OpenAI: GPT-4 (older v0314)

Description

GPT-4-0314 is the first version of GPT-4 released, with a context length of 8,192 tokens, and was supported until June 14. Training data: up to Sep 2021.

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 (10.0)

First, you need to install the marshmallow library if you don't already have it. You can do this with pip:

pip install marshmallow

Now, you can use the marshmallow library to create data classes and perform serialization and deserialization of the JSON data. Here's the code:

import json
from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, post_load

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

# Data classes
class Order:
    def __init__(self, created: datetime, amount: Decimal):
        self.created = created
        self.amount = amount

class Person:
    def __init__(self, first_name: str, last_name: str, date_of_birth: date, orders: list[Order]):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

# Schemas
class OrderSchema(Schema):
    created = fields.DateTime(format="%Y-%m-%dT%H:%M:%S", required=True)
    amount = fields.Decimal(as_string=True, required=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(format="%m/%d/%Y", required=True)
    orders = fields.List(fields.Nested(OrderSchema), required=True)

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

# Deserialize the JSON data to Python objects
person_schema = PersonSchema()
person = person_schema.loads(json_str)

print(person.first_name)  # Output: Jane
print(person.last_name)   # Output: Smith
print(person.date_of_birth)  # Output: 1977-03-16
print(person.orders[0].created)  # Output: 2024-01-25 15:00:04
print(person.orders[0].amount)   # Output: 100.00

# Serialize the Python objects back to JSON
json_dump = person_schema.dumps(person)
print(json_dump)

This code demonstrates how to create data classes Order and Person and their corresponding schemas OrderSchema and PersonSchema. It uses the marshmallow library to deserialize the given JSON data to the data classes and then serialize them back to JSON.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (10.0) (1.01¢)

Let's evaluate this 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 specifies the date format for the date_of_birth field correctly with format="%m/%d/%Y". (1 mark)
The decimal field (amount) is correctly defined with 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 handles the created field as a datetime with the correct format.
It demonstrates deserialization and serialization of the data.
The solution includes a sample JSON input and demonstrates how to use the classes.
(2 marks)

Breakdown of marks:

{
  "dataclasses": 2,
  "schema classes": 2,
  "date_of_birth format": 1,
  "decimal field": 1,
  "post_load methods": 2,
  "additional features": 2
}

Score:

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

This solution fully addresses all the requirements specified in the marking scheme, demonstrating a comprehensive understanding of using marshmallow for JSON serialization and deserialization with custom data types. It correctly handles the date, datetime, and decimal fields as required, and links the schema classes to the dataclasses using @post_load decorators. The additional features, such as demonstrating usage and including imports, make it a complete and practical solution.

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

The solution provided correctly demonstrates the use of the marshmallow library in Python to define data classes and schema classes, along with serialization and deserialization of JSON data. Let's evaluate the solution against the marking scheme:

Dataclasses Definition: The solution defines two dataclasses – Order and Person. Both are well structured and relevant to the JSON data provided. (+2 marks)
Schema Classes Definition: Two schema classes – OrderSchema and PersonSchema – are appropriately defined to handle the data from their corresponding dataclasses. (+2 marks)
Date Format Specification: The date_of_birth field in PersonSchema correctly specifies the date format using format="%m/%d/%Y" which is essential for parsing the given date format in the JSON. (+1 mark)
Decimal Field as_string: In the OrderSchema, the amount field has as_string=True set for the Decimal type, ensuring the amount is parsed and serialized correctly as a string representation of a decimal. (+1 mark)
Linking Schema and Dataclasses with @post_load: Both schema classes utilize the @post_load method to instantiate their respective dataclasses, which is correct and effectively turns the loaded data into instances of Order and Person. (+2 marks)
Additional Points:
The provided JSON is well handled in terms of loading and dumping, demonstrating a practical implementation of both serialization and deserialization.
Error handling, comments, or more complex aspects, such as validation or custom field processing, are either minimal or not included.
However, the main functionalities are correct and essential parts like proper datetime and decimal handling are addressed.
The use of print statements to demonstrate the output correctness after deserialization and before serialization is a good practice for clarity and debugging. (+2 marks)

Overall, the solution meets the requirements laid out in the marking scheme with all key functionalities properly implemented.

{
  "score": 10
}

Response 2 (8.0)

You can do this using the marshmallow library by first installing it:

pip install marshmallow

Then you can create your data classes and schemas to deserialize the JSON data:

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

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

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

    @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)

# Deserialize the JSON data
input_json = """
{
  "first_name": "Jane",
  "last_name": "Smith",
  "date_of_birth": "1977-03-16",
  "orders": [
    {
      "created": "2024-01-25T15:00:04",
      "amount": "100.00"
    }]
}
"""
schema = PersonSchema()
result = schema.loads(input_json)
print(result)

As you can see in the code above, we create data classes for Order and Person with their respective fields and types. Then, we create schemas for each data class using marshmallow's Schema class and the appropriate field types.

Finally, we deserialize the JSON data using the PersonSchema and print the result.

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: (8.0) (0.96¢)

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

The solution correctly defines two dataclasses, Order and Person. (2 marks)
The solution correctly 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 been fields.Date(format="%m/%d/%Y"). (0 marks)
The decimal field in OrderSchema does not include as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are correctly linked with @post_load methods on both schema classes. (2 marks)
Additional observations:
The solution includes an example of how to use the schema to deserialize JSON data.
It imports necessary modules and types.
The solution provides a clear explanation of the code.
(2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclasses linking": 2,
  "Additional observations": 2
}

Score:

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

The solution provides a good foundation for using marshmallow to create dataclasses from JSON, but it misses two important details: specifying the correct date format for date_of_birth and setting as_string=True for the decimal field. These omissions could lead to issues when parsing the actual JSON data provided in the problem statement. Otherwise, the structure and implementation of the dataclasses and schemas are correct and well-explained.

openai/gpt-4-0125-preview says: (6.5) ()

The solution provided generally follows the correct usage of the marshmallow library to define dataclasses and schema classes for serialization and deserialization but has some mismatches and lacks some specific requirements from the problem statement. Let's assess it based on the given marking scheme:

Definition of two dataclasses, one for the person and one for the order: The provided solution clearly defines @dataclass for both Person and Order. (2 marks)
Definition of two schema classes, one for the person and one for the order: The provided solution includes both PersonSchema and OrderSchema, appropriately linked to their respective data classes. (2 marks)
Specification of the date format for the date_of_birth field: The solution does not specify the format for the date_of_birth field which is supposed to be "%m/%d/%Y", and it was required to handle the specified date format from the JSON example. (0 marks)
The decimal field needs as_string=True: The provided code does not include the as_string=True parameter for the Decimal field, which is necessary to correctly handle the amount as a string in JSON. (0 marks)
Linking schema and dataclasses with a @post_load method on the schema classes: The solution correctly implements the @post_load method in both PersonSchema and OrderSchema to create instances of their respective data classes. (2 marks)
Anything else: While other aspects such as the installation of marshmallow and initial logic flow were done correctly, there were additional errors such as the mismatched date_of_birth JSON format in the sample deserialization (JSON provided in the input has "03/16/1977" but "1977-03-16" was used for deserialization). The correct handling of nested data through fields.Nested(OrderSchema) was appropriately managed. Small points could be given for correctly structuring the grand flow and use of List and Nested fields properly. (0.5 marks)

Overall Score:

{
  "score": 6.5
}

Despite correct handling of dataclasses, schema declarations, and post-load methods, critical issues like the formatting of date and decimal fields weren't handled as required, impacting the functionality of the solution significantly.

Response 3 (7.5)

To generate data classes and use the marshmallow library for deserialization, you'll first need to install the library by running:

pip install marshmallow

Then you can create a Python script with the following code:

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


@dataclass
class Order:
    created: datetime
    amount: Decimal


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


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

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


class CustomerSchema(Schema):
    first_name = fields.String(required=True)
    last_name = fields.String(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.List(fields.Nested(OrderSchema), required=True)

    @post_load
    def make_customer(self, data, **kwargs):
        return Customer(**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"
    }
  ]
}
'''

# Deserialize JSON to a Customer object
customer_schema = CustomerSchema()
customer_obj = customer_schema.loads(json_data)

print(customer_obj)

This code defines two data classes, Order and Customer. The OrderSchema and CustomerSchema classes are marshmallow schemata for the respective data classes.

The json_data variable contains the JSON string you provided. We create a CustomerSchema instance and call loads() to deserialize the JSON string into a Customer object. The types for each field, such as date, datetime, and Decimal, are specified in the schemata using marshmallow fields.

This script will output the deserialized Customer object:

Customer(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])

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: (7.5) (1.04¢)

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

The solution defines two dataclasses, Order and Customer. (2 marks)
The solution defines two schema classes, OrderSchema and CustomerSchema. (2 marks)
The solution doesn't 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 doesn't have as_string=True. It should have been fields.Decimal(required=True, as_string=True). (0 marks)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules. (0.5 marks)
It provides a sample JSON and demonstrates how to use the schema to deserialize the data. (0.5 marks)
The solution uses type hints in the dataclasses. (0.5 marks)

Here's the breakdown of marks:

{
  "dataclasses": 2,
  "schema classes": 2,
  "date format": 0,
  "decimal as_string": 0,
  "post_load methods": 2,
  "additional points": 1.5
}

Final score:

{
  "scores": [2, 2, 0, 0, 2, 1.5],
  "total_score": 7.5
}

The solution demonstrates a good understanding of using dataclasses and marshmallow for JSON deserialization. However, it misses two important details: specifying the date format for date_of_birth and using as_string=True for the decimal field. These omissions could lead to errors when parsing the provided JSON data. Despite these issues, the overall structure and implementation of the solution are correct and demonstrate a solid grasp of the concepts.

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

The provided solution overall correctly sets up data serialization and deserialization using Marshmallow along with dataclasses. Here's the detailed evaluation:

Dataclasses Defined: The solution defines two data classes, Order and Customer, which reflects the structure needed for handling the given JSON data (2/2 marks).
Marshmallow Schema Classes Defined: The solution includes the definition of two schema classes, OrderSchema and CustomerSchema, fulfilling the requirement to define schema classes for the respective data classes (2/2 marks).
Date Format for date_of_birth Field: The CustomerSchema class includes a date_of_birth field as a Date field from Marshmallow fields, but it does not explicitly specify the format ("%m/%d/%Y”). This lacks alignment with the provided JSON date format and would result in an error if the format is not the default expected by marshmallow (0/1 mark).
Decimal field configuration with as_string=True: The solution does not use the as_string=True parameter for the Decimal field in OrderSchema. This parameter ensures that decimal numbers are serialized without losing precision, thus not completely fulfilling the specific requirement (0/1 marks).
Linkage of Dataclasses and Schema via @post_load: Both schema classes utilize the @post_load decorator correctly to instantiate data classes (Order and Customer) from deserialized data (2/2 marks).
Additional Requirements and Correctness: The solution correctly demonstrates how to handle JSON deserialization using Marshmallow and outputs the objects accurately. However, the additional requirement to match exact date formats and handle decimals appropriately was not met. There is a clear failure to parse date strings not in the default format and mishandling of decimals without specifying as_string=True (1/2 marks).

Conclusion: While the solution fulfills the major parts of the task, it fails in some finer details, particularly in date format parsing and decimal handling, which are crucial for ensuring data integrity and correct deserialization.

{
  "score": 7
}

1.00	0.94	0.85
0.94	1.00	0.87
0.85	0.87	1.00

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