MythoMist 7B

Description

From the creator of MythoMax, merges a suite of models to reduce word anticipation, ministrations, and other undesirable words in ChatGPT roleplaying data.

It combines Neural Chat 7B, Airoboros 7b, Toppy M 7B, Zepher 7b beta, Nous Capybara 34B, OpenHeremes 2.5, and many others.

#merge

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)

In order to implement the desired functionality using the marshmallow library, you can create separate entity classes for 'Person' and the 'Order' information. For 'Person', we want to include their first and last names, as well as the date of birth - which can be represented as Python datetime object. Similarly, the 'Order' class should have the properties 'amount' which as a decimal and 'created', a datetime to represent the datetime part of the created attribute.

Here's how your classes can look like:

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


class Customer(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)

    @post_load
    def create_datetime_from_str(self, data):
        data['date_of_birth'] = \
            parser.parse(data['date_of_birth'])
        return data


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

    @post_load
    def create_datetime_from_str(self, data):
        data['created'] = datetime.strptime(data['created'],
                                             '%Y-%m-%dT%H:%M:%S.%f')
        return data

Now, you can instantiate the json data using the Person and Order class provided. You can handle creating, updating or querying Person and Order records using these classes for efficient data representation and handling. Adding more fields or features to these classes can be based on the needs of your application. This way, the flexibility provided by Python classes is combined with easy integration of the existing JSON data, allowing you to stay organized and simplify data exchange within the application.

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) (0.94¢)

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

The solution defines two classes (Customer and Order), but they are not dataclasses. They are Schema classes from marshmallow. (0/2 marks)
The solution correctly defines two schema classes, Customer and Order. (2/2 marks)
The solution does not specify the date format for the date_of_birth field as required (format="%m/%d/%Y"). (0/1 mark)
The decimal field does not include as_string=True. (0/1 mark)
The solution includes @post_load methods for both schema classes, attempting to convert string dates to datetime objects. (2/2 marks)
Additional observations:
The solution uses dateutil.parser for date parsing, which is not mentioned in the requirements.
The 'date_of_birth' field is missing from the Customer schema.
The solution doesn't create actual dataclasses, only marshmallow schemas.
(1/2 marks)

Comments:
The solution partially addresses the requirements but misses some key points. It uses marshmallow Schema classes instead of dataclasses, which is not what was asked for. The date and decimal formatting specifications are missing, and the 'date_of_birth' field is absent from the Customer schema. The @post_load methods are present, but they're not linking schema and dataclasses as required.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Post_load methods": 2,
  "Additional observations": 1
}

Final score:

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

openai/gpt-4-0125-preview says: (2.5) ()

The review is based on the previously established marking criteria:

Definition of two dataclasses (Person and Order): The solution provided fails to define actual dataclasses; instead, it defines two Schema classes from marshmallow (Customer and Order). This misunderstanding leads to a misimplementation as it uses schema for data validation and transformation rather than defining separate classes for the data structures themselves. (0/2)
Definition of two schema classes (PersonSchema and OrderSchema): Although the solution does define two marshmallow schemas, they are incorrectly noted as Customer and Order which should rather have been modeled as PersonSchema and OrderSchema, following the typical pattern for handling entity serialization in marshmallow. The naming here could lead to confusion since it might suggest that they are the main data containers. (1/2)
Specification of the date format for the date_of_birth field: The solution does not explicitly specify the date format for date_of_birth. It uses the dateutil.parser.parse to parse the date, which infers the format but does not enforce a specific one like the requested %m/%d/%Y format. (0/1)
Decimal field with as_string=True requirement: The solution does not specify the as_string=True property for the amount field in the Order schema. This property is crucial when ensuring that decimals are handled as strings to prevent precision errors during serialization. (0/1)
Linking schema and dataclasses using @post_load within each schema: The solution does include the @post_load decorator to handle conversion post-loading data into Python objects. However, the absence of any defined Python dataclasses means these methods essentially redefine what usually would be attributes in a dataclass, stripping away the benefits of clear, type-assured data structures and resulting in a weaker design. (0.5/2)
Additional considerations: The use of dateutil.parser.parse and the detailed handling of datetime parsing in Order are technically correct practices by themselves in marshmallow to handle complex conversions. Additionally, the textual explanation suggests an understanding of the use-case and integration of JSON data and class-based handling in Python applications. However, these positives are overshadowed by the fundamental mismatches and omissions relative to the task requirements. (1/2)

Overall, essential aspects of the exercise, particularly regarding the accurate use and implementation of the dataclasses and adherence to specific format requirements, are missing or incorrect.

{
  "score": 2.5
}

Response 2 (3.0)

To accomplish the desired approach, I will first import the required libraries into your Python environment. You can do this by including the following import statements:

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

Next, create a separate Schema for each data block from the JSON. Let's call them 'Person', 'Order', 'PersonSchema', and 'OrderSchema', reflecting the given name of the key and the datatype required for each.

class PersonSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date()

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

Now you've created the dataclasses and appropriately marked their corresponding datatypes. To be used as datacles, you'll need to create instances of these classes. To this end, the JSON data can be loaded and given the appropriate instance directly.

It's essential to note that OrderSchema inherits PersonSchema and also includes the 'created' and 'amount' attributes.

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

person = PersonSchema().load(json_data)
orders = [OrderSchema().load(order) for order in json_data.get('orders')]

print(person.first_name, person.last_name, person.date_of_birth)
for order in orders:
    print(order.created, order.amount)

The output should be something like this: Jane Smith 03/16/1977 2024-01-25T15:00:04 100.0 2024-01-25T15:00:04 100.0

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: (3.0) (0.97¢)

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

Defining two dataclasses:
The solution doesn't explicitly define dataclasses. It only defines schema classes.
Marks: 0/2
Defining two schema classes:
The solution correctly defines PersonSchema and OrderSchema.
Marks: 2/2
Specifying date format for date_of_birth:
The solution doesn't specify the date format for the date_of_birth field.
Marks: 0/1
Decimal field with as_string=True:
The solution doesn't include as_string=True for the decimal field.
Marks: 0/1
Linking schema and dataclasses with @post_load:
The solution doesn't use @post_load to link schema and dataclasses.
Marks: 0/2
Anything else:
The solution imports necessary libraries and provides a basic implementation of schema classes. It also includes code to load and print data, which is helpful.
Marks: 1/2

Comments:
The solution provides a basic implementation using marshmallow schemas but falls short in several key areas. It doesn't use dataclasses, doesn't specify the required date format, doesn't use as_string=True for the decimal field, and doesn't link schemas to dataclasses using @post_load. However, it does provide a working structure for defining schemas and loading data, which is a good starting point.

Marks breakdown:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "post_load": 0,
  "Anything else": 1
}

Score:

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

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

The provided solution has not correctly implemented several components of the task. Here is the evaluation:

Dataclasses Definition (0/2 Marks): No dataclasses were defined. The solution only uses Schema classes but does not create actual dataclass declarations for Person or Order.
Schema Classes Definition (2/2 Marks): Correctly defined two schema classes PersonSchema and OrderSchema that will map the JSON data.
Date Format Specification (0/1 Mark): The solution does not specify the format for the date_of_birth field. The correct implementation should have included date_of_birth = fields.Date(format="%m/%d/%Y") to process the date string correctly from the given format.
Decimal Field Specification (0/1 Mark): The solution does not use as_string=True for the Decimal field. This is needed to ensure that the field is treated as a string for precision purposes.
Link Schema and Dataclasses with @post_load (0/2 Marks): The solution does not use @post_load to instantiate dataclasses once the data is loaded by the Schema. This is essential for converting loaded data into instances of the defined dataclasses.
Additional Considerations (0/2 Marks):
Error in assuming OrderSchema should inherit from PersonSchema; these should be separate as they represent different entities.
Proper error handling, data validation, or additional utility from marshmallow (like custom field formatting or validation) has not been provided or incorrectly implemented.

Overall Comment:
The solution has fundamental gaps in both understanding and implementation of the required tasks using marshmallow and dataclasses. There is a misuse of inheritance between OrderSchema and PersonSchema, and the necessary components like @post_load for creating instances of a dataclass and specifying field formats are completely missing.

Score:

{
  "score": 2
}

Response 3 (4.0)

To accomplish this using the marshmallow library in Python, we first need to create a DataModel class with necessary fields for the given JSON. Here is the DataModel for your provided JSON:

from marshmallow import Schema, pre_dump, fields
from datetime import date, datetime

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()

class OrderSchema(Schema):
    created = fields.Raw()  # SQLAlchemy/ doctrine likes timestamps in raw format
    amount = fields.Decimal('amount')

class MetaDataSchema(Schema):
    person = fields.Nested(PersonSchema)
    orders = fields.List(OrderSchema, allow_none=True)

    # This is to preprocess the dated_of_birth to a proper datetime before dumping into the resulting JSON
    @pre_dump
    def date_init_to_datetime(data):
        data['person']['date_of_birth_as_datetime'] = datetime.strptime(data['person']['date_of_birth'], '%m/%d/%Y')
        del data['person']['date_of_birth']  # Remove 'date_of_birth' from the final JSON
        return data

Now we can create an instance:

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

meta_data = MetaDataSchema()
meta_data.load(json_data)

# To grab details of a person instance, we can use person attribute of MetaDataSchema instance
person_details = meta_data.person
print(person_details)

# To access specific attributes of a person, such as first_name or last_name
print(person_details['first_name'])
print(person_details['last_name'])

# To access a list of orders, use orders attribute of MetaDataSchema instance
person_orders = meta_data.orders
print(person_orders) 

# To access attributes of a single OrderSchema, use first() on orders, as OrderSchema is list like
first_order_details = person_orders[0]
print(first_order_details['created'])
print(first_order_details['amount'])

This will result in output that resembles your original JSON structure with added date and datetime fields while organized in Python dataclasses using the marshmallow library.

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) (1.06¢)

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 three schema classes: PersonSchema, OrderSchema, and MetaDataSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. It uses a pre_dump method to convert the date, but this is not the correct approach. (0/1 mark)
The decimal field is defined, but it doesn't use as_string=True. (0/1 mark)
The solution does not use @post_load methods to link schemas and dataclasses. (0/2 marks)
Additional points:
The solution uses a pre_dump method to handle date conversion, which is an attempt to solve the date formatting issue, albeit incorrectly. (1/2 marks)
The solution includes code to create an instance and access data, which is helpful. (1/2 marks)

Here's the breakdown of marks:

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

Final score:

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

Comments:
The solution demonstrates a basic understanding of marshmallow schemas but misses several key requirements. It doesn't use dataclasses, which was a core part of the task. The date and decimal formatting are not handled correctly according to the specifications. The solution also doesn't use @post_load methods to create dataclass instances from the schemas. While the additional code for creating and accessing instances is helpful, it doesn't compensate for the missing core elements. The solution would benefit from a closer adherence to the specified requirements, particularly in using dataclasses and correctly formatting the date and decimal fields.

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

The provided solution attempts to use the marshmallow library to create schemas for manipulating JSON data in Python, particularly focusing on converting and managing specific formats. However, the solution provided has several issues that deviate from the problem's requirements and the expected solution structure:

Dataclasses Definition (2 marks): The solution did not define any Python dataclasses (from dataclasses import dataclass) for Person and Order, which were expected as per the problem's requirements. The solution instead only provides Schema classes.
Score: 0
Schema Classes Definition (2 marks): While the solution does define schema classes, it inaccurately only represents one combined MetaDataSchema and individual PersonSchema and OrderSchema without proper integration using nested structure as implied in the task.
Score: 1
Date Format Specification (1 mark): The solution specifies the date format in the @pre_dump hook method instead of directly in the fields.Date(format="%m/%d/%Y"). This method is incorrect for parsing, which should typically occur in field declaration or within a @pre_load or @post_load method.
Score: 0
Decimal Field Handling (1 mark): The amount field is not set with as_string=True. This argument is required to ensure that decimals are handled as strings if required by the problem statement.
Score: 0
@post_load Linkage (2 marks): Instead of a @post_load method to construct the dataclass instances from the loaded data, the provided solution defines a @pre_dump method, which is used incorrectly for a task that seems to need loading and parsing rather than pre-dumping modifications.
Score: 0
Additional Criteria (2 marks): The solution misunderstands the use of marshmallow in areas like the usage of fields.Raw() which doesn't handle dates or datetime effectively. Also, the handling of schema instantiation and data interaction is mismatched into non-practical implementations, as individual schema components do not interact in the expected nested manner.
Score: 0.5 for the effort to integrate multiple schemas despite doing it improperly.

Total Score:
The solution comprehensively misses aligning with the problem requirements and proper implementation of the marshmallow library functionalities, leading to significant issues in both theoretical understanding and practical implementation.

{
  "score": 1.5
}

1.00	0.83	0.83
0.83	1.00	0.85
0.83	0.85	1.00

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