Best Python libraries for Machine Learning
Machine Learning, as the name suggests, is the science of programming a computer by which they are able to learn from different kinds of data. A more general definition given by Arthur Samuel is – “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” They are typically used to solve various types of life problems.
In the older days, people used to perform Machine Learning tasks by manually coding all the algorithms and mathematical and statistical formula. This made the process time consuming, tedious and inefficient. But in the modern days, it is become very much easy and efficient compared to the olden days by various python libraries, frameworks, and modules. Today, Python is one of the most popular programming languages for this task and it has replaced many languages in the industry, one of the reason is its vast collection of libraries. Python libraries that used in Machine Learning are:
- Numpy
- Scipy
- Scikit-learn
- Theano
- TensorFlow
- Keras
- PyTorch
- Pandas
- Matplotlib
Numpy
NumPy is a very popular python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions. It is very useful for fundamental scientific computations in Machine Learning. It is particularly useful for linear algebra, Fourier transform, and random number capabilities. High-end libraries like TensorFlow uses NumPy internally for manipulation of Tensors.
# Python program using NumPy # for some basic mathematical # operations import numpy as np # Creating two arrays of rank 2 x = np.array([[1, 2], [3, 4]]) y = np.array([[5, 6], [7, 8]]) # Creating two arrays of rank 1 v = np.array([9, 10]) w = np.array([11, 12]) # Inner product of vectors print(np.dot(v, w), "n") # Matrix and Vector product print(np.dot(x, v), "n") # Matrix and matrix product print(np.dot(x, y)) |
Output:
219 [29 67] [[19 22] [43 50]]
SciPy
SciPy is a very popular library among Machine Learning enthusiasts as it contains different modules for optimization, linear algebra, integration and statistics. There is a difference between the SciPy library and the SciPy stack. The SciPy is one of the core packages that make up the SciPy stack. SciPy is also very useful for image manipulation.
# Python script using Scipy # for image manipulation from scipy.misc import imread, imsave, imresize # Read a JPEG image into a numpy array img = imread('D:/Programs / cat.jpg') # path of the image print(img.dtype, img.shape) # Tinting the image img_tint = img * [1, 0.45, 0.3] # Saving the tinted image imsave('D:/Programs / cat_tinted.jpg', img_tint) # Resizing the tinted image to be 300 x 300 pixels img_tint_resize = imresize(img_tint, (300, 300)) # Saving the resized tinted image imsave('D:/Programs / cat_tinted_resized.jpg', img_tint_resize) |
Original image:
Tinted image:
Resized tinted image:
Scikit-learn
Skikit-learn is one of the most popular ML libraries for classical ML algorithms. It is built on top of two basic Python libraries, viz., NumPy and SciPy. Scikit-learn supports most of the supervised and unsupervised learning algorithms. Scikit-learn can also be used for data-mining and data-analysis, which makes it a great tool who is starting out with ML.
# Python script using Scikit-learn # for Decision Tree Clasifier # Sample Decision Tree Classifier from sklearn import datasets from sklearn import metrics from sklearn.tree import DecisionTreeClassifier # load the iris datasets dataset = datasets.load_iris() # fit a CART model to the data model = DecisionTreeClassifier() model.fit(dataset.data, dataset.target) print(model) # make predictions expected = dataset.target predicted = model.predict(dataset.data) # summarize the fit of the model print(metrics.classification_report(expected, predicted)) print(metrics.confusion_matrix(expected, predicted)) |
Output:
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
precision recall f1-score support
0 1.00 1.00 1.00 50
1 1.00 1.00 1.00 50
2 1.00 1.00 1.00 50
micro avg 1.00 1.00 1.00 150
macro avg 1.00 1.00 1.00 150
weighted avg 1.00 1.00 1.00 150
[[50 0 0]
[ 0 50 0]
[ 0 0 50]]
Theano
We all know that Machine Learning is basically mathematics and statistics. Theano is a popular python library that is used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays in an efficient manner. It is achieved by optimizing the utilization of CPU and GPU. It is extensively used for unit-testing and self-verification to detect and diagnose different types of errors. Theano is a very powerful library that has been used in large-scale computationally intensive scientific projects for a long time but is simple and approachable enough to be used by individuals for their own projects.
# Python program using Theano # for computing a Logistic # Function import theano import theano.tensor as T x = T.dmatrix('x') s = 1 / (1 + T.exp(-x)) logistic = theano.function([x], s) logistic([[0, 1], [-1, -2]]) |
Output:
array([[0.5, 0.73105858],
[0.26894142, 0.11920292]])
TensorFlow
TensorFlow is a very popular open-source library for high performance numerical computation developed by the Google Brain team in Google. As the name suggests, Tensorflow is a framework that involves defining and running computations involving tensors. It can train and run deep neural networks that can be used to develop several AI applications. TensorFlow is widely used in the field of deep learning research and application.
# Python program using TensorFlow # for multiplying two arrays # import `tensorflow` import tensorflow as tf # Initialize two constants x1 = tf.constant([1, 2, 3, 4]) x2 = tf.constant([5, 6, 7, 8]) # Multiply result = tf.multiply(x1, x2) # Initialize the Session sess = tf.Session() # Print the result print(sess.run(result)) # Close the session sess.close() |
Output:
[ 5 12 21 32]
Keras
Keras is a very popular Machine Learning library for Python. It is a high-level neural networks API capable of running on top of TensorFlow, CNTK, or Theano. It can run seamlessly on both CPU and GPU. Keras makes it really for ML beginners to build and design a Neural Network. One of the best thing about Keras is that it allows for easy and fast prototyping.
PyTorch
PyTorch is a popular open-source Machine Learning library for Python based on Torch, which is an open-source Machine Learning library which is implemented in C with a wrapper in Lua. It has an extensive choice of tools and libraries that supports on Computer Vision, Natural Language Processing(NLP) and many more ML programs. It allows developers to perform computations on Tensors with GPU acceleration and also helps in creating computational graphs.
# Python program using PyTorch # for defining tensors fit a # two-layer network to random # data and calculating the loss import torch dtype = torch.floatdevice = torch.device("cpu") # device = torch.device("cuda:0") Uncomment this to run on GPU # N is batch size; D_in is input dimension; # H is hidden dimension; D_out is output dimension. N, D_in, H, D_out = 64, 1000, 100, 10 # Create random input and output data x = torch.randn(N, D_in, device = device, dtype = dtype) y = torch.randn(N, D_out, device = device, dtype = dtype) # Randomly initialize weights w1 = torch.randn(D_in, H, device = device, dtype = dtype) w2 = torch.randn(H, D_out, device = device, dtype = dtype) learning_rate = 1e-6for t in range(500): # Forward pass: compute predicted y h = x.mm(w1) h_relu = h.clamp(min = 0) y_pred = h_relu.mm(w2) # Compute and print loss loss = (y_pred - y).pow(2).sum().item() print(t, loss) # Backprop to compute gradients of w1 and w2 with respect to loss grad_y_pred = 2.0 * (y_pred - y) grad_w2 = h_relu.t().mm(grad_y_pred) grad_h_relu = grad_y_pred.mm(w2.t()) grad_h = grad_h_relu.clone() grad_h[h < 0] = 0 grad_w1 = x.t().mm(grad_h) # Update weights using gradient descent w1 -= learning_rate * grad_w1 w2 -= learning_rate * grad_w2 |
Output:
0 47168344.0 1 46385584.0 2 43153576.0 ... ... ... 497 3.987660602433607e-05 498 3.945609932998195e-05 499 3.897604619851336e-05
Pandas
Pandas is a popular Python library for data analysis. It is not directly related to Machine Learning. As we know that the dataset must be prepared before training. In this case, Pandas comes handy as it was developed specifically for data extraction and preparation. It provides high-level data structures and wide variety tools for data analysis. It provides many inbuilt methods for groping, combining and filtering data.
# Python program using Pandas for # arranging a given set of data # into a table # importing pandas as pd import pandas as pd data = {"country": ["Brazil", "Russia", "India", "China", "South Africa"], "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"], "area": [8.516, 17.10, 3.286, 9.597, 1.221], "population": [200.4, 143.5, 1252, 1357, 52.98] } data_table = pd.DataFrame(data) print(data_table) |
Output:
Matplotlib
Matpoltlib is a very popular Python library for data visualization. Like Pandas, it is not directly related to Machine Learning. It particularly comes in handy when a programmer wants to visualize the patterns in the data. It is a 2D plotting library used for creating 2D graphs and plots. A module named pyplot makes it easy for programmers for plotting as it provides features to control line styles, font properties, formatting axes, etc. It provides various kinds of graphs and plots for data visualization, viz., histogram, error charts, bar chats, etc,
# Python program using Matplotib # for forming a linear plot # importing the necessary packages and modules import matplotlib.pyplot as plt import numpy as np # Prepare the data x = np.linspace(0, 10, 100) # Plot the data plt.plot(x, x, label ='linear') # Add a legend plt.legend() # Show the plot plt.show() |
Output:
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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