Shantanu's Blog

Corporate Consultant

March 28, 2019

 

Image blur

Here are the basic steps to blur the image.

import cv2
from matplotlib import pyplot as plt

# Import the image and convert to RGB
img = cv2.imread('Penguins.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

kernels = [5, 11, 17, 25, 55]
fig, axs = plt.subplots(nrows = 1, ncols = 5, figsize = (20, 20))
for ind, s in enumerate(kernels):
    img_blurred = cv2.blur(img, ksize = (s, s))
    ax = axs[ind]
    ax.imshow(img_blurred)
    ax.axis('off')
plt.show()




img_0 = cv2.blur(img, ksize = (37, 37))
img_1 = cv2.GaussianBlur(img, ksize = (37, 37), sigmaX = 0) 
img_2 = cv2.medianBlur(img, 37)
img_3 = cv2.bilateralFilter(img, 37, sigmaSpace = 75, sigmaColor =75)
# Plot the images
images = [img_0, img_1, img_2, img_3]
fig, axs = plt.subplots(nrows = 1, ncols = 4, figsize = (20, 20))
for ind, p in enumerate(images):
    ax = axs[ind]
    ax.imshow(p)
    ax.axis('off')
plt.show()



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March 16, 2019

 

Pandas case study 10

How to divide each element with the sum values in the row, except the values on the diagonal?

try:
    from StringIO import StringIO
except ImportError:
    from io import StringIO

mymat = """
 a  5   2    3   2   0   1
 b  2   4    3   2   0   1
 c  3   4    3   2   0   3
 d  2   4    3   2   0   1
 e  0   4    3   2   0   8
 f  1   4    3   2   0   1
"""

u_cols = ['tag', 'a', 'b', 'c', 'd', 'e', 'f']

df = pd.read_csv(StringIO(mymat), sep='\s+', names=u_cols)

The expected output would be:

tag    a b c d e f
a 0.00 0.250000 0.375000 0.250000 0.0 0.125000
b 0.25 0.000000 0.375000 0.250000 0.0 0.125000
c 0.25 0.333333 0.000000 0.166667 0.0 0.250000
d 0.20 0.400000 0.300000 0.000000 0.0 0.100000
e 0.00 0.235294 0.176471 0.117647 0.0 0.470588
f 0.10 0.400000 0.300000 0.200000 0.0 0.000000

## set an index
df = df.set_index('tag')

## Fill Diagonal values with 0
np.fill_diagonal(df.values, 0)

## devide each value by sum of row
v = df.div(df.sum(axis=1), axis=0)

https://stackoverflow.com/questions/55107688/how-to-divide-the-selected-element-with-the-sum-values-in-the-row-except-the-va/55108196#55108196

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March 13, 2019

 

Deep learning explained

Sigmoid function will always return the values between 0 and 1

def sigmoid(x):
  return 1 / (1 + np.exp(-x))

Input can be multiplied by filter values. Bias can be added if required. The sigmoid value is returned which is fed to the next level.

def feedforward(weights, bias, inputs):
    total = np.dot(weights, inputs) + bia
    return sigmoid(total)

You can try with different bias values like 4, 32 and 35

feedforward(np.array([0, 1]), 4, np.array([2,3]))

As we can see from the following example, the sample data is changing and the change is reducing over each epoch.

feedforward(np.array([0, 1]), 0, np.array([2,3]))

feedforward(np.array([0, 1]), 0, np.array([0.9525741268224334,0.9525741268224334]))

feedforward(np.array([0, 1]), 0, np.array([0.7216325609518421,0.7216325609518421]))

feedforward(np.array([0, 1]), 0, np.array([0.6729664167640047,0.6729664167640047]))

feedforward(np.array([0, 1]), 0, np.array([0.6621670709858073,0.6621670709858073]))

feedforward(np.array([0, 1]), 0, np.array([0.6597470227463499,0.6597470227463499]))

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March 10, 2019

 

Naive Bayes explained

In order to understand naive bayes, let's import the wine data that comes with sklearn module. We will save all the columns as dataframe along with the target column. We will split the data into features + Target (X + y) and then split it up into train and test. Now fit the training data to create a model called "gnb" and then try to predict the test data. Since we already know the expected value, we can compare it with prediction outcome and then calculate the accuracy score.

from sklearn import datasets
wine = datasets.load_wine()

df_wine=pd.DataFrame(wine.data, columns=wine.feature_names)
df_wine['target'] = wine.target

X = df_wine.iloc[:, :-1]
y = df_wine.iloc[:, -1]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=109)

gnb = GaussianNB()
gnb.fit(X_train, y_train)
y_pred = gnb.predict(X_test)

metrics.accuracy_score(y_test, y_pred)

As you can see the code is just 10 to 12 lines and it is very readable.

You will need to import the following modules for this exercise.

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn import metrics

Here is another and very popular example of using naive bayes.

# Assigning features and label variables
weather = [
    'Sunny', 'Sunny', 'Overcast', 'Rainy', 'Rainy', 'Rainy', 'Overcast',
    'Sunny', 'Sunny', 'Rainy', 'Sunny', 'Overcast', 'Overcast', 'Rainy'
]
temp = [
    'Hot', 'Hot', 'Hot', 'Mild', 'Cool', 'Cool', 'Cool', 'Mild', 'Cool', 'Mild',
    'Mild', 'Mild', 'Hot', 'Mild'
]

play = [
    'No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'Yes',
    'Yes', 'Yes', 'No'
]

df = pd.DataFrame({"play": play, "temp": temp, "weather": weather})

df_encoded = df.apply(LabelEncoder().fit_transform)

X = df_encoded[["weather", "temp"]]
y = df_encoded[["play"]]

model = GaussianNB()
model.fit(X, y)

predicted = model.predict([[0, 2]])  # 0:Overcast, 2:Mild
print("Predicted Value:", predicted)

All the 3 columns in this dataframe were categorical and as per machine learning rule, we need to change the text to numbers. LabelEncoder class will modify the data and create a new dataframe called "df_encoded" with numbers only columns. You can compare both the dataframes side by side using...

pd.concat([df, df_encoded], axis=1)

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March 09, 2019

 

neural networks using sklearn

sklearn supports neural networks as shown in the following code.

a) The first parameter, hidden_layer_sizes, is used to set the size of the hidden layers, creating three layers of 10 nodes each.

b) The second parameter to MLPClassifier specifies the number of iterations, or the epochs, that you want your neural network to execute. One epoch is a combination of one cycle of feed-forward and back propagation phase.

c) By default the 'relu' activation function is used with 'adam' cost optimizer. You can change these functions using the activation and solver parameters, respectively.

# create dataframe using IRIS database
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ["sepal-length", "sepal-width", "petal-length", "petal-width", "Class"]
irisdata = pd.read_csv(url, names=names)

# Features and target split
X = irisdata.iloc[:, 0:4]
y = irisdata.iloc[:, 4]

# fit data using standard scaler
scaler = StandardScaler()
X = scaler.fit_transform(X)

# target values encoded to numeric
le = preprocessing.LabelEncoder()
y = le.fit_transform(y)

# Train test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, random_state=1, test_size=0.20
)

# Use MLP classifier class
mlp = MLPClassifier(hidden_layer_sizes=(10, 10, 10), max_iter=1000)
mlp.fit(X_train, y_train)
predictions = mlp.predict(X_test)

# Accuracy score is almost 100% only 1 error
confusion_matrix(y_test, predictions)
accuracy_score(y_test, predictions)
classification_report(y_test, predictions)

# Here are the modules imported:

import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import (
    classification_report,
    confusion_matrix,
    accuracy_score,
)

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