Synthia 70B

Description

SynthIA (Synthetic Intelligent Agent) is a LLama-2 70B model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations.

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?

In order to generate Python classes using the marshmallow library and deserialize the JSON data into objects, you can follow these steps:

  1. Install the marshmallow library:
pip install marshmallow
  1. Import the required modules:
import marshmallow as ma
  1. Define your own dataclasses and their schema. Here we have a User and Order class, which are defined for the given JSON data:
class UserSchema(ma.ModelSchema):
    first_name = ma.fields.Str()
    last_name = ma.fields.Str()
    date_of_birth = ma.fields.Date()
    orders = ma.Nested(OrderSchema, many=True)


class OrderSchema(ma.ModelSchema):
    created = ma.DateTime()
    amount = Decimal()
  1. Create a new User object and Order object and attach the OrderSchema as a nested field:
class User(UserSchema):
    def __init__(self, email, first_name, last_name):
        self.email = email
        # Create other instance variables using the attributes in User class


class Order(ma.Schema):
    def __init__(self, created, amount=None):
        self.created = created
        self.amount = amount
  1. Create an instance of the JSON data and pass it through a marshmallow as_map method to obtain the desired result:
    ```python
    user_dict = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1970
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) (1.01¢)

Let's evaluate this solution based on the provided marking scheme:

  1. Defining two dataclasses (2 marks):
    The solution defines a User class and an Order class, but they are not proper dataclasses. They are regular classes.
    Score: 1/2

  2. Defining two schema classes (2 marks):
    The solution defines UserSchema and OrderSchema.
    Score: 2/2

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

  4. Decimal field with as_string=True (1 mark):
    The solution does not include as_string=True for the amount field.
    Score: 0/1

  5. Linking schema and dataclasses with @post_load (2 marks):
    The solution does not use @post_load to link schemas and classes.
    Score: 0/2

  6. Anything else (2 marks):

  7. The solution imports marshmallow correctly.
  8. It attempts to create a nested structure for orders in UserSchema.
  9. However, there are some issues:
  10. The User class inherits from UserSchema, which is incorrect.
  11. The Order class inherits from ma.Schema instead of OrderSchema.
  12. The implementation is incomplete (the JSON example is cut off).
    Score: 1/2

Breakdown of marks:

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

Overall score:

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

This solution shows some understanding of marshmallow but has several significant issues. It doesn't properly implement dataclasses, misses important field configurations, and doesn't use @post_load for object creation. The class structure is also incorrect, with schemas being used as base classes instead of separate entities. The implementation is incomplete, making it difficult to fully assess its functionality.

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

The provided solution to using marshmallow for JSON deserialization into Python data classes has several issues and omissions based on the requirements:

  1. Dataclasses Definition:

    • The user-defined classes (User, Order) are not defined with Python's built-in dataclass decorator, which is needed for marking them as data classes.
    • Score: 0/2

  2. Schema Classes Definition:

    • The OrderSchema and UserSchema are defined using ma.ModelSchema, which is incorrect as there is no such class in marshmallow. It should be ma.Schema.
    • Score: 0/2

  3. Date Format Specification:

    • date_of_birth in the UserSchema is mentioned but lacks a format specification. The correct definition should include format="%m/%d/%Y".
    • Score: 0/1

  4. Decimal Field Specification:

    • The amount field in OrderSchema is simply defined as Decimal(), which is not recognized by marshmallow directly for field definitions. It should be ma.fields.Decimal(as_string=True).
    • Score: 0/1

  5. Linkage with @post_load:

    • There’s no @post_load decorator found in the provided solution. This is essential for ensuring that the deserialized JSON data transforms into instances of the data classes.
    • Score: 0/2

  6. Overall Architecture and Logic:

    • The instantiation logic within classes User and Order is mixed up. It seems the classes are inheriting from their own schemas (User(UserSchema)) which should not be the case.
    • Use of ma.ModelSchema and general confusion between class definition and instantiation suggests a fundamental misunderstanding of marshmallow schemas' purpose and usage.
    • Schemas should be separate entities that guide the deserialization/serialization processes but should not serve as base classes for the models themselves.
    • Score: 0/2

Total Score: 

{
  "score": 0
}

This evaluation reflects that the solution fails to meet the requirements as specified and contains multiple conceptual and implementation errors.

To generate dataclasses using the marshmallow library in Python, you can follow the steps below:

  1. Install marshmallow package using pip:
    pip install marshmallow

  2. Import marshmallow and define the dataclasses:
    ```python
    from marshmallow import Schema, fields, post_load

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

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

class Person(Schema):
class Meta:
_schema = PersonSchema
strict = True
```

  1. Create a person object like this:
    python person = Person({ 'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': '03/16/19785', 'orders': [ {'created': '2024-01-25T15:00:04', 'amount': '100.00'}] } })

Here is the explanation of the code:

  • First, you import the relevant modules from the marshmallow library and create Person and Order schemas using Schema and the required fields. The fields.Str is for strings, fields.Date for dates,
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) (0.85¢)

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

  1. Dataclasses:
    The solution doesn't define dataclasses. It defines schema classes, but not separate dataclasses for Person and Order.

  2. Schema classes:
    The solution correctly defines two schema classes: PersonSchema and OrderSchema.

  3. Date format for date_of_birth:
    The solution doesn't specify the date format for the date_of_birth field.

  4. Decimal field:
    The solution doesn't include as_string=True for the amount field.

  5. @post_load method:
    The solution doesn't include a @post_load method to link the schema and dataclasses.

  6. Other observations:

  7. The solution imports necessary modules from marshmallow.
  8. It uses nested fields for the orders in PersonSchema.
  9. The Person class inherits from Schema, which is incorrect. It should be a dataclass.
  10. The date_of_birth in the example data is incorrect (19785 instead of 1977).

Here's the breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "@post_load method": 0,
  "Other observations": 1
}

Final score:

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

The solution provides a partial implementation but misses several key requirements from the marking scheme. It correctly defines the schema classes but fails to create proper dataclasses and misses important details like date format specification and the use of @post_load method.

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

The given solution attempts to use the marshmallow library to parse and validate JSON data into Python dataclasses. While the description proposes some correct steps, the actual implementation contains numerous issues.

  1. Definition of dataclasses: The solution does not employ Python's built-in dataclass module, nor does it appropriately define the necessary classes for capturing the 'Person' and 'Order' data structures. Instead, they have mistakenly defined 'Person' as a subclass of Schema. This fails to match the requirement. (0/2 marks)

  2. Definition of schema classes: The solution includes PersonSchema and OrderSchema with field identifiers specified. However, 'Person' is misdefined as a schema itself rather than a pure data structure, and 'Order' is completely absent. Partial completion by providing the schemas accurately. (1/2 marks)

  3. Specification of date format: The date_of_birth field in the PersonSchema class is defined as a Date field, but it does not specify the required format ("%m/%d/%Y") as requested in the instructions. (0/1 mark)

  4. Decimal Field with as_string=True: The amount field in the OrderSchema is declared as a Decimal field, but it fails to include as_string=True, which is essential for ensuring the amount remains a string when serialized back to JSON, preserving possible leading zeros and exact number formats needed in financial computations. (0/1 mark)

  5. Linking schema and dataclasses via @post_load: The solution does not contain any use of @post_load. This decorator is critical for processing the data after load, thereby instantiating the appropriate dataclass instances. This step is completely missed. (0/2 marks)

  6. Additional aspects: While the solution initializes a Person instance using a dictionary resembling JSON input, it not only misplaces the schema functionalities but also contains various syntactical and logical errors. This section does not correspond correctly to any operational or best practice methods that would be considered beneficial. Misconfiguration of how instantiation should be approached with marshmallow schemas also bears severe penalties. (0/2 marks)

Overall, the solution has substantial problems in understanding and implementing the essential features of the marshmallow library and Python dataclasses, especially regarding how schemas and data are supposed to be manipulated and interconnected. 

{
  "score": 1
}

To generate dataclasses from a JSON using marshmallow in Python, you can create a class and define dataclass fields for each key in the JSON along with their respective types. Here's an example:

import datetime
from marshmallow import Schema, fields


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


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

You can then specify the encoding and decoding of your JSON using the schema to get the dataclasses:

```python

from pymongo import MongoClient
import pprint
user = {'first_name': 'Jane', 'last_name': 'Smith', 'date_of_birth': '03/16/1977', 'orders': [{'created': datetime.datetime(2024, 1, 25, 15, 0, 4), 'amount': 100.00}]}
user_schema = UserSchema()
pprint(user_schema.loads(user)
pprint(PrettyPrinter(dict)(person))
{'date_of_birth': datetime.date(2024, 1, 25), 'first_name': 'Jane', 'last_name': 'Smith', 'orders': [{'created': datetime.datetime(2024, 1, 25, 1

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:

  1. Defining two dataclasses:
    The solution doesn't define any dataclasses. It only defines schema classes.
    Marks: 0/2

  2. Defining two schema classes:
    The solution correctly defines two schema classes: UserSchema and OrderSchema.
    Marks: 2/2

  3. Specifying date format for date_of_birth:
    The solution doesn't specify the date format for the date_of_birth field.
    Marks: 0/1

  4. Decimal field with as_string=True:
    The solution doesn't include as_string=True for the decimal field.
    Marks: 0/1

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

  6. Anything else:

  7. The solution correctly uses fields.Nested for the orders field.
  8. It demonstrates how to use the schema to load data.
  9. However, it includes unnecessary imports (pymongo) and doesn't properly format the example output.
    Marks: 1/2

Comments:
The solution focuses on creating schema classes using marshmallow but doesn't address the requirement of creating dataclasses. It misses some key requirements like specifying the date format and using as_string=True for the decimal field. The solution doesn't demonstrate how to link the schemas to dataclasses using @post_load. While it shows how to use the schema to load data, the example is incomplete and contains errors.

Here's the breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Linking schema and dataclasses": 0,
  "Additional considerations": 1
}

Final score:

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

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

The solution provided demonstrates the use of the Marshmallow library to generate schemas for deserializing JSON to Python objects. Although there were attempts to define schemas for a user and their orders, let's evaluate the provided solution based on the marking scheme to assess its effectiveness and completeness.

  1. Defining Two Dataclasses: The solution does not define any dataclasses using Python's dataclasses module or by any other means. It strictly defines schema classes with no explicit dataclass structures.

  2. Score: 0/2

  3. Defining Two Schema Classes: The solution defines two schema classes (UserSchema and OrderSchema). These schemas correspond to the person and the orders, which meets the part of the requirement.

  4. Score: 2/2

  5. Specifying Date Format for date_of_birth: The UserSchema has defined date_of_birth but has no explicit format specified as "%m/%d/%Y". It needs the parameter format="%m/%d/%Y" which is missing.

  6. Score: 0/1

  7. Decimal Field as_string=True: The amount in OrderSchema does not have the parameter as_string=True. This parameter is essential to ensure amounts are correctly handled as strings to preserve exact value.

  8. Score: 0/1

  9. Linking Schema and Dataclasses with @post_load Method: There is no @post_load method in the solution provided to convert the loaded data into a dataclass or any other custom object. This method is crucial for integrating deserialized data with a dataclass.

  10. Score: 0/2

  11. Additional Attributes: The solution does demonstrate a basic understanding of using the Marshmallow library to construct schemas and mentions loading a JSON-like structure. However, references to irrelevant libraries like pymongo and incorrect code in usage (e.g., incorrect and incomplete pprint implementation) further detract from the clarity and correctness of the solution.

  12. Score: 0/2

Final Conclusion and Score

The solution did put forth an attempt to use Marshmallow for schema definition but lacked critical elements including dataclass definition, proper field handling, and integration between loaded data and Pythonic objects. Significant errors and omissions in critical areas result in a low score.

{
  "score": 2
}
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