added ttest and correlation with class

Author: Tomasz Latkowski

.gitattributes

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+.tsv filter=lfs diff=lfs merge=lfs -text

data/autism.tsv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b3b7d953d54f6bd08f9860347df05bbacfcccca254400cf4711b2be30e1cde71
+size 95496413

selection.py

@@ -2,9 +2,47 @@
 
 
 def fisher(data, num_instances: list, top_k=10):
+    """
+    Performs Fisher feature selection method according to the following formula:
+    D(f) = (m1(f) - m2(f) / (std1(f) - std2(f))
+
+    :param data:
+    :param num_instances:
+    :param top_k:
+    :return: the list of most significant features.
+    """
+    assert len(num_instances) == 2, "Fisher selection method can be performed for two-class problems."
     class1, class2 = tf.split(data, num_instances)
     mean1, std1 = tf.nn.moments(class1, axes=0)
     mean2, std2 = tf.nn.moments(class2, axes=0)
     fisher_coeffs = (mean1 - mean2) / (std1 + std2)
     return tf.nn.top_k(fisher_coeffs, k=top_k)
 
+
+def feature_correlation_with_class(data, num_instances: list, top_k=10):
+    """
+    Makes feature correlation with class selection according to the following formula:
+    D(f) = [(m1(f) - m(f))^2 + (m2(f) - m(f))^2] / 2*sigma(f)^2
+    :return: the list of most significant features.
+    """
+    data = tf.convert_to_tensor(data)
+    class1, class2 = tf.split(data, num_instances)
+    mean1, std1 = tf.nn.moments(class1, axes=0)
+    mean2, std2 = tf.nn.moments(class2, axes=0)
+    mean, std = tf.nn.moments(data, axes=0)
+    corr_coeffs = (tf.square(mean1 - mean) + tf.square(mean2 - mean)) / 2*tf.square(std) # FIXME sth is wrong
+    return tf.nn.top_k(corr_coeffs, k=top_k)
+
+
+def t_test(data, num_instances: list, top_k=10):
+    """
+    Makes feature correlation with class selection according to the following formula:
+    D(f) = [(m1(f) - m(f))^2 + (m2(f) - m(f))^2] / 2*sigma(f)^2
+    :return: the list of most significant features.
+    """
+    class1, class2 = tf.split(data, num_instances)
+    mean1, std1 = tf.nn.moments(class1, axes=0)
+    mean2, std2 = tf.nn.moments(class2, axes=0)
+    t_test_coeffs = (mean1 - mean2) / tf.sqrt(tf.square(std1)/num_instances[0] + tf.square(std2) / num_instances[1])
+    return tf.nn.top_k(t_test_coeffs, k=top_k)
+

statistics.py

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+
+
+def pearson_correlation():
+    pass
+
+
+def f_test():
+    pass