path: root/models/failure_prediction/python/lstm.py

#  pylint: disable=C0103, C0116, W0621, E0401, W0104, W0105, R0913, E1136, W0612, E0102, C0301, W0611, C0411, W0311, W0404, E0602, C0326, C0330, W0106, C0412
# -*- coding: utf-8 -*-
"""LSTM.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/15natzoGkWnOqxZyzavAaRqBFrPNxzd35

Contributors: Rohit Singh Rathaur, Girish L.

Copyright 2021 [Rohit Singh Rathaur, BIT Mesra and Girish L., CIT GUBBI, Karnataka]

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

We mounted the drive to access the data from google drive
"""

from keras.utils.vis_utils import plot_model
from keras.layers import Activation, Dense, Dropout
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import tensorflow as tf
from google.colab import drive
drive.mount('/content/drive')

"""We are importing the libraries:
- TensorFlow: to process and train the model
- Matplotlib: to plot the training anf loss curves
- Pandas: used for data analysis and it allows us to import data from various formats
- Numpy: For array computing
"""

# Importing libraries

"""We are reading the CSV file using `read_csv` function and storing it in a DataFrame named `df_Ellis`"""

df_Ellis = pd.read_csv(
    "/content/drive/MyDrive/Failure/lstm/Ellis_FinalTwoConditionwithOR.csv")
df_Ellis

"""`plot()` function is used to draw points"""

df_Ellis.plot()

"""Using multivariate features:
- Storing only the multivariate features in a dataframe named `features_3`
- Extracting the Timestamp column from `df_Ellis` dataframe
- and combining it with the dataframe `features`
"""

# using multivariate feature

features_3 = [
    'ellis-cpu.system_perc',
    'ellis-cpu.wait_perc',
    'ellis-load.avg_1_min',
    'ellis-mem.free_mb',
    'ellis-net.in_bytes_sec',
    'ellis-net.out_packets_sec',
    'Label']

features = df_Ellis[features_3]
features.index = df_Ellis['Timestamp']
features.head()

"""Plotted features"""

features.plot(subplots=True)

features = features.values

# standardize data
train_split = 141600
tf.random.set_seed(13)

# standardize data
features_mean = features[:train_split].mean()
features_std = features[:train_split].std()
features = (features - features_mean) / features_std

print(type(features))
print(features.shape)

# create mutlivariate data


def mutlivariate_data(
        features,
        target,
        start_idx,
        end_idx,
        history_size,
        target_size,
        step,
        single_step=False):
    data = []
    labels = []
    start_idx = start_idx + history_size
    if end_idx is None:
        end_idx = len(features) - target_size
    for i in range(start_idx, end_idx):
        idxs = range(i - history_size, i, step)  # using step
        data.append(features[idxs])
        if single_step:
            labels.append(target[i + target_size])
        else:
            labels.append(target[i:i + target_size])

    return np.array(data), np.array(labels)


"""We spliited the multivariate data in tarining and validation and printed the shape of that data."""

# generate multivariate data

history = 720
future_target = 72
STEP = 6

x_train_ss, y_train_ss = mutlivariate_data(
    features, features[:, 1], 0, train_split, history, future_target, STEP, single_step=True)

x_val_ss, y_val_ss = mutlivariate_data(features, features[:, 1], train_split, None, history,
                                       future_target, STEP, single_step=True)

print(x_train_ss.shape, y_train_ss.shape)
print(x_val_ss.shape, y_val_ss.shape)

"""The `tf.data.Dataset` API supports writing descriptive and efficient input pipelines. Dataset usage following a common pattern:
- Creating a source dataset from our input data.
- Applied dataset transformations to preprocess the data.
- Iterate over the dataset and process the elements.
Note: Iteration happens in a streaming fashion, so the full dataset does not need to fit into memory.
Once we have a dataset, we can apply transformations to prepare the data for our model:
"""

# tensorflow dataset
batch_size = 256
buffer_size = 10000

train_ss = tf.data.Dataset.from_tensor_slices((x_train_ss, y_train_ss))
train_ss = train_ss.cache().shuffle(buffer_size).batch(batch_size).repeat()

val_ss = tf.data.Dataset.from_tensor_slices((x_val_ss, y_val_ss))
val_ss = val_ss.cache().shuffle(buffer_size).batch(batch_size).repeat()

print(train_ss)
print(val_ss)

"""We used a custom loss function to evaluate the model:"""


def root_mean_squared_error(y_true, y_pred):
    return K.sqrt(K.mean(K.square(y_pred - y_true)))


"""We are building a single step LSTM model for training data with dropout 0.3 and we used ADAM optimizers."""

# Modelling using LSTM
steps = 50

EPOCHS = 20

single_step_model = tf.keras.models.Sequential()

single_step_model.add(tf.keras.layers.LSTM(
    32, return_sequences=False, input_shape=x_train_ss.shape[-2:]))
single_step_model.add(tf.keras.layers.Dropout(0.3))
single_step_model.add(tf.keras.layers.Dense(1))
single_step_model.compile(
    optimizer=tf.keras.optimizers.Adam(),
    loss='mae',
    metrics=[
        tf.keras.metrics.RootMeanSquaredError(
            name='rmse')])
#single_step_model.compile(loss='mse', optimizer='rmsprop')
single_step_model_history = single_step_model.fit(
    train_ss,
    epochs=EPOCHS,
    steps_per_epoch=steps,
    validation_data=val_ss,
    validation_steps=50)
single_step_model.summary()
plot_model(
    single_step_model,
    to_file='/content/drive/MyDrive/Failure/lstm/LSTM.png',
    show_shapes=True,
    show_layer_names=True)

"""We defined the `plot_loss` function to plot the train and test loss"""

# plot train test loss


def plot_loss(history, title):
    loss = history.history['loss']
    val_loss = history.history['val_loss']

    epochs = range(len(loss))
    plt.figure()
    plt.plot(epochs, loss, 'b', label='Train Loss')
    plt.plot(epochs, val_loss, 'r', label='Validation Loss')
    plt.title(title)
    plt.legend()
    plt.grid()
    plt.show()


plot_loss(single_step_model_history,
          'Single Step Training and validation loss')

# plot train test loss


def plot_loss(history, title):
    loss = history.history['rmse']
    val_loss = history.history['val_rmse']

    epochs = range(len(loss))
    plt.figure()
    plt.plot(epochs, loss, 'b', label='Train RMSE')
    plt.plot(epochs, val_loss, 'r', label='Validation RMSE')
    plt.title(title)
    plt.legend()
    plt.grid()
    plt.show()


plot_loss(single_step_model_history,
          'Single Step Training and validation loss')

"""We defined a function `create_time_steps` to create time steps and function `plot_time_series` to plot the time series data"""

# fucntion to create time steps


def create_time_steps(length):
    return list(range(-length, 0))

# function to plot time series data


def plot_time_series(plot_data, delta, title):
    labels = ["History", 'True Future', 'Model Predcited']
    marker = ['.-', 'rx', 'go']
    time_steps = create_time_steps(plot_data[0].shape[0])

    if delta:
        future = delta
    else:
        future = 0
    plt.title(title)
    for i, x in enumerate(plot_data):
        if i:
            plt.plot(
                future,
                plot_data[i],
                marker[i],
                markersize=10,
                label=labels[i])
        else:
            plt.plot(
                time_steps,
                plot_data[i].flatten(),
                marker[i],
                label=labels[i])
    plt.legend()
    plt.xlim([time_steps[0], (future + 5) * 2])

    plt.xlabel('Time_Step')
    return plt

# Moving window average


def MWA(history):
    return np.mean(history)


"""We plotted the time series and predicted values"""

# plot time series and predicted values

for x, y in val_ss.take(5):
    plot = plot_time_series([x[0][:, 1].numpy(), y[0].numpy(),
                             single_step_model.predict(x)[0]], 12,
                            'Single Step Prediction')
    plot.show()

"""# **MultiStep Forcasting**

We splitted the data in the form of training and validation for multistep forcasting:
"""

future_target = 72  # 72 future values
x_train_multi, y_train_multi = mutlivariate_data(features, features[:, 1], 0,
                                                 train_split, history,
                                                 future_target, STEP)
x_val_multi, y_val_multi = mutlivariate_data(features, features[:, 1],
                                             train_split, None, history,
                                             future_target, STEP)

print(x_train_multi.shape)
print(y_train_multi.shape)

"""The `tf.data.Dataset` API supports writing descriptive and efficient input pipelines. Dataset usage following a common pattern:
- Creating a source dataset from our input data.
- Applied dataset transformations to preprocess the data.
- Iterate over the dataset and process the elements.
Note: Iteration happens in a streaming fashion, so the full dataset does not need to fit into memory.
Once we have a dataset, we can apply transformations to prepare the data for our model:
"""

#  TF DATASET

train_data_multi = tf.data.Dataset.from_tensor_slices(
    (x_train_multi, y_train_multi))
train_data_multi = train_data_multi.cache().shuffle(
    buffer_size).batch(batch_size).repeat()

val_data_multi = tf.data.Dataset.from_tensor_slices((x_val_multi, y_val_multi))
val_data_multi = val_data_multi.batch(batch_size).repeat()

print(train_data_multi)
print(val_data_multi)

"""We created a `multi_step_plot` function to plot between `history` and `true_future` data"""

# plotting function


def multi_step_plot(history, true_future, prediction):
    plt.figure(figsize=(12, 6))
    num_in = create_time_steps(len(history))
    num_out = len(true_future)
    plt.grid()
    plt.plot(num_in, np.array(history[:, 1]), label='History')
    plt.plot(np.arange(num_out) / STEP, np.array(true_future), 'bo',
             label='True Future')
    if prediction.any():
        plt.plot(np.arange(num_out) / STEP, np.array(prediction), 'ro',
                 label='Predicted Future')
    plt.legend(loc='upper left')
    plt.show()


for x, y in train_data_multi.take(1):
    multi_step_plot(x[0], y[0], np.array([0]))

"""We are building a single step LSTM model for training data with dropout 0.3 and we used ADAM optimizers."""

multi_step_model = tf.keras.models.Sequential()
multi_step_model.add(tf.keras.layers.LSTM(
    32, return_sequences=True, input_shape=x_train_multi.shape[-2:]))
multi_step_model.add(tf.keras.layers.LSTM(16, activation='relu'))
# aDD dropout layer (0.3)
multi_step_model.add(tf.keras.layers.Dense(72))  # for 72 outputs

multi_step_model.compile(
    optimizer=tf.keras.optimizers.RMSprop(
        clipvalue=1.0), loss='mae', metrics=[
            tf.keras.metrics.RootMeanSquaredError(
                name='rmse')])

multi_step_history = multi_step_model.fit(train_data_multi, epochs=EPOCHS,
                                          steps_per_epoch=steps,
                                          validation_data=val_data_multi,
                                          validation_steps=50)

plot_loss(multi_step_history, 'Multi-Step Training and validation loss')

for x, y in val_data_multi.take(5):
    multi_step_plot(x[0], y[0], multi_step_model.predict(x)[0])

scores = multi_step_model.evaluate(
    x_train_multi,
    y_train_multi,
    verbose=1,
    batch_size=200)
print('MAE: {}'.format(scores[1]))

scores_test = multi_step_model.evaluate(
    x_val_multi, y_val_multi, verbose=1, batch_size=200)
print('MAE: {}'.format(scores[1]))