

The solution correctly defines two dataclasses for the person and the order, following the problem statement: Order and Person.
Two schema classes are also adequately defined, matching the defined dataclasses: OrderSchema and PersonSchema.
The solution does not specify the date format for the date_of_birth field as required by the problem statement (format="%m/%d/%Y"). This is needed to ensure correct parsing of the JSON date string.
The amount field does need as_string=True to ensure it's handled as a string when serialized, but the problem indicates this is required for parsing the decimal field, and it is not set up to parse the amount string from JSON into a Decimal object.
There are properly defined @post_load methods in both schema classes that link back to the data classes, correctly creating instances of the data classes after loading.
One significant deficit not covered by the listed items is that the date_of_birth field in PersonSchema and the created field in OrderSchema do not have their formats specified, and the amount field should be handled as a Decimal, not as a string when loaded from JSON.

Overall, the provided solution needs some corrections to fulfill all the requirements. Specifically, it does not address the correct formatting of dates and datetime, nor does it handle the decimal amount correctly in deserialization, as required by the problem statement. The total score, therefore, is 6 out of 10.

Response 2 (9.0)

To use the marshmallow library to deserialize the JSON you provided into Python objects using dataclasses, you first need to define dataclasses for your data, and then create corresponding Marshmallow schemas to handle the serialization and deserialization processes. Marshmallow will help convert fields like date_of_birth, created, and amount into appropriate Python types (date, datetime, and Decimal respectively).

Here's how to do it step-by-step:

Step 1: Install marshmallow

If you haven't installed marshmallow, you can do so using pip:

pip install marshmallow

Step 2: Define Your Data Classes

You need to define Python dataclasses for your JSON structure. Let's create one for Order and one for the main object that includes the user's details and a list of orders.

from dataclasses import dataclass
from typing import List
from datetime import datetime, date
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]

Step 3: Define Marshmallow Schemas

Create schemas that correspond to your data classes. We'll include field-specific validators and serializers using Marshmallow's fields.

from marshmallow import Schema, fields, post_load, ValidationError
import dateutil.parser

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

    @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()
    orders = fields.List(fields.Nested(OrderSchema))

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

Step 4: Deserialize JSON

Now you can use the schemas to deserialize JSON data into the data classes defined above.

import json
from decimal import Decimal

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

# Deserialize JSON to Python objects
person_schema = PersonSchema()
result = person_schema.loads(json_data)

# Access the loaded data
if isinstance(result, Person):
    print(f"Person: {result.first_name} {result.last_name}")
    for order in result.orders:
        print(f"Order on {order.created}: ${order.amount}")
else:
    # Handle errors
    print("Failed to parse JSON data")

This code will correctly convert the date_of_birth and created fields into Python date and datetime objects, respectively, and amount into a Decimal.

Make sure that the JSON is correctly formatted (quotes should be double quotes in JSON) and field types match the expected types defined in schemas to avoid ValidationError during deserialization. Adjust types and error handling as needed to align with your real application's requirements.

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-haiku says: (10.0) (0.10¢)

Evaluating the provided solution:

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

The solution covers all the required aspects:

The solution should define two dataclasses, one for the person and one for the order (2 marks). The solution defines Person and Order dataclasses as required.
The solution should define two schema classes, one for the person and one for the order (2 marks). The solution defines PersonSchema and OrderSchema as required.
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark) The solution correctly defines the date_of_birth field in the PersonSchema using the fields.Date() field, which will automatically handle the date format.
The decimal field needs as_string=True. (1 mark) The solution correctly defines the amount field in the OrderSchema using fields.Decimal(as_string=True), which will ensure the decimal value is serialized/deserialized as a string.
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks) The solution includes @post_load methods in both PersonSchema and OrderSchema to create the corresponding Person and Order objects.
Anything else: (2 marks) The solution includes additional details, such as the installation of the marshmallow library, error handling, and an example of how to use the deserialized data. This makes the solution more complete and easier to understand.

Overall, the provided solution is well-structured and covers all the required aspects. It receives a total score of:

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

openai/gpt-4-0125-preview says: (8.5) ()

Firstly, let's evaluate the provided solution based on your specified criteria:

Dataclasses Definition
The solution includes two dataclasses Order and Person. Order includes fields created (datetime) and amount (Decimal), and Person encompasses first_name, last_name, date_of_birth (date), and a list of Order.
Score: 2/2
Schema Classes Definition
The solution correctly defines two schema classes: OrderSchema and PersonSchema. These classes include proper marshmallow fields and handle deserialization of nested structures.
Score: 2/2
Date Format Specification
The date format for date_of_birth should be specified in the schema to match the given format ("%m/%d/%Y"). However, the solution does not specify this format in the fields.Date() call within PersonSchema.
Score: 0/1
Decimal Field with as_string=True
The solution specifies as_string=True for the amount Decimal field correctly.
Score: 1/1
Post-Load Method Linking
Both schema classes (OrderSchema and PersonSchema) use the @post_load decorator to link the data output to the respective dataclasses (Order and Person).
Score: 2/2
Additional Criteria
The solution includes proper installation instructions.
Deserialization using schemas is correctly demonstrated along with error handling.
However, JSON parsing instructions should mention the correct format of quotes, and the figure admits this adjustment.
Score: 1.5/2

Overall Evaluation:
- The solution robustly applies marshmallow schemas to handle complex, nested data structures. The primary oversight is the absence of the specified date format in the schema, which is crucial for proper parsing of string date formats.

Final Score Calculation

{
  "score": 8.5
}

anthropic/claude-3.5-sonnet says: (9.0) (1.08¢)

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

Defining two dataclasses (Person and Order):
The solution correctly defines both dataclasses. (2 marks)
Defining two schema classes (PersonSchema and OrderSchema):
The solution correctly defines both schema classes. (2 marks)
Specifying date format for 'date_of_birth':
The solution does not specify the format for 'date_of_birth'. It should have used fields.Date(format="%m/%d/%Y"). (0 marks)
Using 'as_string=True' for the decimal field:
The solution correctly uses as_string=True for the amount field. (1 mark)
Linking schema and dataclasses with @post_load:
The solution correctly uses @post_load decorators to link schemas with dataclasses. (2 marks)
Additional points:
The solution imports necessary modules.
It provides a step-by-step explanation.
It includes an example of how to use the schemas to deserialize JSON data.
(2 marks)

Comments:
The solution is generally well-structured and covers most of the required aspects. However, it missed specifying the date format for the 'date_of_birth' field, which is an important detail given in the problem statement. The solution also provides a good explanation of the process and includes example usage, which is helpful.

Here's the breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal as_string": 1,
  "post_load linking": 2,
  "Additional points": 2
}

Final score:

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

openai/gpt-3.5-turbo-0125 says: (None) (0.08¢)

Overall, the solution provided is well-structured and detailed. It covers all the essential components needed to achieve the desired outcome using the marshmallow library in Python.

Here is 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 out of 10

The solution effectively addresses the problem by defining dataclasses, schema classes, specifying date formats, using as_string=True, and linking schemas and dataclasses with a @post_load method. It provides a clear step-by-step guide on how to implement the solution using the marshmallow library.

openai/gpt-4-1106-preview says: (7.0) (2.28¢)

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 0,
  "marks 4": 1,
  "marks 5": 2,
  "marks 6": 0,
  "scores": [2, 2, 0, 1, 2, 0],
  "total_score": 7
}

Comments on the solution:

Data classes have been defined successfully for both the Person and Order, fulfilling the requirements for the first criteria (2 marks).
Corresponding schema classes have also been rightly defined for both the Person and Order, meeting the second criteria (2 marks).
The solution does not specify the date format for the date_of_birth field using format="%m/%d/%Y", which was a specific requirement for proper field validation (0 marks).
The requirement for the decimal field to use as_string=True is met in the schema definition (1 mark).
The schemas and dataclasses are linked through the use of the @post_load method as required (2 marks).
The solution missed a couple of additional points:
The error handling in the provided code might not be sufficient, as it only prints a statement and does not raise an actual exception.
There's no explicit example of the usage of the dateutil.parser that was imported, although it is not necessary since Marshmallow handles date/datetime parsing out-of-the-box.
There is a potential ImportError since the Decimal is imported but not used anywhere in the schemas; fields.Decimal is used directly without the need to import Decimal from the decimal module (0 marks).

Overall, the solution is well-structured and aligns with the majority of the requirements. With proper error handling and date format specification, it would have scored higher.

Response 3 (9.0)

To work with JSON data and automatically handle conversions to specific Python types such as dates, datetimes, and decimals using the marshmallow library, you can define dataclasses and use corresponding marshmallow schemas to serialize and deserialize the data. Here, I'm going to guide you on how to achieve this.

First, install marshmallow and dataclasses if you haven't already (if you're using Python 3.7 or later, dataclasses is included in the standard library):

pip install marshmallow

Next, define your data classes and corresponding schemas. Here’s how you could do it:

from dataclasses import dataclass
from datetime import datetime, date
from decimal import Decimal
from marshmallow import Schema, fields, post_load, ValidationError
from marshmallow.validate import Range

@dataclass
class Order:
    created: datetime
    amount: Decimal

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(as_string=True, places=2, validate=Range(min=Decimal('0.01')))

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

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[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), required=True)

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

# Sample usage
if __name__ == '__main__':
    # JSON data to be deserialized
    json_data = {
        'first_name': 'Jane',
        'last_name': 'Smith',
        'date_of_birth': '03/16/1977',
        'orders': [
            {
                'created': '2024-01-25T15:00:04',
                'amount': '100.00'
            }
        ]
    }

    schema = PersonSchema()
    try:
        result = schema.load(json_data)
        print(result)
    except ValidationError as err:
        print(err.messages)
        print(err.valid_data)

Explanation:

Dataclass Use:
Order and Person are defined as Python dataclasses to store the state of an object. This means they can automatically generate special methods like __init__().
Marshmallow Schemas:
OrderSchema and PersonSchema are defined using Marshmallow. These schemas are responsible for validating, serializing (dumping), and deserializing (loading) JSON data into the respective dataclass instances.
created in OrderSchema uses DateTime field for datetime handling.
amount in OrderSchema uses Decimal field to handle decimal values with string representation privided and enforced precision.
date_of_birth in PersonSchema uses Dateto correctly parse and output ISO8601 formatted date strings.
The post_load decorators bind a method to the schema’s load process, which means after marshmallow has loaded the data, it automatically constructs the Order or Person dataclass instance.
Error Handling:
The ValidationError is captured during the deserialization process to manage and display any error that might occur if the input data types are not as expected or violate defined constraints (e.g., non-positive amounts).

This setup ensures your data is properly typed and validated, making for cleaner and safer data manipulation within your application.

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) (1.07¢)

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

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It uses the default ISO8601 format instead of the required "%m/%d/%Y". (0 marks)
The decimal field amount correctly uses as_string=True. (1 mark)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution includes error handling with try/except for ValidationError. (1 mark)
The solution adds validation for the amount field with validate=Range(min=Decimal('0.01')). (1 mark)

Here's the breakdown of the marks:

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

Final score:

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

The solution is very good overall, addressing most of the requirements effectively. The main issue is the omission of the specific date format for the date_of_birth field. To improve, the solution should include format="%m/%d/%Y" in the date_of_birth field definition in the PersonSchema. Otherwise, it demonstrates a solid understanding of marshmallow and dataclasses, including some extra features like validation that weren't explicitly required.

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

The provided solution does an excellent job in several respects but falls short in a couple of key requirements based on the provided marking scheme:

Definition of Data Classes:
The solution defines two dataclasses, Order and Person, perfectly matching the requirement. (2 marks)
Definition of Schema Classes:
Corresponding schema classes OrderSchema and PersonSchema are well defined. (2 marks)
Date Formatting for date_of_birth:
Though the PersonSchema handles the date_of_birth field using a Date Marshmallow field, there's no explicit formatting rule setting in the schema using format="%m/%d/%Y" as required by the problem statement. (0 marks)
Decimal Field Configuration:
The solution correctly uses fields.Decimal(as_string=True, places=2 ...) ensuring that the decimal value is treated as a string and with mandatory decimal places. (1 mark)
Linking Schema and Dataclasses with @post_load Method:
The schemas use the @post_load decorator to create instances of Order and Person from the loaded data, which perfectly aligns with the requirements. (2 marks)
Additional Elements:
The solution includes additional thoughtful details like error handling with ValidationError and usage of field constraints (Range validator for amount), which add robustness to the data handling. The explanatory comments and structured sample usage also enhances understandability and practical implementation ease. (2 marks)

Based on the scoring criteria:

{
  "score": 9
}

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

The provided solution is comprehensive and covers all the requirements specified in the marking scheme. Let's evaluate it in detail:

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

The solution defines two dataclasses, Order and Person, which meet the requirements.

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

The solution also defines two schema classes, OrderSchema and PersonSchema, which correspond to the dataclasses.

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

The solution includes the as_string=True option for the amount field in the OrderSchema, which ensures the decimal value is serialized and deserialized as a string.

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

The solution links the schema and dataclasses using the @post_load method on the schema classes, which automatically constructs the respective dataclass instances.

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

The solution also includes error handling for the ValidationError that can occur during the deserialization process, which is an additional feature that goes beyond the requirements.

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

The provided solution is complete and meets all the requirements specified in the marking scheme. It demonstrates a well-structured and robust approach to working with JSON data using marshmallow and dataclasses in Python.

openai/gpt-3.5-turbo-0125 says: (6.0) (0.09¢)

This solution provides a clear and well-structured approach to handling the given problem using the marshmallow library in Python. Here is the breakdown of the evaluation:

The solution defines two dataclasses along with their respective schema classes, linking them appropriately with a @post_load method for deserialization. (2 marks)
The solution specifies the date format for the date_of_birth field, ensuring correct parsing of the date string. (1 mark)
The solution correctly sets as_string=True for the decimal field to handle decimal values. (1 mark)
The overall structure and explanation of the solution are clear, providing a step-by-step guide on how to achieve the desired data serialization and validation. (2 marks)

Total Score: 6 out of 6

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

openai/gpt-4-1106-preview says: (8.0) (2.30¢)

{
  "marks_1": 2,
  "marks_2": 2,
  "marks_3": 1,
  "marks_4": 1,
  "marks_5": 1,
  "marks_6": 1,
  "total_score": 8
}

Evaluation:

marks_1: The solution correctly defines two dataclasses for Person and Order. Thus, it earns full marks here.
marks_2: Two schema classes PersonSchema and OrderSchema are appropriately defined. Full marks awarded.
marks_3: The solution forgot to specify the date format for the date_of_birth field explicitly using format="%m/%d/%Y". As such, it earns no marks in this since the correct date format specification is part of the requirement.
marks_4: The decimal field in OrderSchema has as_string=True, fulfilling this criterion.
marks_5: The @post_load decorators are used for both PersonSchema and OrderSchema. However, the make_person and make_order methods incorrectly take a parameter named data instead of data_. Though the functions will work due to the flexible nature of **kwargs, for precision it deserves half the marks.
marks_6: Error handling via ValidationError is included correctly, demonstrating additional attention to possible errors in deserialization as part of the solution. However, the Range validator on the OrderSchema's amount field and the missing explicit format parameter for the date_of_birth field are points for deduction.

Total Score:
8/10

The solution performs most tasks correctly but with a notable omission and a minor inconsistency with parameter naming.

1.00	0.90	0.88
0.90	1.00	0.86
0.88	0.86	1.00

OpenAI: GPT-4 Turbo

Description

Step 1: Install `marshmallow`

Step 2: Define Data Classes

Step 3: Create Marshmallow Schemas

Step 4: Deserialize JSON to Data Classes

Final Score:

Step 1: Install marshmallow

Step 2: Define Your Data Classes

Step 3: Define Marshmallow Schemas

Step 4: Deserialize JSON

Final Score Calculation

Total Score: 10 out of 10

Explanation:

Total Score: 6 out of 6

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claude-3-haiku	10.0	10.0	10.0
claude-3.5-sonnet	9.0	9.0	9.0
gpt-3.5-turbo-0125	8.0		6.0
gpt-4-0125-preview	7.0	8.5	9.0
gpt-4-1106-preview	6.0	7.0	8.0