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