Mastering Deep Neural Networks for Predictive Analytics in Python
A Comprehensive Guide to Building, Training, and Evaluating Neural Models for Time-Series Forecasting
Deep learning has revolutionized the field of predictive analytics, offering unprecedented accuracy in interpreting complex patterns and forecasting future trends. This article delves into the intricate process of constructing and training deep neural networks (DNNs) using Python, a journey that begins with the foundational steps of importing essential libraries and understanding their roles. Libraries like Pandas, Matplotlib, Seaborn, and TensorFlow form the backbone of our analytical toolkit, allowing us to manipulate large datasets, visualize trends, and harness the power of neural networks for precise predictions.
The journey of building a predictive model is methodical and nuanced, encompassing various stages from data preprocessing and feature engineering to model architecture design and hyperparameter tuning. The article guides you through each step, illustrating the process of splitting the dataset into training, validation, and test sets, an essential practice for evaluating the model’s performance. It also sheds light on the importance of choosing the right metrics, like Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE), for an accurate assessment of the model’s predictive capabilities. Through detailed explanations and code snippets, the article makes the complex world of deep learning accessible and practical for data scientists and enthusiasts.
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import numpy as np
from tqdm import tqdm
import dask.dataframe as dd
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.layers.recurrent import LSTM
from keras.layers import Dense, Conv1D, MaxPool2D, Flatten, Dropout
from keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam, SGD, Nadam
from time import time
from livelossplot import PlotLossesKeras
from keras.layers.advanced_activations import LeakyReLU, PReLU
import tensorflow as tf
from keras.utils.training_utils import multi_gpu_model
from tensorflow.python.client import device_lib
from sklearn.preprocessing import StandardScaler
from livelossplot import PlotLossesKeras
from keijzer import *
%matplotlib inline
%config InlineBackend.print_figure_kwargs={'facecolor' : "w"} # Make sure the axis background of plots is white, this is usefull for the black theme in JupyterLab
sns.set()
This code imports various libraries tools for data analysis, visualization, machine learning, deep learning. These include pas, matplotlib, seaborn, numpy, tqdm, dask, scikit-learn, Keras, TensorFlow. It also sets up utilities for preprocessing data, monitoring training, configuring neural networks. The code is also prepared for multi-GPU support enables a white background for visualizations. Extra utilities may be included through a custom module.
path = _
path = path[:-10] # removes '\\notebooks' from the path string
df = pd.read_csv(path+"\\data\\house_data_processed.csv", delimiter='\t', parse_dates=['datetime'])
df = df.set_index(['datetime'])
magnitude = 1 # Take this from the 1. EDA & Feauture engineering notebook. It's the factor where gasPower has been scaled with to the power 10.
The following code snippet carries out several tasks:
It shortens the ‘path’ variable by removing the last 10 characters, which are assumed to correspond with the substring ‘\\notebooks.’
Next, it tries to load a CSV file named house_data_processed.csv from the \\data subdirectory using the modified ‘path.’ The data is loaded into a pas DataFrame, ‘df.’ The CSV file is expected to have tab-delimited data, with a column named ‘datetime’ containing date objects.
The code then sets the ‘datetime’ column as the index for the DataFrame.
The snippet also sets a variable, ‘magnitude,’ to a value of 1.
Although this is not executed in the snippet, there is a comment explaining that the value for ‘magnitude’ should come from a specific section (1. EDA & Feature engineering notebook) where the ‘gasPower’ feature has been scaled by a factor of 10 to some power. This suggests that further adjustments may need to be made based on analysis performed elsewhere.
Date-time Info To Categorize
Some functions are capable of utilizing the Pandas categorical data type, meaning they do not necessitate the conversion of features into a one-hot encoded format.
columns_to_category = ['hour', 'dayofweek', 'season']
data[columns_to_category] = data[columns_to_category].astype('category') # change datetypes to category
This code converts three columns of the DataFrame data to categorical data type. This type is used for data with a limited number of categories. This conversion can improve memory efficiency can be beneficial for certain types of analysis machine learning tasks that treat categorical data differently from continuous data.