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Add quantum/breast_cancer.py #2983

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3b09d12
Add breast cancer detection algorithm
FirePing32 Oct 7, 2020
9743b92
fixup! Format Python code with psf/black push
Oct 7, 2020
fccec22
Add qiskit
FirePing32 Oct 7, 2020
5475279
updating DIRECTORY.md
Oct 7, 2020
3a2aa88
Remove hyphen from filename
FirePing32 Oct 7, 2020
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updating DIRECTORY.md
Oct 7, 2020
02fac3f
Remove qiskit requirement
FirePing32 Oct 7, 2020
3e15438
Add ImportError exception
FirePing32 Oct 7, 2020
2f75396
Remove unused requirement qiskit
FirePing32 Oct 7, 2020
577843f
Adding doctests
FirePing32 Oct 7, 2020
58dc11d
fixup! Format Python code with psf/black push
Oct 7, 2020
fb24067
Update breast_cancer.py
FirePing32 Oct 7, 2020
38b4fe4
Correct Doctest, plot_data, updated typehints
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Oct 7, 2020
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3 changes: 3 additions & 0 deletions DIRECTORY.md
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Expand Up @@ -662,6 +662,9 @@
* [Sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_99/sol1.py)
* [Validate Solutions](https://github.com/TheAlgorithms/Python/blob/master/project_euler/validate_solutions.py)

## Quantum
* [Breast Cancer](https://github.com/TheAlgorithms/Python/blob/master/quantum/breast_cancer.py)

## Scheduling
* [First Come First Served](https://github.com/TheAlgorithms/Python/blob/master/scheduling/first_come_first_served.py)
* [Round Robin](https://github.com/TheAlgorithms/Python/blob/master/scheduling/round_robin.py)
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129 changes: 129 additions & 0 deletions quantum/breast_cancer.py
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import numpy as np
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, StandardScaler


def breast_cancer(training_size, test_size, n, plot_data):
"""returns breast cancer dataset

Wikipedia reference: https://en.m.wikipedia.org/wiki/Breast_cancer
The function return four values: return sample_train, training_input, test_input, class_labels

>>> breast_cancer(10, 4, 7, False)
(array([[-0.80381589, -0.17496736, 0.02153529, ..., -0.1397565 ,
-0.4738706 , -0.21959184],
[-0.19172231, -0.48047638, -0.5502895 , ..., -0.02604959,
-0.2322415 , -0.19774116],
[-0.8333448 , -0.25876499, -0.26384514, ..., -0.32234254,
-0.17787827, 0.01960881],
...,
[-0.88227162, -0.50950835, -0.48471936, ..., 0.06051423,
0.05369423, -0.01157853],
[-0.36471423, -0.4851768 , -0.58003495, ..., -0.00202821,
-0.1576751 , -0.25529689],
[-0.81134279, -0.25945047, -0.54472716, ..., -0.06312866,
-0.14663466, 0.03603051]]), {'A': array([[-0.19172231, -0.48047638, -0.5502895 , 0.62866253, -0.02604959,
-0.2322415 , -0.19774116],
[-0.08931675, -0.69047276, -0.34473474, 0.2430907 , 0.06598203,
-0.43104084, -0.15886876],
[-0.50124222, -0.38404633, -0.66125808, 0.09100724, 0.10701545,
-0.13283072, -0.0307179 ],
[-0.04524656, -0.06299861, -0.5886886 , 0.33959658, -0.09071451,
-0.43135931, 0.02902253],
[-0.36503477, -0.4966686 , -0.20393198, 0.56148734, 0.18131077,
-0.21103257, -0.14178392],
[-0.44318575, -0.59164354, -0.29118452, 0.04301003, -0.18131692,
0.20621245, -0.09727456],
[-0.4146524 , -0.22790637, -0.69929679, 0.32207269, 0.10755013,
-0.33974676, -0.11637961],
[ 0.18070867, -0.79119457, -0.29254301, 0.04961691, -0.14589901,
-0.41185356, -0.01666176],
[-0.05916688, -0.00087508, -0.31287342, 0.35885527, 0.1041368 ,
-0.34113249, 0.08670244],
[ 0.17702071, -0.80782303, -0.06138567, -0.18814197, 0.04569151,
-0.10576475, 0.15044493]]), 'B': array([[-0.80381589, -0.17496736, 0.02153529, -0.07187231, -0.1397565 ,
-0.4738706 , -0.21959184],
[-0.8333448 , -0.25876499, -0.26384514, 0.1756967 , -0.32234254,
-0.17787827, 0.01960881],
[-0.70730804, -0.29054463, -0.44993423, 0.03257197, -0.10219939,
-0.25074147, -0.08352498],
[-0.79008762, -0.3313085 , -0.28353916, 0.19137684, -0.42320134,
-0.036884 , -0.24138241],
[-0.67599686, -0.50282222, -0.41082298, 0.05409269, 0.08031647,
-0.19597253, -0.10733675],
[-0.67547425, -0.15140441, -0.52477832, 0.02062338, -0.10116737,
-0.13453581, -0.07837964],
[-0.79041979, -0.352277 , -0.39978347, 0.24391654, -0.31451085,
0.11470016, -0.1341472 ],
[-0.78150554, -0.41353571, -0.48452891, -0.05467579, 0.03033078,
-0.32269787, -0.06426137],
[-0.76212084, -0.40881569, -0.48040504, 0.1081265 , 0.02596033,
-0.20427163, 0.09202506],
[-0.74304803, -0.36890094, -0.41951978, 0.2119361 , -0.17708908,
-0.08143223, -0.20604129]])}, {'A': array([[-0.10389794, 0.17924751, -0.67583766, 0.55657289, 0.11036026,
-0.13329861, 0.10039272],
[-0.53086407, -0.4573074 , -0.17817215, 0.40169334, -0.08631139,
-0.27384232, -0.31916126],
[-0.21040012, -0.42492679, -0.16556034, -0.00830156, -0.28627151,
0.05469112, -0.08206313],
[-0.02115007, -0.46893251, -0.14647214, 0.31570857, 0.03058135,
-0.5329338 , 0.28794908]]), 'B': array([[-0.82652554, -0.4336021 , -0.28307103, 0.32561542, -0.15712862,
-0.23854447, -0.08516069],
[-0.73291597, -0.5064755 , -0.35806246, 0.53985827, -0.15899323,
-0.2870263 , -0.15924347],
[-0.90409716, -0.00762145, -0.43410622, -0.03133913, -0.50751343,
-0.42602392, 0.28785164],
[-0.7863327 , -0.3957758 , -0.37520181, 0.27611366, -0.14239159,
-0.18478963, 0.01562388]])}, ['A', 'B'])
"""
class_labels = [r"A", r"B"]
data, target = datasets.load_breast_cancer(return_X_y=True)
sample_train, sample_test, label_train, label_test = train_test_split(
data, target, test_size=0.3, random_state=12
)

# Now we standardize for gaussian around 0 with unit variance
std_scale = StandardScaler().fit(sample_train)
sample_train = std_scale.transform(sample_train)
sample_test = std_scale.transform(sample_test)

# Now reduce number of features to number of qubits
pca = PCA(n_components=n).fit(sample_train)
sample_train = pca.transform(sample_train)
sample_test = pca.transform(sample_test)

# Scale to the range (-1,+1)
samples = np.append(sample_train, sample_test, axis=0)
minmax_scale = MinMaxScaler((-1, 1)).fit(samples)
sample_train = minmax_scale.transform(sample_train)
sample_test = minmax_scale.transform(sample_test)

# Pick training size number of samples from each distro
training_input = {
key: (sample_train[label_train == k, :])[:training_size]
for k, key in enumerate(class_labels)
}
test_input = {
key: (sample_test[label_test == k, :])[:test_size]
for k, key in enumerate(class_labels)
}

return sample_train, training_input, test_input, class_labels

# plot_data can be set to true to plot graph of array output

if plot_data:
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Algorithmic functions should not print() or plot as discussed in CONTRIBUTING.md. So this function should return values an allow the caller to catch the exception, print(), and/or plot.

try:
import matplotlib.pyplot as plt
except ImportError as e:
print(e)
for k in range(0, 2):
plt.scatter(
sample_train[label_train == k, 0][:training_size],
sample_train[label_train == k, 1][:training_size],
)

plt.title("PCA dim. reduced Breast cancer dataset")
plt.show()