minte9
LearnRemember   MLEARNING





Make datasets

Generate synthetic datasets for machine learning tasks. 
 
""" The make_* functions in the sklearn.datasets module are used 
to generate synthetic datasets for machine learning tasks.

Make blobs generates isotropic Gaussian blobs for clustering tasks. 
Make classification generates a random n-class classification problem.
Make regression generates a random regression problem.
"""

import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.datasets import make_classification
from sklearn.datasets import make_regression

# Make blob
features1, target1 = make_blobs(
    n_samples = 100,
    n_features = 2,
    centers = 3, # three target classes
    cluster_std = 0.5,
    shuffle = True,
    random_state = 1
)

# Make classification
features2, target2 = make_classification(
    n_samples = 100,
    n_features = 2,
    n_informative = 2,
    n_redundant = 0,
    n_classes = 2,
    weights = [.25, .75],
    random_state = 1
)

# Make regression
features3, target3, coef3 = make_regression(
    n_samples = 100,
    n_features = 3,
    n_informative = 3,
    n_targets = 1,
    noise = 0,
    coef = True,
    random_state = 1
)

# Plot blobs
plt.scatter(features1[:, 0], features1[:, 1], c=target1)
plt.title('Make blob - Simultated dataset')
plt.show()
plt.scatter(features2[:, 0], features2[:, 1], c=target2)
plt.title('Make classification - Simultated dataset')
plt.show()
plt.scatter(features3[:, 0], features3[:, 1], c=target3)
plt.title('Make regression - Simultated dataset')
plt.show()

print("Blob / Features[0:3]:\n", features1[0:3])
print("Target[:10]:", target1[:10], '\n')
print("Classification / Features[0:3]:\n", features2[0:3])
print("Target[:10]:", target2[:10], '\n')
print("Regression / Features[0:3]:\n", features3[0:3])
print("Target[:10]:\n", target3[:10], '\n')

"""
    Blob / Features[0:3]:
     [[ -1.22685609   3.25572052]
      [ -9.57463218  -4.38310652]
      [-10.71976941  -4.20558148]]
    Target:[:10]: [0 1 1 1 2 2 2 1 0 0] 

    Classification / Features[0:3]:
     [[ 1.30022717 -0.7856539 ]
      [ 1.44184425 -0.56008554]
      [-0.84792445 -1.36621324]]
    Target:[:10]: [1 1 0 0 0 1 1 1 0 1]

    Regression / Features[0:3]:
     [[ 1.29322588 -0.61736206 -0.11044703]
      [-2.793085    0.36633201  1.93752881]
      [ 0.80186103 -0.18656977  0.0465673 ]]
    Target[:10]:
      [ -10.37865986   25.5124503    19.67705609  149.50205427 -121.65210879
         90.29412996  214.01379719  224.74157328  -73.17331138 -195.62776209] 
"""

Survey Simulation

Suppose, we have a survey among the employees of a company. 
 
""" Regression Simulated Dataset (experience / salary)

Simulate the data for building a regression model.
Suppose, we have a survey among the employees of a company.

As a developer, often you have no access to survey data.
You need to simulate the data for building the regression model.
"""

import matplotlib.pyplot as plt
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd

# Sample dataset
X, y = make_regression(
    n_samples = 100, 
    n_features = 1, # Employ years of experience
    n_informative = 1, 
    n_targets = 1,  # Employ's salary
    noise = 10, 
    coef = False, 
    random_state = 0
)

# Scale feature and target
X = np.interp(X, (X.min(), X.max()), (0, 20))           
y = np.interp(y, (y.min(), y.max()), (10000, 200000))   

# Fit a linear regression model
reg = LinearRegression().fit(X, y)


# Plot dataset points
plt.scatter(X, y, label='training data')
plt.title('Simultated dataset (Experience / Salary)')

# Plot the regression line 
x_line = np.linspace(np.min(X), np.max(X), 100)
y_line = reg.intercept_ + x_line * reg.coef_[0]

plt.plot(x_line, y_line, color='red', label='prediction')
plt.text(10, 25000, r'y = %0.2f + %0.2f x' % (reg.intercept_, reg.coef_[0]))
plt.xlabel('Years of experience')
plt.ylabel('Salary')
plt.legend()
plt.show()


# Print data frame
df = pd.DataFrame(data={'Experience': X.flatten(), 'Salary': y})
print(df.head(10))
"""
       Experience         Salary
    0    9.096218   95224.004179
    1   14.637429  132619.663538
    2   12.255808  123760.689176
    3    7.215160   98496.528556
    4    6.905628   80966.199869
    5   12.427999  138646.723320
    6    6.534503   62290.952298
    7   12.363590  129242.508929
    8   11.451010  132720.442525
    9    9.295277   93053.397973
"""




References: