Data Visualization in Python with matplotlib, Seaborn, and Bokeh

 Визуализация данных — важный аспект всех приложений ИИ и машинного обучения. Вы можете получить ключевое представление о своих данных с помощью различных графических представлений. В этом уроке мы поговорим о нескольких вариантах визуализации данных в Python. Мы будем использовать набор данных MNIST и библиотеку Tensorflow для обработки чисел и обработки данных. Чтобы проиллюстрировать различные методы создания различных типов графиков, мы будем использовать графические библиотеки Python, а именно matplotlib, Seaborn и Bokeh.

(.env) boris@boris-All-Series:~/PYTHON/SEABORN$ cat seabornPlottting1.py

from tensorflow.keras.datasets import mnist

from tensorflow import dtypes, tensordot

from tensorflow import convert_to_tensor, linalg, transpose

import numpy as np

import matplotlib.pyplot as plt

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

 

# Load dataset

(x_train, train_labels), (_, _) = mnist.load_data()

# Choose only the digits 0, 1, 2

total_classes = 3

ind = np.where(train_labels < total_classes)

x_train, train_labels = x_train[ind], train_labels[ind]

# Verify the shape of training data

total_examples, img_length, img_width = x_train.shape

print('Training data has ', total_examples, 'images')

print('Each image is of size ', img_length, 'x', img_width)

 

# Convert the dataset into a 2D array of shape 18623 x 784

x = convert_to_tensor(np.reshape(x_train, (x_train.shape[0], -1)),

                      dtype=dtypes.float32)

# Eigen-decomposition from a 784 x 784 matrix

eigenvalues, eigenvectors = linalg.eigh(tensordot(transpose(x), x, axes=1))

# Print the three largest eigenvalues

print('3 largest eigenvalues: ', eigenvalues[-3:])

# Project the data to eigenvectors

x_pca = tensordot(x, eigenvectors, axes=1)

 

# Create the plot

fig, ax = plt.subplots(figsize=(12, 8))

scatter = ax.scatter(x_pca[:, -1], x_pca[:, -2], c=train_labels, s=5)

legend_plt = ax.legend(*scatter.legend_elements(),

                       loc="lower left", title="Digits")

ax.add_artist(legend_plt)

plt.title('First Two Dimensions of Projected Data After Applying PCA')

plt.show()

(.env) boris@boris-All-Series:~/PYTHON/SEABORN$ cat seabornPlottting2.py

from tensorflow.keras.datasets import mnist

from tensorflow.keras import utils

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Reshape

from tensorflow import dtypes, tensordot

from tensorflow import convert_to_tensor, linalg, transpose

import numpy as np

import pandas as pd

from bokeh.plotting import figure, show

from bokeh.layouts import row

 

# Load dataset

(x_train, train_labels), (_, _) = mnist.load_data()

# Choose only the digits 0, 1, 2

total_classes = 3

ind = np.where(train_labels < total_classes)

x_train, train_labels = x_train[ind], train_labels[ind]

# Verify the shape of training data

total_examples, img_length, img_width = x_train.shape

print('Training data has ', total_examples, 'images')

print('Each image is of size ', img_length, 'x', img_width)

 

 

# Convert the dataset into a 2D array of shape 18623 x 784

x = convert_to_tensor(np.reshape(x_train, (x_train.shape[0], -1)),

                      dtype=dtypes.float32)

# Eigen-decomposition from a 784 x 784 matrix

eigenvalues, eigenvectors = linalg.eigh(tensordot(transpose(x), x, axes=1))

# Print the three largest eigenvalues

print('3 largest eigenvalues: ', eigenvalues[-3:])

# Project the data to eigenvectors

x_pca = tensordot(x, eigenvectors, axes=1)

 

 

# Prepare for classifier network

epochs = 10

y_train = utils.to_categorical(train_labels)

input_dim = img_length*img_width

# Create a Sequential model

model = Sequential()

# First layer for reshaping input images from 2D to 1D

model.add(Reshape((input_dim, ), input_shape=(img_length, img_width)))

# Dense layer of 8 neurons

model.add(Dense(8, activation='relu'))

# Output layer

model.add(Dense(total_classes, activation='softmax'))

# Compile model

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

history = model.fit(x_train, y_train, validation_split=0.33, epochs=epochs, batch_size=10, verbose=0)

 

 

# Prepare pandas DataFrame

df_history = pd.DataFrame(history.history)

print(df_history)

 

 

# Create scatter plot in Bokeh

colormap = {0: "red", 1:"green", 2:"blue"}

my_scatter = figure(title="First Two Dimensions of Projected Data After Applying PCA",

                    x_axis_label="Dimension 1",

                    y_axis_label="Dimension 2",

                    width=500, height=400)

for digit in [0, 1, 2]:

    selection = x_pca[train_labels == digit]

    my_scatter.scatter(selection[:,-1].numpy(), selection[:,-2].numpy(),

                       color=colormap[digit], size=5, alpha=0.5,

                       legend_label="Digit "+str(digit))

my_scatter.legend.click_policy = "hide"

 

 

# Plot accuracy in Bokeh

p = figure(title="Training and validation accuracy",

           x_axis_label="Epochs", y_axis_label="Accuracy",

           width=500, height=400)

epochs_array = np.arange(epochs)

p.line(epochs_array, df_history['accuracy'], legend_label="Training",

       color="blue", line_width=2)

p.line(epochs_array, df_history['val_accuracy'], legend_label="Validation",

       color="green")

p.legend.click_policy = "hide"

p.legend.location = 'bottom_right'

 

show(row(my_scatter, p))

REFERENCES

https://machinelearningmastery.com/data-visualization-in-python-with-matplotlib-seaborn-and-bokeh/