ReMM SLERP 13B (extended)

Description

A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge

Note: this is an extended-context version of ReMM SLERP 13B. It may have higher prices and different outputs.

Prompt

I need to take this json:

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

And generate some dataclasses.

date_of_birth needs to be a date.
created is a datetime.
amount is a decimal.

How would I do this using the marshmallow library in python?

To accomplish this using Marshmallow, you would need to define two custom dataclasses that match the first_name, last_name, date_of_birth, orders, and their respective nested fields. Here's an example implementation:

from datetime import datetime
from marshmallow import Schema, fields, post_load, post_dump, ValidationError

class Person(Schema):
    class Meta:
        fields = ['first_name', 'last_name', 'date_of_birth', 'orders']

    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format="%m/%d/%Y")
    orders = fields.List(fields.Nested(Order))

class Order(Schema):
    class Meta:
        fields = ['created', 'amount']

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

def __post_load_orders(self, data, *args, **kw):
    return data.get('orders', [])

def __post_dump_orders(self, obj, *args, **kw):
    return {'orders': [order.to_dict() for order in obj.orders]}

Person.orders.post_load = __post_load_orders
Person.orders.post_dump = __post_dump_orders

With this implementation, you can now deserialize the given JSON into a Person object:

```python
json_data = {'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth

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) (0.91¢)

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

  1. Defining two dataclasses:
    The solution doesn't define dataclasses. It defines Schema classes instead.

  2. Defining two schema classes:
    The solution correctly defines two schema classes: Person and Order.

  3. Date format for date_of_birth:
    The solution correctly specifies the date format for date_of_birth as "%m/%d/%Y".

  4. Decimal field with as_string=True:
    The solution doesn't include as_string=True for the decimal field.

  5. Linking schema and dataclasses with @post_load:
    The solution attempts to use post_load, but it's not implemented correctly. It's defined as a separate function and attached to the schema field, not the schema class.

  6. Additional points:

  7. The solution attempts to handle serialization and deserialization of orders using post_load and post_dump methods.
  8. The implementation is incomplete as it doesn't show how to use the schemas to deserialize the JSON data.

Here's the breakdown of marks:

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

Final score:

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

The solution shows a good understanding of Marshmallow schemas but misses some key requirements. It doesn't use dataclasses as requested, and there are issues with the implementation of post_load and the decimal field. The attempt to handle orders serialization and deserialization is noteworthy but not fully correct. Overall, the solution provides a partial implementation of the required functionality.

openai/gpt-4-0125-preview says: (4.0) ()

The provided solution attempts to use the Python Marshmallow library to deserialize JSON into Python data types using a schema, but there are several inconsistencies and errors which do not meet the requirements as described in the problem statement:

  1. The solution defines schema classes (Person and Order), but it does not define dataclasses. Marshmallow schemas are used for serialization and deserialization, not as the model data structure itself. (0/2 marks)

  2. Two schema classes are defined (Person and Order), which is correct according to the problem requirements. (2/2 marks)

  3. The date_of_birth field in the Person class correctly specifies the date format as "%m/%d/%Y". (1/1 mark)

  4. The amount field in the Order schema should be set with as_string=True to ensure that it is handled as a string; this specification is missing. (0/1 mark)

  5. The use of @post_load and @post_dump methods to link the schema and potential dataclasses is attempted, but incorrect. The methods are not decorated correctly, and their implementations are lacking proper handling for creating and dumping instances of the orders. Moreover, the methods are not part of the class definitions, and there's no clear dataclass to which the deserialized data could be mapped effectively. (0/2 marks)

  6. Additional aspects like proper error handling or integration of the defined schemas in a real context (such as a REST API or a data processing script) are not shown or mentioned in the solution. The main functionality, i.e., deserializing the JSON and defining suitable classes for handling the data robustly, is also incomplete or erroneous. (1/2 marks)

Overall, while the solution attempts to address some aspects of the defined problem, it fails to complete others correctly and lacks proper implementation strategies for effective data modeling or deserialization.

{
  "score": 4
}

You can use Marshmallow's fields module to define the expected data types for each field in the JSON response, and then use the AutoSchema class to automatically generate the dataclasses based on the schema. Here's an example code snippet that does this:

```python
import marshmallow
from marshmallow import fields

class CustomerSchema(marshmallow.Schema):
class Meta:
unknown = marshmallow.EXCLUDE

first_name = marshmallow.fields.Str(required=True)
last_name = marshmallow.fields.Str(required=True)
date_of_birth = marshmallow.fields.Date(format='%m/%d/%Y')
orders = marshmallow.fields.Nested('OrderSchema')

class OrderSchema(marshmallow.Schema):
class Meta:
unknown = marshmallow.EXCLUDE

created = marshmallow.fields.DateTime()
amount = marshmallow.fields.Decimal(required=True, places=2)

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

data = marshmallow.validate(customers, CustomerSchema(many=True))
for customer in data:
print(customer)

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) (0.97¢)

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

  1. The solution doesn't define dataclasses explicitly. It uses schemas but doesn't create dataclasses. (0/2 marks)

  2. The solution correctly defines two schema classes: CustomerSchema and OrderSchema. (2/2 marks)

  3. The solution correctly specifies the date format for the date_of_birth field using format='%m/%d/%Y'. (1/1 mark)

  4. The decimal field (amount) is defined, but it doesn't use as_string=True. It uses places=2 instead. (0/1 mark)

  5. The solution doesn't use @post_load method to link schemas and dataclasses. (0/2 marks)

  6. Additional points:

  7. The solution uses unknown = marshmallow.EXCLUDE in Meta classes, which is good for handling unknown fields.
  8. It demonstrates how to validate data using marshmallow.validate().
    (1/2 marks)

Comments:
The solution provides a good start with correct schema definitions and proper date format specification. However, it misses some key requirements:
- It doesn't create dataclasses as requested.
- The decimal field misses the as_string=True parameter.
- There's no @post_load method to create dataclass instances from validated data.
- The solution focuses more on validation than on creating dataclasses from the JSON data.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 1,
  "Decimal field configuration": 0,
  "Post_load method usage": 0,
  "Additional features": 1
}

Final score:

{
  "scores": [0, 2, 1, 0, 0, 1],
  "total_score": 4
}

The solution scores 4 out of 10 based on the given marking scheme.

openai/gpt-4-0125-preview says: (4.0) ()

The provided solution exhibits an attempt to use the Marshmallow library for parsing and validating a given JSON structure into Python objects. Here is an evaluation based on the provided criteria:

  1. Definition of two dataclasses, one for the person and one for the order:

  2. The solution doesn't explicitly define Python data classes (dataclasses). It uses Marshmallow schema to define the structure but does not create any dataclass. (0/2)

  3. Definition of two schema classes, one for the person and one for the order:

  4. There are two schema classes defined: CustomerSchema for the person and OrderSchema for the order. This meets the requirement. (2/2)

  5. Specification of the date format for the date_of_birth field:

  6. The date_of_birth field in the CustomerSchema correctly uses a date format specification (format='%m/%d/%Y'). (1/1)

  7. The decimal field needs as_string=True:

  8. The amount field in OrderSchema is defined as a decimal but does not include the as_string=True property, which is needed to ensure its serialized as a JSON string. (0/1)

  9. Linking the schema and dataclasses with a @post_load method:

  10. The solution does not implement any @post_load methods in either CustomerSchema or OrderSchema. This method would be required to convert the loaded data into instances of data classes, which were also not defined in the solution. (0/2)

  11. Additional elements:

  12. The code incorrectly uses a method marshmallow.validate, which does not exist. Correct usage would involve creating an instance of a schema and calling either load or dump methods.
  13. Additionally, the handling of nested orders in the CustomerSchema through a proper nested field using 'OrderSchema' string reference is correctly implemented. (1/2)

Based on the provided solution and the evaluation rubric, the score is calculated as:

{
  "score": 4
}

To generate dataclasses from the given JSON data using Marshmallow, you can use the DataClassSerializer class provided by the library. Here's an example:

from marshmallow import DataClassSerializer, fields, ValidationError

# Define custom dataclasses
class Person(dataclasses.MangoSchema):
    first_name: str
    last_name: str
    date_of_birth: date = fields.DateTime(format='MM/DD/YYYY')
    orders: list[Order] = fields.Nested(OrderSchema())

class Order(dataclasses.MangoSchema):
    created: datetime = fields.DateTime()
    amount: decimal = fields.Decimal(precision=2, scale=2)

# Create a serializer instance for the person dataclass
person_serializer = Person()

# Parse the JSON data into a dataclass object
try:
    person = person_serializer.load({
        'first_name': 'Jane',
        'last_name': 'Smith',
        'date_of_birth': '03/16/1977',
        'orders': [
            {'created': '2024-01-25T15:00:04', 'amount': '100.00'},
        ],
    })
except ValidationError as e:
    # Validation error if the input data doesn't match the schema
    print(e)

# Output: Person object with loaded data
print(person)

In this example, I've used two custom dataclasses: Person and Order. The Person class has two attributes (first_name and last_name) that are ordinary strings

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: (0.5) ()

Evaluation of the Proposed Solution:

  1. Definition of Two Dataclasses:
  2. The solution attempts to define dataclasses (Person and Order) but incorrectly inherits from dataclasses.MangoSchema which seems to be a typographical or conceptual error. Standard Python dataclasses should be used and the correct parent class in the context of marshmallow is typically a marshmallow.Schema.
  3. Actual dataclasses imports and decorators are missing.

  4. Score: 0/2

  5. Definition of Two Schema Classes:

  6. The solution does not define separate schema classes correctly but appears to be mixing up dataclasses ideas with schema.

  7. Score: 0/2

  8. Specification of the Date Format for the date_of_birth Field:

  9. Incorrect format 'MM/DD/YYYY' used, and incorrect field data type (fields.DateTime instead of fields.Date).

  10. Score: 0/1

  11. Decimal Field as_string=True:

  12. The amount field as a decimal is present but the parameter as_string=True is missing, which is required to treat it properly as a string when loading or dumping.

  13. Score: 0/1

  14. @post_load method for Linking the Schemas and Dataclasses:

  15. No @post_load method is implemented in the solution, which is crucial for constructing the dataclass instances with nested structures properly after loading data.

  16. Score: 0/2

  17. Additional Elements:

  18. Error in terminology and syntax which further complicates the solution's accuracy.
  19. There's an attempt to serialize and handle errors which shows an understanding of marshmallow's typical usage, so partial credit here.
  20. Score: 0.5/2

Overall, the key components required for the marshmallow serialization like defining the correct schema classes, proper usage of the fields, and implementing @post_load are missing or incorrectly implemented. Additionally, significant errors in basic setup and understanding of marshmallow’s capacities and requirements were obvious.

{
  "score": 0.5
}

anthropic/claude-3.5-sonnet says: (2.0) (0.90¢)

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

  1. Defining two dataclasses (Person and Order):
    The solution attempts to define two dataclasses, but incorrectly uses dataclasses.MangoSchema instead of dataclass.dataclass.

  2. Defining two schema classes:
    The solution doesn't explicitly define separate schema classes. Instead, it attempts to combine the schema and dataclass definitions.

  3. Specifying date format for date_of_birth:
    The solution attempts to specify the date format, but uses 'MM/DD/YYYY' instead of the required "%m/%d/%Y".

  4. Decimal field with as_string=True:
    The solution doesn't include as_string=True for the decimal field.

  5. Linking schema and dataclasses with @post_load:
    The solution doesn't include @post_load methods.

  6. Other observations:

  7. The solution uses DataClassSerializer, which is not a standard Marshmallow class.
  8. The Order class is referenced before it's defined.
  9. The solution attempts to use Marshmallow fields directly in the dataclass definitions, which is incorrect.

Here's the breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 0,
  "Date format": 0,
  "Decimal field": 0,
  "@post_load": 0,
  "Other": 1
}

Final score:

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

The solution shows an understanding of the basic concept but has several significant errors and misunderstandings about how to properly use Marshmallow with dataclasses. It would need substantial revisions to correctly solve the problem as specified.

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