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from typing import Union, Tuple |
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import datetime |
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class NextPlaceBaseModel: |
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def __init__(self): |
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self._load_model() |
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def _load_model(self): |
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""" |
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Perform any actions needed to load the model. |
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EX: Establish API connections, download an ML model for inference, etc... |
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""" |
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print("Loading model...") |
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print("Model loaded.") |
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def _sale_date_predictor(self, daysOnMarket: int): |
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""" |
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Calculate the expected sale date based on the national average |
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:param daysOnMarket: number of days this house has been on the market |
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:return: the predicted sale date, based on the national average of 34 days |
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""" |
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national_average = 34 |
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if daysOnMarket < national_average: |
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days_until_sale = national_average - daysOnMarket |
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sale_date = datetime.date.today() + datetime.timedelta(days=days_until_sale) |
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return sale_date |
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else: |
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return datetime.date.today() + datetime.timedelta(days=1) |
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def run_inference(self, input_data: dict[str, Union[str, int, float]]) -> Tuple[float, str]: |
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""" |
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Predict the sale price and sale date for the house represented by `input_data` |
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:param input_data: a formatted Synapse from the validator, representing a currently listed house |
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:return: the predicted sale price and predicted sale date for this home |
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""" |
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predicted_sale_price = float(input_data['price']) if ('price' in input_data) else 1.0 |
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predicted_sale_date = self._sale_date_predictor(input_data['dom']) if ('dom' in input_data) else datetime.date.today() + datetime.timedelta(days=1) |
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predicted_sale_date = predicted_sale_date.strftime("%Y-%m-%d") |
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print(f"Predicted sale price: {predicted_sale_price}") |
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print(f"Predicted sale date: {predicted_sale_date}") |
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return predicted_sale_price, predicted_sale_date |
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