OpenAI: GPT-4 Turbo Preview

Description

The preview GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Training data: up to Dec 2023.

Note: heavily rate limited by OpenAI while in preview.

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 using the marshmallow library in Python, you will need to define data classes and use marshmallow Schema to handle serialization and deserialization, including converting types like date, datetime, and Decimal as specified. Here's how you can do it:

First, ensure you have marshmallow installed.

pip install marshmallow

Now, here's a possible solution:

from dataclasses import dataclass
from datetime import date, datetime
from decimal import Decimal
from typing import List
from marshmallow import Schema, fields, post_load, ValidationError


# Define your data classes
@dataclass
class Order:
    created: datetime
    amount: Decimal

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


# Define Marshmallow Schemas
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()
    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)


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

    try:
        person_schema = PersonSchema()
        person = person_schema.load(json_input)
        print(person)
    except ValidationError as err:
        print(err.messages)

What we did here:

Defined Data Classes: Order and Person - Python dataclass is a decorator that is used to add special methods to classes, which includes __init__, __repr__, __eq__, and __hash__ methods by default. Here, Order has datetime and Decimal types for created and amount respectively, while Person includes a list of Order objects and date for date_of_birth.
Defined Marshmallow Schemas: OrderSchema and PersonSchema - These schemas are responsible for serializing and deserializing the Python objects to and from JSON format, as well as handling type conversions using the fields module in marshmallow.
Type Conversion: Marshmallow automatically handles the conversion of types based on the field specified in the schema. fields.DateTime() will parse and output datetime objects, fields.Date() will do the same for date objects, and fields.Decimal(as_string=True) will handle the conversion to and from Decimal, keeping it as a string during the process to avoid losing precision.
Error Handling: The try...except block around the loading process is used to catch and print any ValidationError that might be raised if the input data does not comply with the schema definition.

This example demonstrates how to use the marshmallow library to work with complex nested data structures, including custom type conversions and collections.

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) (0.99¢)

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 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 and handles potential validation errors. (1 mark)
The solution provides an example usage of the schemas. (1 mark)

Here's the breakdown of marks:

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

The solution is well-structured and covers most of the requirements. However, it misses the specific date format for the date_of_birth field, which is an important detail. The use of dataclasses, marshmallow schemas, and the @post_load decorators is correct and efficiently implements the required functionality. The additional error handling and example usage are helpful additions.

Here's the final score:

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

openai/gpt-3.5-turbo-0125 says: (8.0) (0.10¢)

Overall, the provided solution effectively addresses the problem of generating data classes from a JSON structure while utilizing the marshmallow library in Python. The solution includes the definition of data classes for Order and Person, along with corresponding schemas for serialization and deserialization.

The solution also properly specifies the date format for the date_of_birth field, correctly sets the as_string=True parameter for the Decimal field, and establishes a link between the schemas and data classes using the @post_load method.

Breakdown of Marks:
- Marks 1: 2 (Dataclasses defined)
- Marks 2: 2 (Schemas defined)
- Marks 3: 1 (Date format specified for date_of_birth)
- Marks 4: 1 (Decimal field set with as_string=True)
- Marks 5: 2 (Link established with @post_load method)
- Anything else: 2

Total Score: 8 out of 10

{
  "marks 1": 2,
  "marks 2": 2,
  "marks 3": 1,
  "marks 4": 1,
  "marks 5": 2
}
{
  "scores": [2, 2, 1, 1, 2],
  "total_score": 8
}

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

The provided solution does a good job in constructing dataclasses for Order and Person to mirror the given JSON structure, and has appropriately connected these dataclasses with marshmallow schemas. Here’s a detailed review:

Two Dataclasses Defined:
The solution includes a @dataclass for Order and Person.
The Order dataclass correctly handles the datetime and Decimal requirements for the created and amount fields.
The Person dataclass covers all necessary fields including converting date_of_birth to a date type and contains a list of Order objects. This satisfies the problem requirements.
Schema Classes:
Correctly defines OrderSchema with a Decimal field having as_string=True, preserving the decimal integrity as string.
Defines PersonSchema with a List(fields.Nested(OrderSchema)) for correctly handling orders.
Both schemas have been structured using correct marshmallow fields.
Date and Decimal Field Handling:
The amount is handled with fields.Decimal(as_string=True).
However, the date_of_birth field in PersonSchema does not specify the date format. While marshmallow can deduce date formats, the explicit requirement was to handle date_of_birth using format “%m/%d/%Y,” which is missing here.
Usage of @post_load:
Excellent integration of @post_load method for both schemas ensuring the resultant structure is a correct instance of the defined dataclasses.
Additional Handling:
The solution includes a proper demonstration of how to deserialize JSON input using the defined schemas, and attempts appropriate error handling for validation errors.
The mentioned usage of pip install marshmallow shows basic setup, useful for someone unfamiliar.
Miscellaneous:
The explanation provided in the comments is clear and informative, aiding in understanding the solution.

Given the breakdown, points are awarded as follows:
- Two correct dataclasses: 2/2 marks
- Correctly defined schema classes: 2/2 marks
- Precision handling for amount with as_string=True: 1/1 mark
- Missing explicit date format handling in date_of_birth: 0/1 mark
- Correct implementation of @post_load hooks: 2/2 marks
- Miscellaneous points including the example usage and excellent explanation: 2/2 marks

{
  "score": 9
}

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

The provided solution is a good implementation that meets the requirements of the problem. Let's evaluate it based on the marking scheme:

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

Breakdown of the marks:

The solution defines two dataclasses, 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 "%m/%d/%Y". (1 mark)
The decimal field uses as_string=True. (1 mark)
The schema and dataclasses are linked with a @post_load method on the schema classes. (2 marks)
The solution includes error handling and provides a clear implementation. (2 marks)

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

The solution scores a total of 10 out of 10 based on the provided marking scheme.

openai/gpt-4-1106-preview says: (9.0) (1.90¢)

{
  "evaluation": {
    "marks 1": 2,
    "marks 2": 2,
    "marks 3": 0,
    "marks 4": 1,
    "marks 5": 2,
    "marks 6": 2,
    "total_score": 9
  }
}

Comment:
- The solution correctly defines two data classes for the person and the order, hence full marks for marks 1.
- Two schema classes are defined correctly, so full marks for marks 2.
- The date format for date_of_birth field was not specified as %m/%d/%Y which is a requirement. Therefore, no marks for marks 3.
- The as_string=True was correctly included for the amount field, so full marks for marks 4.
- The schemas and dataclasses are linked with the @post_load decorator correctly implemented, thus earning full marks for marks 5.
- Error handling using try...except, and clear explanation for steps taken in comments are part of anything else, along with a practical example of usage, which awards full marks for marks 6.

Given the correct implementation of most requirements except for specifying the date format, the solution receives a high score.

Response 2 (9.0)

To accomplish your goal using the marshmallow library in Python, you need to define dataclasses for your entities and then use marshmallow Schemas to handle the serialization and deserialization of your JSON data, converting string dates to date objects, ISO format strings to datetime objects, and string representations of numbers to Decimal objects.

Here's a step-by-step example:

Step 1: Install Marshmallow

First, ensure you have marshmallow installed in your environment. If not, you can install it using pip:

pip install marshmallow

Step 2: Define Your Data Classes

In Python 3.7 and above, you can use the dataclasses module to define your data structures.

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

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

Step 3: Create Marshmallow Schemas

Marshmallow Schemas will define how to load or dump your instances to and from JSON, including custom field handling for date, datetime, and Decimal.

from marshmallow import Schema, fields, post_load, EXCLUDE

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

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

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

    @post_load
    def make_customer(self, data, **kwargs):
        return Customer(**data)

    class Meta:
        unknown = EXCLUDE

Step 4: Loading JSON Data

Now, using the schemas defined above, you can load your JSON data and automatically convert date_of_birth to a date object, created to a datetime object, and amount to a Decimal.

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"
    }
  ]
}
"""

# Parse the JSON data
data = json.loads(json_data)

# Deserialize the data to our Python dataclass instances
customer_schema = CustomerSchema()
customer = customer_schema.load(data)

print(customer)
# You'll see an instance of the Customer dataclass with the appropriate conversions.

This approach provides a clear separation between your data definitions and the serialization/deserialization logic, making it easier to maintain and adapt as your application grows. Marshmallow's @post_load decorator ensures that once the data is loaded, it's automatically converted into your dataclass instances, providing a neat and powerful mechanism for handling complex nested structures and data types.

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) (0.96¢)

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

The solution defines two dataclasses, one for the person (Customer) and one for the order (Order). (2 marks)
The solution defines two schema classes, CustomerSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have included format="%m/%d/%Y" in the field definition. (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 clear explanation of the steps involved.
It includes an example of how to use the schemas to load JSON data.
It uses the EXCLUDE meta option to handle unknown fields.
(2 marks)

Breakdown of marks:

{
  "dataclasses": 2,
  "schema classes": 2,
  "date format": 0,
  "decimal field": 1,
  "post_load methods": 2,
  "additional points": 2
}

Overall, this is a good solution that covers most of the requirements. The main issue is the missing date format for the date_of_birth field. The solution provides a comprehensive approach to handling the JSON data with proper type conversions.

Score:

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

anthropic/claude-3-haiku says: (10.0) (0.08¢)

The provided solution is a comprehensive and well-structured approach to the problem using the Marshmallow library in Python. Let's evaluate it against the given marking scheme:

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

Breakdown:

The solution defines two dataclasses, one for the Order and one for the Customer, which satisfies the requirement. (2 marks)
The solution defines two schema classes, one for the Order and one for the Customer, which satisfies the requirement. (2 marks)
The solution specifies the date format for the date_of_birth field using the %m/%d/%Y format, which meets the requirement. (1 mark)
The solution uses the as_string=True option for the Decimal field, which meets the requirement. (1 mark)
The solution links the schema and dataclasses using the @post_load method on the schema classes, which satisfies the requirement. (2 marks)
The solution includes additional details, such as the installation of Marshmallow, the usage of the dataclasses module, and the loading of JSON data, which demonstrate a thorough understanding of the problem and the implementation. (2 marks)

Total Score:

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

The provided solution meets all the requirements specified in the marking scheme and demonstrates a comprehensive understanding of the problem and the use of Marshmallow. It receives a total score of 10 out of 10.

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

The provided solution to serialize and deserialize JSON data using Python's marshmallow library and dataclasses is evaluated against the specified criteria:

Define Two Dataclasses: The solution defines two dataclasses, Order and Customer, which are appropriately structured to map to the JSON data structure.
Score: 2/2
Define Two Schema Classes: The solution includes OrderSchema and CustomerSchema, which handle the serialization and deserialization processes for their corresponding dataclasses.
Score: 2/2
Specify Date Format: Though the schema correctly uses the fields.Date() for the date_of_birth field, it does not specify the format ("%m/%d/%Y") as required to ensure correct parsing of the date string from the JSON data.
Score: 0/1
Decimal Field Handling (as_string=True): The amount field in OrderSchema is handled with fields.Decimal(as_string=True), ensuring that the serialization retains the decimal number as a string which matches the requirement.
Score: 1/1
Link Schema and Dataclasses with @post_load Method: Both schemas contain a @post_load method that reconstructs the dataclass instances (Order and Customer) from the loaded data, which is perfectly in line with the requirement.
Score: 2/2
Additional Aspects:
Correct usage of methods and marshmallow capabilities such as EXCLUDE for unknown fields handling ensures clean handling of potentially malformed or extraneous input data.
Good integration of Python's features such as dataclasses and precise marshmallow field specifications, although the date format issue is a miss.
Score: 2/2

Final Assessment:

The solution effectively builds upon the Python dataclasses and the marshmallow library to create a robust system for handling complex JSON structures. The main flaw is the lack of date format specification which could lead to errors in date parsing if the input varies from expectations. Overall, the solution lays down a strong foundation for JSON data handling according to the requirements.

{
  "score": 9
}

openai/gpt-3.5-turbo-0125 says: (10.0) (0.09¢)

This solution is well-structured and provides a clear step-by-step guide on how to achieve the desired outcome using the marshmallow library in Python. It includes the definition of data classes for the entities, creation of marshmallow schemas, and loading JSON data with the desired conversions.

The solution appropriately defines two data classes for the person and order entities, followed by the definition of two schema classes for serialization and deserialization. The date format for the date_of_birth field, decimal field with as_string=True, and linking of schema and data classes with a @post_load method are all correctly implemented.

Overall, the solution provides a comprehensive and effective approach to handling the JSON data transformation requirements using marshmallow in Python.

Breakdown of Marks:
- Defining dataclasses: 2 marks
- Defining schema classes: 2 marks
- Specifying date format for date_of_birth: 1 mark
- Decimal field with as_string=True: 1 mark
- Linking schema and dataclasses with @post_load: 2 marks
- Clear, step-by-step explanation and guidance: 2 marks

Therefore, the total score for this solution is 10 out of 10.

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

openai/gpt-4-1106-preview says: (6.0) (2.14¢)

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

Comments on the solution:

Two dataclasses are defined correctly for both a person (named Customer) and an order (Order), hence full marks for dataclass definition.
Two schema classes are defined for both Customer and Order, complying with the requirement.
The solution does not specify the date format for the date_of_birth field using the required format (format="%m/%d/%Y"), missing a crucial detail.
The decimal field has as_string=True defined correctly in the OrderSchema.
The schema and dataclasses are linked with the @post_load method, but the method does not convert the nested list items, and therefore partial credit is awarded here because the linkage is not correctly handling nested objects as per the problem statement.
Missed considerations in the "Anything else" category include:
The data_of_birth field in the CustomerSchema should specify the format to parse the date correctly.
The created field in the OrderSchema does not require a particular format since the provided format is already ISO 8601-compatible which DateTime handles by default.

Overall, the solution covers the basics well and correctly outlines the use of marshmallow with dataclasses. It does fall short in correctly specifying the date format and constructing nested dataclass instances, thus the score is 6 out of 10.

Response 3 (9.0)

To achieve the data serialization and deserialization with the data types you specified (i.e., converting strings to date, datetime, and decimal types), you can utilize the marshmallow library in Python. Below is a step-by-step guide on how to define your data classes using Marshmallow schemas, perform conversions, and work with nested structures.

First, ensure that you have Marshmallow installed in your environment:

pip install marshmallow

Then, we can define the User and Order data classes along with their corresponding Marshmallow schemas:

from dataclasses import dataclass
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load, validates_schema, ValidationError

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

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

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

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

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

# Example of how to use these schemas to deserialize JSON to the data classes
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

# Create a UserSchema instance
user_schema = UserSchema()

# Deserialize the JSON data to User dataclass instance
user = user_schema.load(json_data)

print(user)

Key Points:

date_of_birth and created fields: To convert string dates and times to Python date and datetime objects respectively, we use Marshmallow's fields.Date() and fields.DateTime().
amount field: To convert the string amount to a Decimal, we use fields.Decimal(as_string=True). This ensures that the amount is properly converted and handled as a decimal, preserving the precision for financial data.
Nested Objects Handling: The OrderSchema is used within UserSchema to handle the nested orders list. This nested structure allows for a clean separation of concerns and accurately models the JSON data structure.
@post_load Decorator: This decorator is used to instantiate your data classes after Marshmallow has loaded the data. This helps in directly getting instances of your data classes from the loaded JSON.

This setup gives you a robust way of handling JSON serialization/deserialization that respects specific data types, especially useful for dates, datetimes, and decimals.

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) (1.09¢)

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

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema and OrderSchema. (2 marks)
The solution doesn't explicitly 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 an example of how to use the schemas.
It includes explanatory comments.
(2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 1,
  "Schema-dataclass linking": 2,
  "Additional aspects": 2
}

Total 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 correctly defines the dataclasses and schema classes, uses the @post_load decorator to link schemas with dataclasses, and properly configures the decimal field.

The main oversight is the lack of a specific date format for the date_of_birth field. To fully meet the requirements, the solution should have used fields.Date(format="%m/%d/%Y") in the UserSchema.

Despite this minor issue, the solution demonstrates a good understanding of Marshmallow and how to use it for JSON deserialization with specific data types. The inclusion of an example and explanatory comments adds value to the solution.

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

Overall, the provided solution is well-structured and demonstrates a good understanding of using the Marshmallow library in Python for data serialization and deserialization. The solution covers the necessary steps to define data classes, create schemas for serialization/deserialization, handle specific data types like dates, datetimes, and decimals, and how to work with nested structures effectively.

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
}

Score:

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

The solution has effectively addressed the problem requirements and provided a clear and concise implementation using the Marshmallow library, earning a total score of 10 out of 10.

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

The provided solution indeed addresses most of the problem requirements and is well-explained. Here are the evaluations based on the given marking scheme:

Two dataclasses defined for the person and the order:
The solution correctly defines Order and User dataclasses. (+2 marks)
Two schema classes defined for the person and the order:
OrderSchema and UserSchema are accurately defined. (+2 marks)
Specify the date format for the date_of_birth field is missing:
The solution does not specify the date format for date_of_birth as %m/%d/%Y. (0 marks)
The decimal field using as_string=True:
Properly included in OrderSchema for the amount field as fields.Decimal(as_string=True). (+1 mark)
Link schema and dataclasses with @post_load method:
Both schemas include a @post_load method (make_order and make_user) correctly connecting the loaded data to dataclasses. (+2 marks)
Miscellaneous/Any other points:
Nested handling is correctly implemented with fields.List(fields.Nested(OrderSchema)) in UserSchema.
Usage of date and datetime marshmallow fields for appropriate conversion.
The solution includes an example of usage of the schemas which helps in illustrating how to deserialize data.
However, date_of_birth format oversight is crucial, given that without specifying the exact format, errors can occur during date conversion. (+1.5 marks)

Based on the detailed breakdown, the overall evaluation is as follows:

{
  "score": 8.5
}

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

The provided solution is a well-designed and comprehensive approach to solving the given problem using the Marshmallow library in Python. Let's break down the evaluation:

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

The solution correctly defines two dataclasses, User and Order, as required, which fulfills the first two criteria.

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

The solution also correctly defines two schema classes, UserSchema and OrderSchema, which align with the respective dataclasses, meeting the next two criteria.

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

The solution specifies the correct date format for the date_of_birth field using the fields.Date() function with the default date format, and it also correctly uses fields.Decimal(as_string=True) for the amount field, meeting the next two criteria.

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

The solution uses the @post_load decorator on the schema classes to link the schema and dataclasses, which is a clean and effective approach, meeting the next two criteria.

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

The overall solution is well-structured, easy to understand, and demonstrates a good understanding of using Marshmallow for data serialization and deserialization. The code is well-documented and covers all the required aspects of the problem.

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

The solution provided covers all the aspects mentioned in the marking scheme and demonstrates a thorough understanding of the problem. The solution receives a total score of 10 out of 10.

openai/gpt-4-1106-preview says: (8.0) (2.02¢)

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

{
  "scores": [2, 2, 0, 0, 2, 2],
  "total_score": 8
}

Comment:

The first two marks are awarded because the solution correctly defines two dataclasses for the person (User) and the order (Order).
Another two marks are awarded because the solution defines two corresponding schema classes, OrderSchema and UserSchema.
Unfortunately, marks are deducted as the date format for the date_of_birth field is not specified (format="%m/%d/%Y") in the schema as required.
Another deduction occurs because the as_string parameter for the amount field should be specified as as_string=True in the OrderSchema, but it was not.
The linked @post_load methods are properly included in both schema classes to instantiate the dataclasses from the loaded data, thus gaining those marks.
Additional marks are awarded for a functional implementation of nested objects, handling of JSON data cleanly, and informative comments throughout the code that explain the usage of Marshmallow's features.

Overall, although the solution did define the necessary dataclasses and utilized @post_load hooks correctly, it failed to meet specifics for field types such as the date format for date_of_birth and ensuring the amount is represented as a string.

1.00	0.88	0.91
0.88	1.00	0.86
0.91	0.86	1.00

OpenAI: GPT-4 Turbo Preview

Description

Step 1: Install Marshmallow

Step 2: Define Your Data Classes

Step 3: Create Marshmallow Schemas

Step 4: Loading JSON Data

Key Points:

Score:

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