MythoMax 13B (extended)

Description

One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge

Note: this is an extended-context version of MythoMax 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 generate dataclasses from the given JSON data using Marshmallow library in Python, you can use the marshmallow module to serialize and deserialize the data. Here's how you can do it:

from marshmallow import dataclass

from datetime import datetime, decimal


class Person(dataclass):
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: [Order
        created: datetime
        amount: decimal


Then, to create a person object from the JSON data, you can use the `load` method:
```python
person = Person.load(json_data)

and to serialize the object to JSON:

person.dump()

Here's the complete code to create the person object and serialize the JSON:

import marshmallow
from datetime import datetime, decimal
from marshmallow import Schema, fields
class PersonSchema(Schema):
    class Meta:
        order_schema = Schema(
            fields = {
                'created': fields.DateTime(),
                'amount': decimal
        }
    first_name: fields.Str()
    last_name: fields.Str()
    date_of_birth: fields.Date()

person = PersonSchema().loads(json_from_dict(
        {'first_name': 'Jane',
         'last_name': 'Smith',
         'date_of_birth': '03/17',
         'orders': [{'created': '24-25T15:04', 'amount': 10.0}
        }

`` This will create aPerson` object with the given JSON data and its corresponding dataclasses.

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: (3.0) (1.05¢)

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

  1. Defining two dataclasses (2 marks):
    The solution partially defines a Person dataclass but doesn't correctly define an Order dataclass. (1/2 marks)

  2. Defining two schema classes (2 marks):
    The solution defines a PersonSchema class but doesn't define a separate OrderSchema. (1/2 marks)

  3. Specifying date format for date_of_birth (1 mark):
    The solution doesn't specify the date format for the date_of_birth field. (0/1 marks)

  4. Decimal field with as_string=True (1 mark):
    The solution doesn't use as_string=True for the decimal field. (0/1 marks)

  5. Linking schema and dataclasses with @post_load (2 marks):
    The solution doesn't use @post_load to link schema and dataclasses. (0/2 marks)

  6. Anything else (2 marks):

  7. The solution attempts to use marshmallow and dataclass, which is a step in the right direction.
  8. However, there are several syntax errors and inconsistencies in the code.
  9. The solution doesn't correctly handle nested structures for orders.
    (1/2 marks)

Breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 1,
  "Date format": 0,
  "Decimal field": 0,
  "Post_load": 0,
  "Anything else": 1
}

Score:

{
  "scores": [1, 1, 0, 0, 0, 1],
  "total_score": 3
}

The solution demonstrates a basic understanding of using Marshmallow for data serialization but falls short in several key areas. It doesn't correctly implement the required dataclasses, lacks proper schema definitions, and misses important details like date format specification and decimal field handling. The code also contains syntax errors and doesn't properly handle the nested structure for orders. Overall, while it shows some familiarity with the concepts, significant improvements are needed to meet the requirements.

openai/gpt-4-0125-preview says: (0.0) ()

The provided solution attempts to use the Marshmallow library in Python to create dataclasses and schemas to serialize and deserialize JSON data correctly. However, there are multiple issues and inaccuracies in their approach:

  1. Incorrect dataclass usage (0/2 marks):
  2. The dataclass decorator from the standard Python dataclasses library is used incorrectly. dataclass typically applies to a regular class definition for simple data structures, not for the structures involving nested data as shown. The marshmallow_dataclass library integrates both functionalities, which isn't mentioned or imported.

  3. The Order class within the Person class is incorrectly defined. This syntax is not valid in Python.

  4. Schema classes definition (0/2 marks):

  5. The schema definition for person and orders is attempted but wrongly implemented. The nested schema order_schema is written inside the PersonSchema class but does not follow proper syntax or proper usage of Marshmallow's nested fields.

  6. There is no correct link between the dataclasses (which are incorrectly defined as well) and the schema classes using Marshmallow.

  7. Incorrect date format specifications (0/1 mark):

  8. The date format specification for date_of_birth is not correctly mentioned (it doesn't use the format tag %m/%d/%Y).

  9. Handling of the decimal type (0/1 mark):

  10. The amount field is inappropriately handled as a direct type decimal without properly using fields.Decimal(as_string=True) as per the given requirement.

  11. Lack of @post_load method usage (0/2 marks):

  12. There's no @post_load method in any schema. This method is crucial for creating a link where the loaded data can be directly transformed into an instance of the defined dataclass.

  13. Miscellaneous other issues (0/2 marks):

  14. Incorrect imports (decimal instead of Decimal from the decimal module). Misalignment of class definitions and improper handling of nested data.
  15. Code syntax errors such as missing imports for fields from marshmallow and the incorrect and unexplained use of load and dump methods directly on dataclasses.
  16. Inconsistent and malformed methods of processing and passing data between classes and schemas, with unexplained and misused parts of the code and logic.

Overall, the submitted solution has severe fundamental flaws in understanding and applying the functionalities of Python and the Marshmallow library specifically suited to the problem requirements.

{
  "score": 0
}

You can achieve this using the marshmallow library by defining custom marshmallow dataclasses for 'Customer' and 'Order' like so:

from marshmallow import Schema, fields
from datetime import datetime

class CustomerSchema(Schema):
first_name = fields.Str()
last_name = fields.Str()
date_of_birth = fields.DateTime()

class OrderSchema(Schema):
created = fields.DateTime(converter=lambda x: datetime.datetime.strptime(x,'%m/%d/%m/%Y:%H:%M:%S')
amount = fields.Decimal()

And then marshalling the JSON data with marshmallow.make method:
import json
data = {'first_name': 'Jane',
,'last_name': 'Smith',
'date_of_birth': '03/17/197',
orders: [{'created': '24/21/05:04:01','amount': 10.0
}
}
customers = marshallow.make(data, pks.CustomerSchema())['customers'][0]
print(customers)
Will give you:
{
"date_of_birth": datetime.datetime(197, 3, 3, 17, 0, 0, 0, 0),
"amount": 10.0
"last_name": "Smith"
"first_name": "Jane"
}
The 'orders' will also be marshalled but without amount as it's inside an array.
So if you need the full object including 'orders' you can do:
data['orders'] = [marshallow.make(item['orders'] for item in data['orders'] if item]

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: (3.0) (0.90¢)

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

  1. The solution defines two schema classes (CustomerSchema and OrderSchema), but it doesn't define dataclasses. (1 mark out of 2)

  2. The solution defines two schema classes correctly. (2 marks)

  3. The solution doesn't specify the correct date format for the date_of_birth field. It's using DateTime instead of Date, and there's no format specified. (0 marks)

  4. The decimal field is not specified with as_string=True. (0 marks)

  5. There are no @post_load methods to link schemas and dataclasses. (0 marks)

  6. Other issues:

  7. The datetime conversion for 'created' field is incorrect (wrong format string).
  8. There are syntax errors in the JSON data and the marshalling code.
  9. The solution doesn't properly handle the nested 'orders' structure.
  10. The solution attempts to use 'marshallow.make' which is not a standard marshmallow method.
    (0 marks)

Breakdown of marks:

{
  "Dataclass definition": 1,
  "Schema definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Other aspects": 0
}

Score:

{
  "scores": [1, 2, 0, 0, 0, 0],
  "total_score": 3
}

This solution demonstrates a basic understanding of marshmallow schemas but falls short in several key areas. It doesn't use dataclasses as requested, mishandles date and decimal fields, and contains several syntax errors. The approach to marshalling the data is also incorrect. The solution would need significant revisions to meet the requirements of the problem.

openai/gpt-4-0125-preview says: (1.0) ()

The provided solution to convert a JSON string into Python dataclasses using the Marshmallow library shows an attempt to address the problem, but it contains multiple critical misunderstandings and errors in its execution. Here's an evaluation based on the marking scheme:

  1. Two dataclasses defined for the person and the order: The solution fails to define any Python dataclasses; instead, it only defines Schema classes from the Marshmallow library. (0/2 marks)

  2. Two schema classes, one for the person and one for the order: Although the solution does attempt to define two schema classes (CustomerSchema and OrderSchema), they are not implemented correctly with the right field types and converters. (1/2 marks)

  3. Date format specification for date_of_birth (format="%m/%d/%Y"): The date_of_birth field is incorrectly declared as a generic DateTime without specifying the correct format. (0/1 marks)

  4. Decimal field with as_string=True: The amount field in OrderSchema is mentioned as a Decimal, but the as_string=True parameter is not used, which was required to fulfill the marking requirement. (0/1 marks)

  5. Link schema and dataclasses with a @post_load method: No @post_load methods are defined for linking schema validations to the construction of dataclass instances, nor are dataclasses even defined to utilize such a method. (0/2 marks)

  6. Additional considerations:

  7. There's a misunderstanding in the usage of field converters in OrderSchema.
  8. Incorrectly assumes existence of a marshmallow.make() method which does not exist in the Marshmallow API.
  9. Typing and syntax errors throughout the sample code.
  10. Misinterpretation of how to handle nested JSON objects, especially orders array.
  11. Printing results and manipulations are theoretically incorrect and practically infeasible with given code.
    (0/2 marks)

Overall, the solution shows a fundamental misunderstanding of both Python's dataclasses and the correct usage of the Marshmallow library, including managing nested structures, formatting dates and times correctly, and serializing decimals.

Final Score:

{
  "score": 1
}

To generate dataclasses from the given JSON data using Marshmallow library in Python, you can use the load method of the Schema class. Here's an example code snippet:

from marshmallow import Schema, fields, post_load



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



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



person = PersonSchema().load(json_data={
    'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/17',
  orders: [
    {
      'created': '24-25T15:04'
      'amount': '10.0'
    }
})

print(person)

This will output the following dataclass:

Person(
  first_name='Jane'
  last_name='Smith'
  date_of_birth=datetime.date(200219, 3, 17, 0)
  orders=[
    Order(
      created=datetime.datetime(202020, 4, 2, 15, 15, 0, 4, 0)
      amount=Decimal('10.0')
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) (0.88¢)

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

  1. The solution defines only one dataclass implicitly (Person), but not explicitly. The Order dataclass is missing. (1/2 marks)

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

  3. The solution does not specify the date format for the date_of_birth field. (0/1 mark)

  4. The decimal field does not have as_string=True. (0/1 mark)

  5. The solution does not include @post_load methods to link schemas and dataclasses. (0/2 marks)

  6. Additional observations (1/2 marks):

  7. The solution attempts to use Marshmallow for deserialization.
  8. There are syntax errors in the JSON data and schema definitions.
  9. The output doesn't match the input data, suggesting issues with parsing.

Here's the breakdown of marks:

{
  "Dataclass definition": 1,
  "Schema definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "@post_load method": 0,
  "Additional observations": 1
}

Final score:

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

The solution demonstrates a basic understanding of using Marshmallow for data deserialization but falls short in several key areas. It doesn't properly define dataclasses, lacks essential configuration for date and decimal fields, and doesn't use @post_load decorators to create instances of dataclasses. There are also syntax errors that would prevent the code from running correctly. While it shows an attempt to use Marshmallow, significant improvements are needed to meet the requirements fully.

openai/gpt-4-0125-preview says: (1.0) ()

The provided solution attempts to use the Marshmallow library to handle JSON data, converting it into Python dataclasses, but there are several fundamental issues and errors in the provided code:

  1. Dataclasses Definition (0/2 Marks):

  2. The solution does not define any dataclass for Person or Order. This is a critical omission because the question specifically asked for dataclasses to be defined and utilized.

  3. Schema Classes Definition (1/2 Marks):

  4. Both PersonSchema and OrderSchema are indeed defined. However, there is a mistake in how the orders field is included in PersonSchema. The correct syntax should use many=True to indicate a list of nested objects.

  5. Date Format Specification (0/1 Mark):

  6. The date_of_birth field is declared but does not specify the required format (%m/%d/%Y) as requested by the problem statement.

  7. Decimal Field as String (0/1 Mark):

  8. The amount field in OrderSchema is declared as a decimal but does not use as_string=True. Without this, the decimal won't be processed as a string, which may lead to unintended data handling behavior.

  9. Linking Schema with Dataclasses (@post_load Method) (0/2 Marks):

  10. No @post_load methods are provided in the schemas. These methods are essential to map the loaded data onto dataclasses, which is a key requirement per the problem statement.

  11. Additional Considerations (0/2 Marks):

  12. The json_data dictionary in the test case is using incorrect and unmatched data formats and values that don't align with the schema definitions, indicating a lack of thorough testing or example matching.
  13. The syntax errors like missing import statements for data classes, missing commas in the dictionaries, and misuse of json syntax (not using quotes around keys) are significant.
  14. There is no actual declaration or import of any dataclass, indicating a fundamental misunderstanding or oversight regarding the use of dataclasses vs. regular Python classes.

Overall, the provided solution fails to adequately address the primary needs of the task, including critical features like correct data formatting, appropriate use of dataclasses, and necessary schema configurations for deserialization.

Final Score:

{
  "score": 1
}
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