89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
# This is a sample Python script.
|
|
|
|
# Press Shift+F10 to execute it or replace it with your code.
|
|
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
|
|
from keras.models import Sequential
|
|
from keras.layers import Dense
|
|
from keras.optimizers import Adam
|
|
import pandas as pd
|
|
import tensorflow as tf
|
|
import numpy as np
|
|
from sklearn.preprocessing import LabelEncoder
|
|
import argparse
|
|
import shutil
|
|
import mlflow
|
|
import logging
|
|
logging.basicConfig(level=logging.WARN)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
mlflow.set_tracking_uri("http://localhost:5000")
|
|
mlflow.set_experiment("s487197")
|
|
|
|
def get_x_y(data):
|
|
|
|
lb = LabelEncoder()
|
|
data = data.drop(["Location 1"], axis=1)
|
|
data = data.drop(columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post", "CrimeDate", "Inside/Outside"], axis=1)
|
|
for column_name in data.columns:
|
|
data[column_name] = lb.fit_transform(data[column_name])
|
|
x = data.drop('Weapon', axis=1)
|
|
y = data['Weapon']
|
|
|
|
|
|
|
|
return data, x, y
|
|
|
|
def train_model():
|
|
|
|
parser = argparse.ArgumentParser(description='Train')
|
|
|
|
parser.add_argument('-epochs', type=int, default=20)
|
|
parser.add_argument('-lr', type=float, default=0.01)
|
|
parser.add_argument('-validation_split', type=float, default=0.2)
|
|
args = parser.parse_args()
|
|
|
|
train = pd.read_csv('baltimore_train.csv')
|
|
|
|
data_train, x_train, y_train = get_x_y(train)
|
|
normalizer = tf.keras.layers.Normalization(axis=1)
|
|
normalizer.adapt(np.array(x_train))
|
|
with mlflow.start_run() as run:
|
|
print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
|
|
print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))
|
|
mlflow.log_param("epochs", args.epochs)
|
|
mlflow.log_param("lr", args.lr)
|
|
mlflow.log_param("validation_split", args.validation_split)
|
|
model = Sequential(normalizer)
|
|
model.add(Dense(64, activation="relu"))
|
|
model.add(Dense(10, activation='relu'))
|
|
model.add(Dense(10, activation='relu'))
|
|
model.add(Dense(10, activation='relu'))
|
|
model.add(Dense(5, activation="softmax"))
|
|
model.compile(Adam(learning_rate=args.lr), loss='sparse_categorical_crossentropy', metrics = ['accuracy'] )
|
|
model.summary()
|
|
|
|
history = model.fit(
|
|
x_train,
|
|
y_train,
|
|
epochs=args.epochs,
|
|
validation_split=args.validation_split)
|
|
mlflow.log_metric("loss", float(, hist['loss']))
|
|
mlflow.log_metric('accuracy', float(hist['accuracy']))
|
|
signature = mlflow.models.signature.infer_signature(train_x, model.predict(x_test))
|
|
if tracking_url_type_store != "file":
|
|
|
|
mlflow.sklearn.log_model(mlf_model, "baltimore_model", registered_model_name="BaltimoreModel", signature=signature)
|
|
else:
|
|
mlflow.sklearn.log_model(mlf_model, "model", signature=signature)
|
|
hist = pd.DataFrame(history.history)
|
|
hist['epoch'] = history.epoch
|
|
mlflow.sklearn.save_model(mlf_model, "mlflow_model")
|
|
shutil.make_archive('mlflow_model', 'zip', 'mlflow_model')
|
|
model.save('baltimore_model')
|
|
|
|
shutil.make_archive('baltimore', 'zip', 'baltimore_model')
|
|
|
|
|
|
train_model()
|
|
|