--- /dev/null
+# 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]))
Code Review / thoth.git / blobdiff