ium_444380/train_model.py

import sys

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from keras.models import Sequential
from keras.layers import Dense
import tensorflow as tf
import numpy as np


tf.config.set_visible_devices([], 'GPU')

# Read and split data
train_data = pd.read_csv("crime_train.csv")
val_data = pd.read_csv("crime_dev.csv")
test_data = pd.read_csv("crime_test.csv")

x_columns = ["DISTRICT", "STREET", "YEAR", "MONTH", "DAY_OF_WEEK", "HOUR", "Lat", "Long"]
y_column = "OFFENSE_CODE_GROUP"

x_train = train_data[x_columns]
y_train = train_data[y_column]
x_val = val_data[x_columns]
y_val = val_data[y_column]
x_test = test_data[x_columns]
y_test = test_data[y_column]

num_categories = len(y_train.unique())
num_features = len(x_columns)

# Train label encoders for categorical data
encoder_y = LabelEncoder()
encoder_day = LabelEncoder()
encoder_dist = LabelEncoder()
encoder_street = LabelEncoder()
encoder_y.fit(y_train)
encoder_day.fit(x_train["DAY_OF_WEEK"])
encoder_dist.fit(x_train["DISTRICT"])
encoder_street.fit(pd.concat([x_val["STREET"], x_test["STREET"], x_train["STREET"]], axis=0))


# Encode train categorical data
y_train = encoder_y.transform(y_train)
x_train["DAY_OF_WEEK"] = encoder_day.transform(x_train["DAY_OF_WEEK"])
x_train["DISTRICT"] = encoder_dist.transform(x_train["DISTRICT"])
x_train["STREET"] = encoder_street.transform(x_train["STREET"])

# Encode train categorical data
y_val = encoder_y.transform(y_val)
x_val["DAY_OF_WEEK"] = encoder_day.transform(x_val["DAY_OF_WEEK"])
x_val["DISTRICT"] = encoder_dist.transform(x_val["DISTRICT"])
x_val["STREET"] = encoder_street.transform(x_val["STREET"])

# Encode train categorical data
y_test = encoder_y.transform(y_test)
x_test["DAY_OF_WEEK"] = encoder_day.transform(x_test["DAY_OF_WEEK"])
x_test["DISTRICT"] = encoder_dist.transform(x_test["DISTRICT"])
x_test["STREET"] = encoder_street.transform(x_test["STREET"])

# Define model
model = Sequential()
model.add(Dense(32, activation='relu', input_dim=num_features))
model.add(Dense(64, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(num_categories, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'sparse_categorical_accuracy'])

if len(sys.argv) > 1:
    epochs = sys.argv[1]
else:
    epochs = 10

# Train model
model.fit(x_train, y_train, epochs=int(epochs), validation_data=(x_val, y_val))

# Make predictions
y_pred = model.predict(x_test)
output = [np.argmax(pred) for pred in y_pred]
output_text = encoder_y.inverse_transform(list(output))

# Save predictions
data_to_save = pd.concat([test_data[x_columns], test_data[y_column]], axis = 1)
data_to_save["PREDICTED"] = output_text
data_to_save.to_csv("out.csv")

# Save model
model.save("model")