path: root/models/failure_prediction/python/lstm_correlation.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_correlation.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1pDIYGV2-FR7QJEhCt9HxlJfeIeqw8xBj

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.
"""

import os
from keras.layers import Activation, Dense, Dropout
import seaborn as sns
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('/gdrive')

"""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(
    "/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/Ellis_FinalTwoConditionwithOR.csv")
df_Ellis

df_Ellis.plot()

# we show here the hist
df_Ellis.hist(bins=100, figsize=(20, 15))
# save_fig("attribute_histogram_plots")
plt.show()

cpu_system_perc = df_Ellis[['ellis-cpu.system_perc']]
cpu_system_perc.rolling(12).mean().plot(
    figsize=(20, 10), linewidth=5, fontsize=20)
plt.xlabel('Timestamp', fontsize=30)

load_avg_1_min = df_Ellis[['ellis-load.avg_1_min']]
load_avg_1_min.rolling(12).mean().plot(
    figsize=(20, 10), linewidth=5, fontsize=20)
plt.xlabel('Timestamp', fontsize=30)

cpu_wait_perc = df_Ellis[['ellis-cpu.wait_perc']]
cpu_wait_perc.rolling(12).mean().plot(
    figsize=(20, 10), linewidth=5, fontsize=20)
plt.xlabel('Year', fontsize=30)

df_dg = pd.concat([cpu_system_perc.rolling(12).mean(), load_avg_1_min.rolling(
    12).mean(), cpu_wait_perc.rolling(12).mean()], axis=1)
df_dg.plot(figsize=(20, 10), linewidth=5, fontsize=20)
plt.xlabel('Year', fontsize=20)

# we establish the corrmartrice
color = sns.color_palette()
sns.set_style('darkgrid')

correaltionMatrice = df_Ellis.corr()
f, ax = plt.subplots(figsize=(20, 10))
sns.heatmap(
    correaltionMatrice,
    cbar=True,
    vmin=0,
    vmax=1,
    square=True,
    annot=True)
plt.show()

df_Ellis.corrwith(df_Ellis['ellis-load.avg_1_min'])

# using multivariate feature

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

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

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)

# 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)

# 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)


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


# 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 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')

# 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)

# 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**"""

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)

#  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)

# 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]))

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]))