Fimbulvetr 11B v2

Description

Creative writing model, routed with permission. It's fast, it keeps the conversation going, and it stays in character.

If you submit a raw prompt, you can use Alpaca or Vicuna formats.

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)

To achieve this using the Marshmallow library in Python, you can create a schema that defines the dataclasses based on the given JSON. Let's start by importing the necessary libraries and defining the required classes.

from marshmallow import Schema, fields, DateTime, Date, Decimal, post_load, validates
from datetime import datetime, date

class DateTimeField(fields.Field):
    def _deserialize(self, value, attr, data):
        # Convert '2024-01-25T15:00:04' to datetime.datetime
        time = datetime.strptime(value, '%Y-%m-%d%H:%M:%S')
        return time

class DateField(fields.Field):
    def _deserialize(self, value, attr, data):
        # Convert '03/16/1977' to datetime.date
        return date(int(value.split('/')[-1]), int(value.split('/')[-2]), int(value.split('/')[-3]))

class DecimalField(Decimal):
    pass

class OrderSchema(Schema):
    created = DateTimeField(missing=datetime.min)
    amount = DecimalField(missing=Decimal("0.0"))

class CustomerSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = DateField(missing=date.min)
    orders = fields.Nested(OrderSchema)

    @post_load
    def make_customer(self, data):
        # Convert OrderSchema's datetime and Decimal attributes to their types
        data['orders'] = [{'created': data['orders'][0]['created'].isoformat(), 'amount': float(data['orders'][0]['amount'])}
        return data

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

schema = CustomerSchema()
parsed_json = schema.load(my_json)

print(parsed_json)

In the above code, we create DateTimeField and DateField subclasses by extending fields.Field, as strptime functions from datetime module are used to deserialize the created and date_of_birth fields. The DecimalField, on the other hand, is simply a class definition subclassing Decimal with no additional logic.

We create OrderSchema and CustomerSchema for the nested orders and the outer customer structure respectively. For CustomerSchema, we also define the make_customer method at @post_load to convert the created and amount attributes from the OrderSchema to datetime.datetime and float, respectively. This is because Marshmallow doesn't perform this conversion out of the box.

By loading the JSON with the created CustomerSchema, you will get an object with the desired dataclass structure. The output will be a dictionary, but the created and date_of_birth will be datetime.datetime and datetime.date correspondingly, and amount will be float, which is the Python equivalent to decimal.Decimal.

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) (1.02¢)

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

The solution does not define dataclasses. It only defines schema classes. (0/2 marks)
The solution defines two schema classes: OrderSchema and CustomerSchema. (2/2 marks)
The solution specifies a date format for the date_of_birth field, but it's not using the required format="m/d/Y". It's using a custom DateField instead. (0/1 mark)
The solution doesn't use as_string=True for the decimal field. It uses a custom DecimalField instead. (0/1 mark)
The solution includes a @post_load method on the CustomerSchema class, but it's not correctly linking the schema to dataclasses. (1/2 marks)
Additional points:
The solution provides custom field classes for date and datetime parsing.
It handles nested structures correctly.
It includes a sample usage of the schema.
(2/2 marks)

The solution attempts to solve the problem but misses some key requirements. It doesn't use dataclasses, and it implements custom field classes instead of using Marshmallow's built-in options. The @post_load method is present but doesn't create proper dataclass instances.

Here's the breakdown of marks:

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

Final score:

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

The solution scores 5 out of 10 points.

openai/gpt-4-0125-preview says: (2.0) ()

The provided solution attempts to use the marshmallow library in Python to parse and validate JSON data into an appropriate Python structure with type conversions. However, there are several discrepancies and mistakes regarding the requirements and marshmallow usage:

Dataclasses Definition (1/2 Marks):
- The task specifically requested the use of dataclasses, but the given solution does not actually define any Python dataclasses (from dataclasses import dataclass). Instead, it confuses the handling with regular class definitions and marshmallow schema mappings.
Schema Classes (1/2 Marks):
- Two schema classes (OrderSchema and CustomerSchema) are defined, which is correct. However, these schemas do not adhere to several best practices and requirements stated for using marshmallow effectively (e.g., incorrect usage of fields for type conversions).
Date Format Specification (0/1 Marks):
- The solution does not use the marshmallow.fields.Date field for date_of_birth correctly. It was required to specify the date format directly via marshmallow's built-in functionality (format="%m/%d/%Y"), but it uses a custom field with manual parsing instead.
Decimal Field Specification (0/1 Marks):
- Instead of using marshmallow.fields.Decimal(as_string=True), the solution defines a class DecimalField, which directly inherits from Decimal. This does not fulfil the requirement to handle decimals as strings.
Linkage Of Schemas and Dataclasses (0/2 Marks):
- There is no proper connection between data classes and schema classes using marshmallow's @post_load to produce dataclass instances. The @post_load method in the solution incorrectly tries to adjust the data into a different format rather than creating instances.
Additional Considerations (0/2 Marks):
- The solution lacks error handling, correct use of built-in marshmallow fields, and misuse of marshmallow's capabilities suggesting a fundamental misunderstanding of how to utilize marshmallow with Python dataclasses.
- JSON key 'orders' is erroneously not quoted which can lead to syntax errors in Python.

Overall, the provided solution illustrates an attempt to use custom fields unnecessarily while missing out on the simpler and more efficient built-in capabilities of marshmallow. Furthermore, it deviates from the task's specific requirements regarding data handling and marshmallow usage.

Final score for the given solution:

{
  "score": 2
}

Response 2 (4.0)

To generate dataclasses for the given JSON using the Marshmallow library in Python, you need to create a Schema which will handle parsing your data. Here's a simple example to do that:

First, install the Marshmallow library using pip:

pip install marshmallow

Next, create a new file customer.py and add the following code:

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

class DateField(fields.Field):
    def __init__(self, *args, **kwargs):
        super(DateField, self).__init__(date_parse=True, *args, **kwargs)

class DateTimeField(fields.Field):
    def __init__(self, *args, **kwargs):
        super(DateTimeField, self).__init__(dt_format='%Y-%m-%dT%H:%M:%S', *args, **kwargs)

class DecimalField(fields.Field):
    def _deserialize(self, value, attr, data):
        return Decimal(value)

    def _serialize(self, value, attr, obj):
        return str(value)

class CustomerSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = DateField(required=True)
    orders = fields.List(OrderSchema())

    class Meta:
        extra = 'forbid'

class OrderSchema(Schema):
    created = DateTimeField(required=True)
    amount = DecimalField(required=True)

# Load your json into a dictionary
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00',
        }
    ]
}

# Load the data
customer = CustomerSchema().load(json_data)

# Access the parsed data
print(customer)

# The output will look something like this:
# 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('100.0'))])

This code creates DateField, DateTimeField, and DecimalField subclasses to handle deserialization and serialization of dates, datetime, and decimal values respectively. The CustomerSchema class describes a Custom schema, and the nested OrderSchema class parses the orders list. The Meta.extra.forbid option ensures that attributes not present in CustomerSchema raise a ValidationError.

Put the code in customer.py and run it. It will load your JSON data and deserialize it based on the specified datatypes. You can now access the Customer object, which has attributes as date_of_birth as a date object, created as a datetime.datetime object, and amount as a decimal.Decimal object.

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) (1.08¢)

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

The solution does not define dataclasses. It only defines Schema classes. (0/2 marks)
The solution correctly defines two schema classes: CustomerSchema and OrderSchema. (2/2 marks)
The solution does not specify the correct date format for the date_of_birth field. It uses a custom DateField without specifying the format. (0/1 mark)
The solution does not use as_string=True for the decimal field. Instead, it uses a custom DecimalField. (0/1 mark)
The solution does not use @post_load methods to link schema and dataclasses. (0/2 marks)
Additional points:
The solution provides custom field classes for Date, DateTime, and Decimal, which is a good approach. (1 mark)
The solution includes a Meta class with extra='forbid' to prevent unknown fields. (1 mark)
(2/2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution provides a good start with the Marshmallow library, but it misses several key requirements. It doesn't use dataclasses, which was a core part of the task. The date format for date_of_birth is not correctly specified, and the decimal field doesn't use as_string=True. The solution also doesn't use @post_load methods to create dataclass instances. However, the custom field classes and the use of Meta.extra are good additions that show understanding of more advanced Marshmallow features.

openai/gpt-4-0125-preview says: (2.0) ()

The solution provided attempts to address the conversion of a JSON structure into data structures using the Marshmallow library, handling specific data types like dates, datetimes, and decimals. Let’s evaluate the solution based on the provided marking scheme:

Definition of Two Dataclasses (0/2 Marks):
The solution does not define any Python dataclass for the Person or Order. It only defines Schema classes which serve different purposes.
Definition of Two Schema Classes (2/2 Marks):
The solution provides CustomerSchema for the person and OrderSchema for the orders as required.
Date Format Specification for date_of_birth (0/1 Mark):
The DateField class is implemented but does not specify the format as %m/%d/%Y for date_of_birth as required. It only generically sets a date_parse attribute to True, which isn't standard in Marshmallow.
Decimal Field with as_string=True (0/1 Mark):
There is an attempt to handle decimal values in the DecimalField class. However, the as_string=True attribute specifically, as required for Marshmallow fields, is absent.
Linking Schema and Dataclasses via @post_load Method (0/2 Marks):
There are no @post_road methods implemented linking the schema parsing to the creation of dataclass instances. These methods are crucial to properly initializing dataclass instances with parsed data.
Additional Requirements (0/2 Marks):
The required usage of date_of_birth as a date object, created as a datetime.datetime object, and amount as a decimal.Decimal object is neither accurately implemented nor demonstrated through 'dataclass' integrations. An instance and initialization of these types using dataclass decorated structures are lacking.

Total (2/10 Marks):
- Much of the core requirements, such as using dataclass and proper initializations using @post_load, are not met. The serialization and deserialization customizations are addressed, but the specific requirements are not followed properly.

{
  "score": 2
}

Response 3 (5.0)

To use the Marshmallow library for this task, you'll first need to create a BaseSchema class to define your model attributes. This class will be inherited by your OrderSchema and CustomerSchema classes, which will handle the validation and serialization of Order and Customer objects, respectively.

Firstly, install the Marshmallow library if you haven't already:

pip install marshmallow

from datetime import datetime, date
from marshmallow import (
    SCHEMA_CLASS_REGISTRY,
    DateTime,
    Date,
    Decimal,
    fields,
    Schema,
    ValidationError
)


class BaseSchema(Schema):
    pass


class OrderSchema(BaseSchema):
    created = fields.Date(data_key="created", attribute="created", load_default=datetime.strptime, dump_default=datetime.strftime, date_format="%Y-%m-%dT%H:%M:%S")
    amount = fields.Decimal(default=Decimal, load_default=Decimal, dump_default=lambda obj: str(obj))



class CustomerSchema(BaseSchema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(data_key="date_of_birth", attribute="date_of_birth", load_date_format="%m/%d/%Y")
    orders = fields.Nested(OrderSchema, many=True)


SCHEMA_CLASS_REGISTRY["OrderSchema"] = OrderSchema
SCHEMA_CLASS_REGISTRY["CustomerSchema"] = CustomerSchema

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

customer = Customer(**CustomerSchema().load(data).data)
print(f"First name: {customer.first_name}, Last Name: {customer.last_name}, Date of Birth: {customer.date_of_birth}, Orders: {customer.orders}")

To create these classes, you first define the OrderSchema and CustomerSchema, inheriting from the base BaseSchema. In OrderSchema, you set the created field to utilize Marshmallow's built-in Date type, which automatically converts between string and datetime.datetime objects using strptime and strftime for loading and dumping. You also define amount as a Decimal field, using the Decimal instance as the default value for Python's built-in decimal.Decimal class, and again using a custom loader and dumper to handle the conversion.

In the CustomerSchema, you use the Date type for date_of_birth with a custom date_format. Since orders is a list of Order schema objects, you use the Nested class with many=True to deserialize the incoming orders field.

Finally, you add the schemas to the Marshmallow registry using SCHEMA_CLASS_REGISTRY before loading the data and initializing a Customer object with the deserialized data. You'll need to define your Customer class, which should have the expected attributes:

class Customer:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

Now, you can deserialize your input JSON into a structured representation using the CustomerSchema and construct a Customer class from the loaded data. This allows for validation, conversion of data types, and normalization of input data, based on your schema definitions.

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) (1.09¢)

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

The solution does not define dataclasses for the person and order. It defines a Customer class, but not as a dataclass. (0 marks)
The solution defines two schema classes: OrderSchema and CustomerSchema. (2 marks)
The solution specifies the date format for the date_of_birth field correctly with load_date_format="%m/%d/%Y". (1 mark)
The decimal field (amount) is not explicitly set with as_string=True. Instead, it uses a custom dump_default function. While this achieves a similar result, it doesn't exactly match the requirement. (0 marks)
The solution does not use @post_load decorators to link the schema and dataclasses. (0 marks)
Additional points:
The solution uses a BaseSchema class, which is a good practice.
It correctly handles nested schemas for the orders field.
It registers the schemas in SCHEMA_CLASS_REGISTRY.
(2 marks)

Here's the breakdown of marks:

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

Total score:

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

The solution demonstrates a good understanding of Marshmallow schemas but doesn't fully meet the requirements specified in the marking scheme. It misses the use of dataclasses and the @post_load decorator, and doesn't exactly match the specified decimal field configuration. However, it does provide a working solution for serializing and deserializing the given JSON data.

openai/gpt-4-0125-preview says: (5.5) ()

The provided solution attempts to use the Marshmallow library in Python to convert JSON data into Python data structures using schemas and custom serialization/deserialization methods. Here is the evaluation based on the criteria given:

Dataclasses Definition:
The solution does define a Customer class at the end of the code. However, there is no Order class defined explicitly as a dataclass. The lack of a separate Order class with attributes and types specified detracts from fulfilling this point.
Score: 0.5/2
Schema Classes Definition:
The solution defines two schema classes, OrderSchema and CustomerSchema. These are suited to the task as they attempt to handle the serialization and deserialization of respective entity data.
Score: 2/2
Date Format for date_of_birth:
The CustomerSchema does specify the date_of_birth as a date field with a specific format ("%m/%d/%Y"). This is correctly implemented to match the input format.
Score: 1/1
Decimal Field with as_string=True:
The amount field in the OrderSchema is handled using a Decimal field, but the attribute as_string=True is not explicitly provided. Instead, a lambda is used for string conversion during output, which does not directly satisfy the requirement though seeks the same end.
Score: 0.5/1
@post_load Method to Link Schemas and Dataclasses:
There is no @post_load method implemented in any of the schemas that would construct and return an instance of the respective data class (i.e., linking deserialized data back to a dataclass). This is a significant omission as it directly relates to transforming loaded data into a structured Python object accurately.
Score: 0/2
Additional Points:
Effort was made to handle date and decimal conversions, although these were not perfectly aligned with given specifications.
The explanation provided helps understand the code's purpose, showing the application of schema inheritance and use of the Marshmallow library.
Score: 1.5/2

Overall Evaluation:
- While the approach taken shows some necessary understanding and correct uses of Marshmallow, key elements are missing or incorrectly applied. Notably, the handling of decimal fields, the correct use of date fields, and most importantly, the failure to link schema validation to object instantiation correctly impact functionality.

Final Score:

{
  "score": 5.5
}

1.00	0.92	0.88
0.92	1.00	0.89
0.88	0.89	1.00

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