Add quantum/breast_cancer.py

DIRECTORY.md

@@ -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)

quantum/breast_cancer.py

@@ -0,0 +1,62 @@
+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=False):
+    """returns breast cancer dataset
+
+    Wikipedia reference: https://en.m.wikipedia.org/wiki/Breast_cancer
+
+    >>> breast_cancer(10, 4, 7)
+    24.9
+    """
+    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)
+    }
+
+    if plot_data:
+        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()
+
+    return sample_train, training_input, test_input, class_labels