Mixtral 8x7B Instruct

Description

A pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts (feed-forward networks) for a total of 47 billion parameters.

Instruct model fine-tuned by Mistral. #moe

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 generate dataclasses using the provided JSON schema and the marshmallow library, follow the steps below:

First, install marshmallow if you haven't already; you can do that by running:

pip install marshmallow

Once marshmallow is installed, you can create the necessary dataclasses as follows:

import marshmallow as mm
from datetime import datetime, date
from decimal import Decimal

class OrderSchema(mm.SQLAlchemyAutoSchema):
    class Meta:
        model = None
        load_instance = True

class Order(mm.Schema):
    created = mm.fields.DateTime(dump_default=datetime.utcnow, format="iso")
    amount = mm.fields.Decimal()

class UserSchema(mm.SQLAlchemyAutoSchema):
    class Meta:
        model = None
        load_instance = True

    orders = mm.Nested(OrderSchema, many=True)

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

    orders = mm.fields.Nested(Order, many=True, data_key="orders")

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

user_schema = UserSchema()
user_obj = user_schema.load(user_data)
print(user_obj)

In this example, I created two nested schemas, Order and User, for the given JSON. Each schema converts the appropriate fields to their corresponding data types. Don't forget to update the data for date_of_birth. I changed it to '1977-03-16' to make it a proper date format. Later, you can load the JSON into these dataclasses using the load() method from the respective schemas. This example uses the Python datetime, date, and decimal modules to ensure the correct data types are used.

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) (0.95¢)

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

The solution defines two schema classes (User and Order), but not dataclasses. (1 mark out of 2)
The solution correctly defines two schema classes (UserSchema and OrderSchema). (2 marks)
The solution doesn't specify the date format for the date_of_birth field. (0 marks)
The decimal field is not specified with as_string=True. (0 marks)
The schema and dataclasses are not linked with a @post_load method. (0 marks)
Additional points:
The solution uses SQLAlchemyAutoSchema, which wasn't required. (0 marks)
The solution demonstrates how to load data using the schema. (1 mark)
The solution imports necessary modules. (1 mark)

Here's the breakdown of the marks:

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

The total score is:

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

Comments:
The solution partially addresses the problem but misses several key requirements. It uses schema classes instead of dataclasses, which is not what was asked for. The date format for date_of_birth is not specified, and the decimal field is not correctly configured. The solution doesn't use the @post_load decorator to link schemas and dataclasses. However, it does show how to use the schema to load data, which is useful. The solution could be improved by adhering more closely to the specified requirements and using dataclasses as requested.

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

The provided solution does use the marshmallow library to handle JSON data deserialization, but it contains several conceptual and technical errors in relation to the specified requirements for correctly generating dataclasses and schema classes. Here's the evaluation based on the given criteria:

Definition of two dataclasses, one for the person and one for the order:
The solution defines two schema classes (Order and User) instead of two dataclasses. This is incorrect as schemas in marshmallow are not equivalent to dataclasses but are meant for serializing and deserializing purposes.
Score: 0/2
Definition of two schema classes, one for the person and one for the order:
The solution mixes up the definition of dataclasses and schema classes. It uses mm.Schema but it incorrectly names them as dataclasses.
The use of mm.SQLAlchemyAutoSchema is misleading, inappropriate, and irrelevant as no models are provided, and it's also unused.
Score: 0.5/2
Specify the date format for the date_of_birth field:
The solution does not specify the date format for date_of_birth. The formatting for this field is required to parse strings like '03/16/1977', but the solution changes the format in the example JSON and does not handle it in marshmallow.
Score: 0/1
The decimal field needs as_string=True:
The solution fails to apply the as_string=True condition on the Decimal field constructor in marshmallow, which is necessary to ensure the amount is correctly handled as a string in JSON.
Score: 0/1
The schema and dataclasses should be linked with a @post_load method on the schema classes:
There is no use of the @post_load decorator to link schema loading to a dataclass creation, which is crucial for correctly initializing dataclass instances from loaded data.
Score: 0/2
Anything else:
The implementation inaccurately names schema objects as dataclasses, which can be highly misleading for users unfamiliar with the difference.
Handling of nested structure (orders within user) is somewhat addressed, although incorrectly implemented, which indicates an understanding of nested data.
Incorrectly suggests changing input data format instead of handling conversions as required.
Score: 1/2

Overall, although the solution attempts to use marshmallow and alleviate handling of data types, it misses the requirements laid down in the problem statement and incorporates several wrong practices.

Final score:

{
  "score": 1.5
}

Response 2 (6.0)

To generate dataclasses for the provided JSON using the marshmallow library in Python, you can follow these steps:

First, make sure to install the marshmallow library by running pip install marshmallow.

Then, create the dataclasses:

from datetime import datetime
from dateutil import parser
from marshmallow import Schema, fields, pprint
from marshmallow.dataclass import class_schema

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

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

# Define the User dataclass
@class_schema
class User:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list

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

# Parse date_of_birth
json_data['date_of_birth'] = parser.parse(json_data['date_of_birth'])

# Initialize the schema
schema = UserSchema()

# Deserialize the JSON to the User dataclass
user_data = schema.load(json_data)

# Print the deserialized data
pprint(user_data.to_dict())

In this script, we define dataclasses for the User and Order objects using marshmallow. We also load the JSON using the schema, parse the string-formatted date_of_birth, and serialize it to the newly defined User dataclass.

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: (6.0) (1.00¢)

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

Defining two dataclasses (2 marks):
The solution defines one dataclass explicitly (User) using @class_schema. It doesn't explicitly define an Order dataclass. Half marks.
Defining two schema classes (2 marks):
The solution correctly defines two schema classes: UserSchema and OrderSchema. Full marks.
Specifying date format for date_of_birth (1 mark):
The solution doesn't specify the date format for the date_of_birth field. No marks.
Decimal field with as_string=True (1 mark):
The solution doesn't use as_string=True for the decimal field. No marks.
Linking schema and dataclasses with @post_load (2 marks):
The solution doesn't use @post_load to link schemas and dataclasses. Instead, it uses @class_schema, which is an alternative approach. Half marks.
Anything else (2 marks):
The solution imports necessary libraries.
It handles the JSON data loading and parsing.
It uses dateutil.parser to parse the date_of_birth.
Full marks for these additional considerations.

Breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "Linking schemas and dataclasses": 1,
  "Additional considerations": 2
}

Final score:

{
  "scores": [1, 2, 0, 0, 1, 2],
  "total_score": 6
}

The solution demonstrates a good understanding of using marshmallow for data serialization and deserialization. However, it misses some specific requirements like date format specification and decimal field configuration. The approach to defining dataclasses could be improved by explicitly defining both User and Order dataclasses. Overall, the solution provides a functional base but needs some refinements to fully meet the given requirements.

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

The provided solution attempts to utilize the marshmallow library to handle a JSON serialization and deserialization task involving complex data types such as dates and decimals. Here's the evaluation based on the given rubric:

Definition of Two Dataclasses (1/2 marks): The solution gets half credit here because it correctly defines a User dataclass, but does not define an Order dataclass, which is indicated in the task requirements. Instead, it defines schemas directly.
Definition of Two Schema Classes (2/2 marks): Two schema classes were correctly defined: UserSchema and OrderSchema. They are properly declared with appropriate fields.
Specification of Date Format for date_of_birth (0/1 mark): The field date_of_birth is declared in the UserSchema as fields.Date(), but no format ("%m/%d/%Y") is specified. Marshmallow supports specifying formats via the format argument in the fields, which was not used.
Decimal Field as String (0/1 mark): The amount field in OrderSchema uses fields.Decimal() but fails to include the as_string=True attribute. This is needed to ensure the decimal amount is properly serialized and deserialized as a string type.
Link Schema and Dataclasses with @post_load method (0/2 marks): The @post_load method is absent from both schema classes. These methods are crucial to properly link the schema with the dataclasses moving the data loading operation into dataclass instances.
Other Considerations (1/2 marks):
- Importing of necessary libraries and handling of datetime conversion is present, which is beneficial. However, the code erroneously attempts to convert date_of_birth outside of the marshmallow pipelines using parser.parse, which defeats some benefits of using marshmallow for handling all transformations.
- The user feedback via pprint of the resultant deserialization is positive.

Final assessment:

Given the assigned points, we see several significant misses on crucial criteria and only partial functionality correctness. Most importantly, the link between schema operations to the dataclass via @post_load and proper date and decimal handling are missing. Score:

{
  "score": 4
}

Response 3 (6.0)

Sure! Here's how you can create dataclasses using the marshmallow library for that JSON data. First, install the library using pip:

pip install marshmallow

Now, here's the Python code to generate the dataclasses:

from datetime import datetime
from dateutil.parser import parse
from decimal import Decimal
from marshmallow import Schema, fields, post_load


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

    @post_load
    def make_order(self, data, **kwargs):
        return {"created": data["created"], "amount": Decimal(data["amount"])}


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

    @post_load
    def make_person(self, data, **kwargs):
        data["date_of_birth"] = parse(data["date_of_birth"])
        data["orders"] = [OrderSchema().make_order(order) for order in data.get("orders", [])]
        return {"first_name": data["first_name"],
                "last_name": data["last_name"],
                "date_of_birth": data["date_of_birth"],
                "orders": data["orders"]}


# Usage example
person_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}
person_schema = PersonSchema()
person = person_schema.load(person_data)
print(person)

This will fulfill your requirements. For each element in the orders list, created will be of type datetime, and amount will be of type decimal, while date_of_birth will be a date.

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: (6.0) (0.94¢)

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

The solution does not define dataclasses. It uses dictionaries instead. (0/2 marks)
The solution defines two schema classes: OrderSchema and PersonSchema. (2/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The decimal field does not have as_string=True. (0/1 mark)
The schema classes are linked with @post_load methods. (2/2 marks)
Additional points:
The solution uses the dateutil.parser to parse the date_of_birth, which is a flexible approach.
It handles the nested structure of orders correctly.
It provides a usage example.
(2/2 marks)

Breakdown of marks:

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

Score:

{
  "scores": [0, 2, 0, 0, 2, 2],
  "total_score": 6
}

Comments:
The solution provides a working implementation using marshmallow schemas, but it doesn't fully meet all the requirements. It doesn't use dataclasses as requested, instead opting for dictionaries. The date format for date_of_birth is not explicitly specified, and the decimal field is not configured with as_string=True. However, the solution does correctly implement schema classes with post_load methods and handles nested structures well. To improve, the solution should incorporate dataclasses, specify the date format, and configure the decimal field as requested.

openai/gpt-4-0125-preview says: (3.0) ()

The provided solution attempts to use the marshmallow library to convert given JSON data into a Python dictionary structure, employing serialization and deserialization patterns. However, it doesn't fully align with all requirements laid out in the problem statement.

Define two dataclasses:
- The solution does not explicitly define dataclasses for the person and the order. Instead, it directly returns dictionaries inside the @post_load methods.
- Marks awarded: 0/2
Define two schema classes:
- Two schema classes, OrderSchema and PersonSchema, are correctly defined.
- Marks awarded: 2/2
Specify the date format for the date_of_birth field:
- The date_of_birth field in PersonSchema should have specified format="%m/%d/%Y" in the Date field but it merely uses fields.Date(). The correct format was not set.
- Marks awarded: 0/1
Decimal field needs as_string=True:
- The field for amount in OrderSchema uses fields.Decimal() without the as_string=True property. Therefore, it does not meet the requirement to ensure the amount is handled as a string internally.
- Marks awarded: 0/1
Link schema and dataclasses with a @post_load method:
- While @post_load methods are used, they do not create dataclass instances as no dataclasses are defined. They return dictionaries which is not the intent of dataclasses which should bundle data into an object.
- Marks awarded: 0/2
Additional Marks:
- The schemas are loaded correctly to deserialize the JSON data with attempts to convert data into proper types like datetime and Decimal although not precisely as required.
- Minor issues such as usage of the undefined parse function from dateutil (likely intended as datetime.strptime) show gaps in the implementation.
- The nested 'OrderSchema' is used effectively in the 'PersonSchema'.
- Marks awarded: 1/2

Overall evaluation:
- The solution concept of using marshmallow to parse and structure data from JSON is good but has gaps in execution concerning the specified requirements. The decision to use dictionaries over actual dataclasses along with missing specifics on formatting and type handling means that the solution scores lower.

{
  "score": 3
}

1.00	0.90	0.86
0.90	1.00	0.87
0.86	0.87	1.00

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