104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
import sys
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import pandas as pd
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from sklearn.preprocessing import LabelEncoder
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from keras.models import Sequential
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from keras.layers import Dense
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import tensorflow as tf
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import numpy as np
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from sacred import Experiment
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from sacred.observers import FileStorageObserver, MongoObserver
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ex = Experiment(save_git_info=False)
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ex.observers.append(FileStorageObserver("experiments"))
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#ex.observers.append(MongoObserver(url="mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017", db_name="sacred"))
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@ex.config
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def config():
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epochs = 10
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@ex.automain
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def main(epochs):
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tf.config.set_visible_devices([], 'GPU')
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# Read and split data
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train_data = pd.read_csv("crime_train.csv")
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val_data = pd.read_csv("crime_dev.csv")
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test_data = pd.read_csv("crime_test.csv")
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x_columns = ["DISTRICT", "STREET", "YEAR", "MONTH", "DAY_OF_WEEK", "HOUR", "Lat", "Long"]
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y_column = "OFFENSE_CODE_GROUP"
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x_train = train_data[x_columns]
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y_train = train_data[y_column]
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x_val = val_data[x_columns]
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y_val = val_data[y_column]
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x_test = test_data[x_columns]
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y_test = test_data[y_column]
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num_categories = len(y_train.unique())
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num_features = len(x_columns)
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# Train label encoders for categorical data
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encoder_y = LabelEncoder()
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encoder_day = LabelEncoder()
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encoder_dist = LabelEncoder()
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encoder_street = LabelEncoder()
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encoder_y.fit(y_train)
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encoder_day.fit(x_train["DAY_OF_WEEK"])
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encoder_dist.fit(x_train["DISTRICT"])
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encoder_street.fit(pd.concat([x_val["STREET"], x_test["STREET"], x_train["STREET"]], axis=0))
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# Encode train categorical data
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y_train = encoder_y.transform(y_train)
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x_train["DAY_OF_WEEK"] = encoder_day.transform(x_train["DAY_OF_WEEK"])
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x_train["DISTRICT"] = encoder_dist.transform(x_train["DISTRICT"])
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x_train["STREET"] = encoder_street.transform(x_train["STREET"])
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# Encode train categorical data
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y_val = encoder_y.transform(y_val)
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x_val["DAY_OF_WEEK"] = encoder_day.transform(x_val["DAY_OF_WEEK"])
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x_val["DISTRICT"] = encoder_dist.transform(x_val["DISTRICT"])
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x_val["STREET"] = encoder_street.transform(x_val["STREET"])
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# Encode train categorical data
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y_test = encoder_y.transform(y_test)
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x_test["DAY_OF_WEEK"] = encoder_day.transform(x_test["DAY_OF_WEEK"])
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x_test["DISTRICT"] = encoder_dist.transform(x_test["DISTRICT"])
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x_test["STREET"] = encoder_street.transform(x_test["STREET"])
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# Define model
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model = Sequential()
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model.add(Dense(32, activation='relu', input_dim=num_features))
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model.add(Dense(64, activation='relu'))
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model.add(Dense(128, activation='relu'))
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model.add(Dense(num_categories, activation='softmax'))
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'sparse_categorical_accuracy'])
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# Train model
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history = model.fit(x_train, y_train, epochs=int(epochs), validation_data=(x_val, y_val))
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# Make predictions
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y_pred = model.predict(x_test)
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output = [np.argmax(pred) for pred in y_pred]
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output_text = encoder_y.inverse_transform(list(output))
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# Save predictions
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data_to_save = pd.concat([test_data[x_columns], test_data[y_column]], axis = 1)
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data_to_save["PREDICTED"] = output_text
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data_to_save.to_csv("out.csv")
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# Save model
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model.save("model")
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ex.add_artifact("model/saved_model.pb")
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# Log metrics
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ex.log_scalar("loss", history.history["loss"])
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ex.log_scalar("accuracy", history.history["accuracy"])
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ex.log_scalar("val_loss", history.history["val_loss"])
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ex.log_scalar("val_accuracy", history.history["val_accuracy"])
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