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 (3.0)

You can achieve this by creating custom classes for the fields in Schema, as well as defining the serialization and deserialization behavior. Here's the code for your question:

from marshmallow_dataclass import class_serializer
from datetime import datetime
from marshmallow import fields, decimals

@class_serializer
class CustomDateTimeField(fields.Field):
    def serialize(self, value, attr, obj):
        return value

    def deserialize(self, value, attr, data):
        return value
    deserializer_pre_validators = [
        fields.deserialize(datetime.fromtimestamp, "<isoformat>")]

@class_serializer
class Name(fields.Field):
    def mangle(self, name):
        return name.lower().capitalize()

    def unmarshal(self, name, obj, value):
        return obj.bmi(value)

class Node:
    def __init__(self, first_name=None, last_name=None, date_of_birth=None, orders=None):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

    def bmi(self, data):
        result = {'first_name': 'last_name', 'date_of_birth': 'date_of_birth'}
        return result

class CustomRootSchema(ma.ModelSchema):
    name = Name()
    orders = fields.Nested(NodeOrder, many=True)
    fees = fields.Decimal(core.DECIMAL)

    class Meta:
        model = Node
        strict = True

class NodeOrderSchema:
    created = CustomDateTimeField()
    amount = fields.Decimal()

In this code, we define a custom DateTimeField class that uses the datetime.fromtimestamp() function to convert a timestamp to an ISO-formatted string. We also implement the deserializer_pre_validators to ensure that the deserialization process doesn't accept non-timestamp values.

The Name field is defined with mangle and unmarshal methods to capitalize the first letter of the name and lowercase the rest of the name.

The Node class contains the init method that initializes the fields passed as arguments, and the __getstate__ and __setstate__ methods check their equality for pickling support.

We define two schemas: CustomRootSchema for serialization and deserialization of the Node object, and NodeOrderSchema for serialization and deserialization of the NodeOrder field within the orders field of the Node class.

Make sure to import the necessary modules and define the appropriate variables before using this code.

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) (0.99¢)

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

The solution defines only one dataclass (Node) instead of two. It's missing a dataclass for the order. (1/2 marks)
The solution defines two schema classes (CustomRootSchema and NodeOrderSchema), but NodeOrderSchema is not properly defined as a Schema class. (1/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 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 the schema and dataclasses. (0/2 marks)
Additional points:
The solution attempts to create custom field classes (CustomDateTimeField, Name).
It includes a custom Node class, although it's not a proper dataclass.
The solution attempts to handle serialization and deserialization, although the implementation is incomplete.
(1/2 marks)

Comments:
The solution shows an understanding of some concepts related to marshmallow and dataclasses, but it falls short in several areas. It doesn't properly implement the required dataclasses, misses important field configurations, and lacks the necessary @post_load methods. The custom field implementations are incomplete and don't address the specific requirements for date, datetime, and decimal fields as specified in the problem.

Here's the breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 1,
  "Date format": 0,
  "Decimal field": 0,
  "post_load methods": 0,
  "Additional points": 1
}

Final score:

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

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

The provided solution has several issues particularly relating to how it utilizes Marshmallow and dataclasses to solve the stated problem. Here's the evaluation based on the criteria:

Definition of two dataclasses, one for the person and one for the order (2 marks):
The code does not define any dataclasses as required by Python standards. Instead, it uses plain Python classes (Node and the NodeOrder which is referenced but not defined). Therefore, this fails to meet the specified requirement as it doesn't use the dataclass decorator at all.
Score: 0 points
Definition of two schema classes, one for the person and one for the order (2 marks):
The code attempts to define schema classes, but not in the standard way expected when using Marshmallow with dataclasses. The CustomRootSchema is hinted at but it mixes up concepts not coherent with typical Marshmallow usage. NodeOrderSchema is mentioned but not properly defined or shown in the code.
Score: 0.5 points
Specification of date format for date_of_birth field (format="%m/%d/%Y") (1 mark):
The code does not specify the date format for the date_of_birth field as required. It completely misses this aspect.
Score: 0 points
Decimal field with as_string=True (1 mark):
The code references a decimal field in the schema but does not set as_string=True to true properly to ensure amount fields are serialized as strings.
Score: 0 points
Schema and dataclasses linked with a @post_load method on the schema classes (2 marks):
There is no @post_load method used in any of the schemas to link the schema to any dataclasses, as there are no actual dataclasses defined.
Score: 0 points
Anything else (2 marks):
The overall structure and use of Marshmallow and dataclasses are incorrect and deviate significantly from best practices or correct usage patterns.
Some of the implementations like CustomDateTimeField seem to be ad hoc and do not align effectively with how data should typically be processed in Marshmallow.
The approach also has unused or undefined references such as NodeOrder.
Score: 0.5 points

Considering the above points, the coding solution clearly does not fulfill the requirements of the problem.

Final Score JSON object:

{
  "score": 1
}

Response 2 (2.0)

To create a datetime serializer in Python using the Marshmallow library, you have to define a field for created, which extends the DateTime base class of marshmallow. Here's an example code snippet that demonstrates how to create a DatetimeField class:

from marshmallow import fields
import datetime

# Define the DatetimeField class
class DatetimeField(fields.Datetime):
    def to_python(self, value):
        pointer = None
        string_date = value
        if 'date' in locals():
            pointer, value = datetime.datetime.strptime(value, loc.date)
        else:
            string_date = value[:19] if isinstance(value, str) else value
            pointer, value = datetime.datetime.strptime(value, '%d-%b-%y %H:%M')
        return value, pointer

    def to_dating(self, value):
        return datetime.date.today().strftime(self.__missing__(value, 'Date format not supported. Use a DateTime serializer instead').datestrftime(self.dateformat), value) 

# Add the class to marshmallow
fields.Datetime.register_converter(DatetimeField)

You can then use the DatetimeField class to define your serializer:

from marshmallow import ValidationError, Schema
from marshmallow.fields import DatetimeField, Float, String
import datetime

class ConvertDisableJsonClass(Schema):
    first_name = String()
    last_name = String()
    date_of_birth = DateTime()
    orders = FieldList(Orders, field_name='order')

class Orders(Schema):
    created = DatetimeField('created')
    amount = Float()

class OrdersArray(Schema):
    orders = fields.List(Orders())

In your case, you need to create a date serializer and a datetime serializer. The Orders class represents a specific order and has fields created (DatetimeField), and amount (Float). The OrdersArray class represents an array of orders and has a list field orders (List(Orders())). The ConvertDisableJsonClass class represents the main object and has fields first_name (String), last_name (String), date_of_birth (DateTime), and orders (List(Orders())).

To use these classes, you can create an instance of the ConvertDisableJsonClass and load the JSON into it:

turning_gates = ConvertDisableJsonClass()
turning_gates.load(data)

Make sure to replace data with the JSON input you want to convert to the ConvertDisableJsonClass 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: (2.0) (1.09¢)

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

Defining two dataclasses: The solution doesn't define any dataclasses. (0/2 marks)
Defining two schema classes: The solution defines three schema classes: ConvertDisableJsonClass, Orders, and OrdersArray. While this is more than required, it doesn't exactly match the requirement. (1/2 marks)
Specifying date format for date_of_birth: The solution doesn't specify the required format for the date_of_birth field. (0/1 mark)
Decimal field with as_string=True: The solution uses Float() for the amount field instead of a Decimal field with as_string=True. (0/1 mark)
Linking schema and dataclasses with @post_load: The solution doesn't implement @post_load methods. (0/2 marks)
Additional points:
The solution attempts to create a custom DatetimeField, which shows an understanding of custom field creation.
The solution includes error handling for datetime parsing.
The solution demonstrates knowledge of Marshmallow's field types and schema structure.
(1/2 marks)

Breakdown of marks:

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

Total score:

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

Comments:
The solution shows some understanding of Marshmallow, but it doesn't fully address the requirements. It lacks dataclass definitions, doesn't use the correct field types for dates and decimals, and doesn't implement the @post_load method to link schemas with dataclasses. The custom DatetimeField implementation is interesting but unnecessary for this task and contains some errors. The solution would benefit from focusing more closely on the specific requirements of the problem.

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

The provided solution contains various errors and missed requirements based on the initial task description and marking scheme:

Defining Dataclasses (0/2 Points):
The solution does not define any dataclasses. Instead, it incorrectly uses Schema classes for handling and represents the structure of the data. dataclasses were specifically requested for modeling the entities.
Defining Schema Classes (0/2 Points):
While the solution does define Schema classes (ConvertDisableJsonClass, Orders, OrdersArray), they don't conform to proper marshmallow usage nor the task requirements. The classes lack proper field definitions for handling the types (date, datetime, decimal) specified in the problem.
Specifying Date Format (0/1 Points):
The date_of_birth field should specifically use the date format "%m/%d/%Y". The solution uses a DateTime() field and doesn't specify any format, thus failing to meet this requirement.
Decimal Field with as_string=True (0/1 Points):
The amount field is inaccurately represented using the Float() type in Marshmallow. The task specifically necessitates using decimal type and representing it as a string in JSON, which requires using fields.Decimal(as_string=True). This is not addressed in the given solution.
Using @post_load to Link Schema and Dataclasses (0/2 Points):
The @post_load method in Marshmallow is typically used to transform loaded data into an instance of a dataclass or other complex types. This vital transformation and method linking are omitted in the solution provided.
Additional Points (0/2 Points):
Additional aspects such as correct use of custom field definition for handling datetime properly with appropriate formats, integration of schemas, and proper implementation of lists in schemas are handled incorrectly or not at all.

The given solution is deviated significantly from the requirements in defining data structures properly with Marshmallow and dataclasses in python, representing a misunderstanding and mishandling of library features and task demands.

Final Score Assessment:

{
  "score": 0
}

Response 3 (5.0)

In Python, you can use the Marshmallow library to convert JSON to specific types or dataclasses. First, you need to install Marshmallow in your project. You can do this using pip:

pip install marshmallow

Next, you can define your dataclasses using the @dataclass decorator from the dataclasses library. Here's the code to define the dataclasses:

from dataclasses import dataclass
from marshmallow import fields, post_load

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date

    @post_load
    def date_of_birth(self, data, **kwargs):
        """
        Convert json string to date object
        """
        d = data['date_of_birth']
        return date(*d.split('/')[-1]), d[:-1].split('/')

@dataclass
class Order:
    created: datetime
    amount: Decimal


class OrderSchema(ma.Schema):
    created = fields.Function(lambda order: order.created)
    # put Order in ma.fields.Nested of Person

Now, when you have a list of orders that you want to convert to Order instances, you can use the marshmallow.loads function and the OrderSchema class to convert it:

import json
from marshmallow import loads

# your JSON here
json_string = '{"orders": [{"created": 111,' + \
                '"amount": 222}]}'  # your JSON here

# load your JSON as a dict()
json_Dict = json.loads(json_string)

# get your schema
schema = OrderSchema()

# use your schema to convert the json to a marshmallow object
marsh_obgj = schema.load(json_Dict)

# now you can get the raw data
todict = marsh_obgj.data

# and from under 'data'
json_dict_out = dec_schema.load(todict)

# get the Order list
orders = dec_schema.load(todict)

In the above example, we first load the JSON string into a dictionary. Then, we create an instance of the OrderSchema class, which represents the schema for the Order dataclass. By calling schema.load(json_Dict), we execute the deserialization process. Finally, we can access the deserialized object by calling marsh_obgj.data.

You can refer to the Marshmallow documentation for more information on using Marshmallow with 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: (5.0) (0.93¢)

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

Defining two dataclasses (Person and Order):
The solution defines both dataclasses correctly.
Defining two schema classes:
The solution only defines one schema class (OrderSchema). It's missing a schema for the Person class.
Specifying the date format for the date_of_birth field:
The solution doesn't specify the date format as required (format="%m/%d/%Y").
Decimal field with as_string=True:
The solution doesn't include this requirement for the amount field.
Linking schema and dataclasses with @post_load:
The solution includes a @post_load decorator, but it's incorrectly placed on the date_of_birth method of the Person class instead of being on the schema class.
Additional points:
The solution attempts to handle date conversion, although incorrectly.
It includes some code for JSON parsing and schema usage, which is helpful.
However, it doesn't fully address the requirements for datetime and Decimal fields.

Here's the breakdown of marks:

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

Final score:

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

The solution partially addresses the problem but misses several key requirements and contains some errors. It would benefit from more attention to the specific requirements, especially regarding field types and schema definitions.

openai/gpt-4-0125-preview says: (1.5) ()

The provided solution has multiple elements that need review based on the given problem and marshmallow usage. Let's analyze it according to the marking scheme:

Definition of dataclasses (2 marks):
The solution defines two data classes, Person and Order, which corresponds to the expected data from the JSON. However, incorrect or missing imports (e.g., date, datetime, Decimal) are notable, and no attempt is made to import these necessary types from the appropriate modules.
Given these issues, I would assign 0.5 marks because the classes are mentioned but not usable in their current form.
Definition of schema classes (2 marks):
The solution defines a schema class, OrderSchema, but does not define a corresponding schema for the Person class, which is a significant omission as it's required to fully implement the deserialization for the JSON structure provided.
Given this, 0.5 marks seem appropriate due to partial implementation.
Date format specification for date_of_birth (1 mark):
No date formats are specified in the post_load method or elsewhere in the schema; hence the specific requirement to format "date_of_birth" as %m/%d/%Y is not met.
This section receives 0 marks.
Decimal field with as_string=True (1 mark):
The amount field should be handled by a fields.Decimal with as_string=True; this specification is entirely missing in the solution.
This section deserves 0 marks.
Linking schemas and dataclasses with @post_load (2 marks):
The use of the @post_load decorator is demonstrated poorly, showing misunderstanding (it’s used inside the Person dataclass apparently to modify the date_of_birth instance variable post-load, which is incorrect usage).
Considering the misuse and misplacement of @post_load, 0 marks should be allocated here.
Anything else (2 marks):
The example provided for deserialization is largely confusing and inaccurate, particularly in how JSON manipulation and loading are handled. The structure and logic presented in the serialization and deserialization process are problematic and demonstrate a misunderstanding of Marshmallow's practices.
Given the extensive issues, 0.5 marks could be considered, particularly because there was an attempt to provide an example, albeit incorrect.

Total Score:

Given the omissions, errors, and misinterpretations in handling basic marshmallow operations and Python coding practices, the total score is:

{
  "score": 1.5
}

This score reflects the fundamental misunderstandings and omissions in the provided solution in relation to the specific requirements of the problem.

1.00	0.69	0.61
0.69	1.00	0.73
0.61	0.73	1.00

Most Similar

Model Consistency

Model Evaluations

claude-3-haiku
claude-3.5-sonnet	3.0	2.0	5.0
gpt-3.5-turbo-0125
gpt-4-0125-preview	1.0	0.0	1.5
gpt-4-1106-preview