Amburoute

This project involves several Python scripts, each serving a unique purpose:

• train.py: Responsible for training the model using provided data.
• predict.py: Used for making predictions based on the trained model.
• map.py: Contains functions for converting geographical coordinates.
• location.py: Manages location data and file reading.
• draw.py: (Not detailed in the provided code but likely for visualizing results).

Training the Model

The training process involves reading city data, transforming it into a suitable format, and fitting a logistic regression model. Here’s a breakdown of the train.py script:

• Data Preparation: The get_city_set function extracts unique city names from the input data.
• Mapping Cities: The read_file function creates a mapping of city names to integer labels.
• Model Training: The Trainer class handles the training process, where the model is fitted to the transformed data.

Example Code for Training

from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
 
class Trainer(object):
    def __init__(self, map, binary):
        self.map = map
        self.binary = binary
 
    def train(self, data):
        x, y = self.__transform(data)
        model = LogisticRegression(multi_class='multinomial', solver='saga')
        model.fit(x, y)
        return model