Google: Gemini Pro 1.0

Description

Google's flagship text generation model. Designed to handle natural language tasks, multiturn text and code chat, and code generation.

See the benchmarks and prompting guidelines from Deepmind.

Usage of Gemini is subject to Google's Gemini Terms of Use.

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 (5.0)

from marshmallow import Schema, fields, validates, ValidationError
from datetime import datetime, date

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

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

    @validates('date_of_birth')
    def validate_date_of_birth(self, value):
        try:
            datetime.strptime(value, '%m/%d/%Y')
        except ValueError:
            raise ValidationError('Invalid date format. Please use MM/DD/YYYY.')

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

# Create the schema
schema = CustomerSchema()

# Validate and load the data
try:
    customer = schema.load(data)
except ValidationError as err:
    print(err.messages)
else:
    print(customer)

This code defines two dataclasses: OrderSchema and CustomerSchema. OrderSchema handles the created and amount fields, while CustomerSchema handles the entire customer object. The validate_date_of_birth method ensures that the date_of_birth field is in the correct format. Finally, the code loads the JSON data, validates it using the schema, and prints the validated data.

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)

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

Evaluation of the Provided Solution

Dataclasses Definition:
The solution does not define any Python 'dataclass', it instead defines classes that derive from Schema (Marshmallow schema classes), which are not dataclasses according to Python's dataclasses library.
Score: 0/2
Schema Classes Definition:
The solution correctly defines two schema classes using Marshmallow (OrderSchema and CustomerSchema).
Score: 2/2
Date Format Specification for date_of_birth:
Although there is a validator for enforcing the date format, the schema does not explicitly specify the format using Marshmallow conventions (fields.Date(format="%m/%d/%Y")). The validator provides a runtime check but does not prescribe serialization/deserialization format directly in the schema definition.
Score: 0/1
Decimal Field as String:
The field amount in OrderSchema should have the attribute as_string=True which is not present in the provided solution. Therefore, the Conditions were not fully implemented for the Decimal field.
Score: 0/1
Link Schema and Dataclasses with @post_load:
There is no @post_load method implemented that would convert the loaded data into a structured dataclass instance or similar structure. The solution missed this requirement.
Score: 0/2
Additional Considerations:
The use of validates for validating the date format on the schema looks good.
However, the actual instantiation of the data, error handling, and integration of schemas are not carried out using advanced features of marshmallow like @post_load.
Score: 1/2

Overall, the solution demonstrates basic usage of the marshmallow library for defining schema classes and handling validations. However, it falls short in several key areas, mainly around properly defining and handling data structure conversion and the specific handling of some field data types.

Total Score

{
  "score": 3
}

anthropic/claude-3.5-sonnet says: (5.0) (0.95¢)

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

The solution defines two schema classes (OrderSchema and CustomerSchema), but it doesn't define dataclasses. (1 mark out of 2)
The solution correctly defines two schema classes: OrderSchema and CustomerSchema. (2 marks)
The solution doesn't specify the date format for the date_of_birth field using format="%m/%d/%Y". Instead, it uses a custom validator. (0 marks out of 1)
The decimal field doesn't include as_string=True. (0 marks out of 1)
The solution doesn't use @post_load to link schema and dataclasses. (0 marks out of 2)
Additional points:
The solution includes a custom validator for date_of_birth.
It demonstrates how to use the schema to load and validate data.
(2 marks)

Comments:
The solution provides a good start with marshmallow schemas, but it misses some key requirements. It doesn't use dataclasses, which was a central part of the task. The date format for date_of_birth is handled through a custom validator rather than the specified format parameter. The decimal field is missing the as_string=True parameter. Lastly, there's no use of @post_load to create dataclass instances. However, the solution does show a good understanding of schema creation and data validation with marshmallow.

Here's the breakdown of marks:

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

Final score:

{
  "scores": [1, 2, 0, 0, 0, 2],
  "total_score": 5
}

Response 2 (4.0)

from marshmallow import Schema, fields, validates, ValidationError
from datetime import date, datetime
from decimal import Decimal

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

class UserSchema(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)

    @validates("date_of_birth")
    def validate_date_of_birth(self, date_of_birth):
        try:
            date.fromisoformat(date_of_birth)
        except ValueError:
            raise ValidationError("Invalid date format. Expected YYYY-MM-DD.")

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

    # Load data and validate
    user_data = UserSchema().load(user_data)

    print(user_data)

This code defines two dataclasses, OrderSchema and UserSchema, using the marshmallow library. OrderSchema defines the nested order structure, while UserSchema defines the overall user structure. Each dataclass specifies the appropriate field types, which in this case includes Datetime, Date, and Decimal.

Additionally, the code includes a validator function validate_date_of_birth to ensure the provided date is in the YYYY-MM-DD format.

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: (4.0) (0.98¢)

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

The solution defines two schema classes (OrderSchema and UserSchema), but it doesn't define dataclasses. It only uses marshmallow schemas.
The solution correctly defines two schema classes: OrderSchema and UserSchema.
The solution doesn't specify the date format for the date_of_birth field as "%m/%d/%Y". Instead, it uses the ISO format (YYYY-MM-DD) and includes a custom validator.
The decimal field (amount) is not specified with as_string=True.
The solution doesn't include @post_load methods to link schemas with dataclasses.
Additional points:
The solution includes a custom validator for the date_of_birth field.
It provides a sample usage of the schema in the if __name__ == "__main__": block.

Here's the breakdown of marks:

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

Total score:

{
  "scores": [0, 2, 0, 0, 0, 2],
  "total_score": 4
}

Comments:
The solution partially addresses the problem by correctly defining schema classes using marshmallow. However, it misses several key requirements:
1. It doesn't define dataclasses, only schemas.
2. The date format for date_of_birth is not as specified (it uses ISO format instead of "%m/%d/%Y").
3. The decimal field is not specified with as_string=True.
4. There's no @post_load method to link schemas with dataclasses.

While the solution includes a custom validator and demonstrates usage, it doesn't fully meet the specified requirements. The total score reflects these shortcomings.

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

The provided solution uses marshmallow to define schemas for deserialization and validation but misses several key requirements set in the problem:

Dataclasses Definition:
The solution provides the schema definitions (OrderSchema and UserSchema) but does not define actual Python dataclasses. As per the problem statement, the solution should have defined dataclasses for the 'Person' and 'Order'.
Score for this: 0/2
Schema Classes Definition:
The schemas for Orders and Users are created with OrderSchema and UserSchema.
Correctly implemented: 2/2
Date Format Specification:
The solution involves validation for date_of_birth as an ISO format (YYYY-MM-DD) as indicated in the custom validator validate_date_of_birth, which contradicts the required "%m/%d/%Y" format mentioned in the problem.
Score for this: 0/1
Decimal Field as_string:
The solution does not set as_string=True for the amount field in the OrderSchema, which means amounts would not be serialized correctly as strings.
Score for this: 0/1
Linking Schema and Dataclasses with @post_load:
There are no @post_load methods implemented to convert loaded data into instances of dataclasses. This step is crucial for the integration of marshmallow schemas with actual dataclass instances.
Score for this: 0/2
Additional Considerations:
Validates the date, but it's using the wrong format as per the requirement.
Incorrect date format handling, lack of as_string on the decimal field, missing @post_load methods, and not defining actual dataclasses limits the functionality and correctness.
Understanding of marshmallow concepts like nested schemas and simple validation is shown.
Score for this: 1/2

Overall, the solution implemented certain aspects relatively well such as schema structuring and validation using marshmallow, but it failed in implementing critical requirements from the task description.

Final score:

{
  "score": 3
}

Response 3 (5.0)

from datetime import date
from decimal import Decimal
from marshmallow import Schema, fields, ValidationError

class Order(Schema):
    created = fields.DateTime(format="%Y-%m-%dT%H:%M:%S")
    amount = fields.Decimal()

class Person(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date(format="%m/%d/%Y")
    orders = fields.List(fields.Nested(Order))

try:
    data = {
        "first_name": "Jane",
        "last_name": "Smith",
        "date_of_birth": "03/16/1977",
        "orders": [
            {
                "created": "2024-01-25T15:00:04",
                "amount": "100.00"
            }
        ]
    }
    person_schema = Person()
    person = person_schema.load(data)
    print(person)
except ValidationError as err:
    print(err.messages)

This code defines two dataclasses:

Order: Represents a single order with a creation timestamp and amount.
Person: Represents a person with a first name, last name, date of birth, and a list of orders.

The marshmallow.Schema class is used to define the structure of these dataclasses. The fields attribute specifies the fields that each dataclass should have.

The fields.DateTime, fields.Date, and fields.Decimal fields are used to specify the data types for the created, date_of_birth, and amount fields, respectively.

The fields.List field is used to specify that the orders field should be a list of Order dataclasses.

The person_schema.load(data) method is used to deserialize the JSON data into a Person dataclass.

The print(person) statement prints the deserialized Person dataclass.

If there are any validation errors, the ValidationError exception will be raised. The err.messages attribute will contain a dictionary of error messages.

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: (5.0) (0.96¢)

Thank you for providing the solution. I'll evaluate it based on the marking scheme you've provided:

The solution does not explicitly define dataclasses. It only defines Schema classes. (0/2 marks)
The solution correctly defines two schema classes: Order and Person. (2/2 marks)
The solution correctly specifies the date format for the date_of_birth field with format="%m/%d/%Y". (1/1 mark)
The solution does not include as_string=True for the decimal field. (0/1 mark)
The solution does not include @post_load methods to link schemas and dataclasses. (0/2 marks)
Additional points:
The solution correctly uses nested fields for the orders list.
It includes error handling with a try-except block for ValidationError.
The datetime format for the 'created' field is correctly specified.
(2/2 marks)

Comments:
The solution provides a good start with correct schema definitions and field types. However, it misses some key requirements:
- It doesn't use dataclasses, only schemas.
- The decimal field is missing the as_string=True parameter.
- There are no @post_load methods to create actual dataclass instances.

These omissions mean that while the solution can parse the JSON, it won't create the required dataclass objects.

Here's the breakdown of marks:

{
  "Dataclass definitions": 0,
  "Schema definitions": 2,
  "Date format specification": 1,
  "Decimal field specification": 0,
  "Post_load methods": 0,
  "Additional points": 2
}

Final score:

{
  "scores": [0, 2, 1, 0, 0, 2],
  "total_score": 5
}

openai/gpt-4-0125-preview says: (5.0) ()

The solution provided does attempt to solve the problem of deserializing JSON data using the marshmallow library; however, there are a few key inaccuracies and omissions:

Dataclasses vs Schemas: The solution incorrectly refers to Order and Person as dataclasses. In fact, they are schema classes. Python dataclasses are a separate feature and normally involve the @dataclass decorator from the dataclasses module. This distinction is crucial. (0/2 marks)
Schema classes definition: Although it mentions that Order and Person are schema classes, they are directly derived from Schema and defined correctly as classes, true to marshmallow structure. (2/2 marks)
Date format specification: The format for the date_of_birth field is correctly specified as "%m/%d/%Y". (1/1 marks)
Decimal field handling: The solution fails to set as_string=True for the amount field. This is a requirement to ensure the decimal value is serialized back into JSON as a string, maintaining precision. (0/1 marks)
Post-load linking: The solution does not utilize a @post_load method to connect schema classes to any corresponding dataclass or another structure, which means that loaded data will simply return a dictionary rather than an instance of a structured Python class. (0/2 marks)
Additional details such as error handling and print for checking outputs: The inclusion of a try-except block for handling ValidationError is good as it ensures robustness in real-world applications. The final serialization test with print(person) is also beneficial for debugging or verifying loaded data. (2/2 marks)

Overall, while the solution captures some aspects of using marshmallow effectively, it misses several critical elements of proper schema-to-object mapping and the detailed requirements of field types.

{
  "score": 5
}

1.00	0.91	0.87
0.91	1.00	0.87
0.87	0.87	1.00

Google: Gemini Pro 1.0

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Evaluation of the Provided Solution

Total Score

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