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Declarative Machine Learning
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#-------------------------------------------------------------
/**
* Gets sklearn.metrics.classification_report-like output that can be used by DML user.
* y_true: row or column vector, Ground truth (correct) target values.
* y_pred: row or column vector, Estimated targets as returned by a classifier.
* labels: column vector, list of label to include in the report.
*
* PySpark example:
* from sklearn import datasets, neighbors
* from systemml.mllearn import LogisticRegression
* from pyspark.sql import SQLContext
* sqlCtx = SQLContext(sc)
* digits = datasets.load_digits()
* X_digits = digits.data
* y_digits = digits.target + 1
* n_samples = len(X_digits)
* X_train = X_digits[:int(.9 * n_samples)]
* y_train = y_digits[:int(.9 * n_samples)]
* X_test = X_digits[int(.9 * n_samples):]
* y_test = y_digits[int(.9 * n_samples):]
* logistic = LogisticRegression(sqlCtx)
* logistic.fit(X_train, y_train)
* y_predicted = logistic.predict(X_test)
*
* script = """
* classification_report = function(matrix[double] y_true, matrix[double] y_pred, matrix[double] labels) return (string out) {
* num_rows_error_measures = nrow(labels)
* error_measures = matrix(0, rows=num_rows_error_measures, cols=5)
* for(i in 1:num_rows_error_measures) {
* class_i = labels[i,1]
* tp = sum( (y_true == y_pred) * (y_true == class_i) )
* tp_plus_fp = sum( (y_pred == class_i) )
* tp_plus_fn = sum( (y_true == class_i) )
* precision = tp / tp_plus_fp
* recall = tp / tp_plus_fn
* f1Score = 2*precision*recall / (precision+recall)
* error_measures[i,1] = class_i
* error_measures[i,2] = precision
* error_measures[i,3] = recall
* error_measures[i,4] = f1Score
* error_measures[i,5] = tp_plus_fn
* }
* # Added num_true_labels to debug whether the input data was randomized or now, which is common requirement of SGD-style algorithms.
* # Also, helps debug class-skew related problems.
* out = "class \tprecision\trecall \tf1-score\tnum_true_labels\n" + toString(error_measures, decimal=7, sep="\t")
* }
* out = classification_report(y_true, y_pred, seq(1, 10))
* print(out)
* """
* from systemml import MLContext, dml
* ml = MLContext(sc)
* script = dml(script).input(y_true=y_test, y_pred=y_predicted)
* ml.execute(script)
*
* This outputs:
* class precision recall f1-score num_true_labels
* 1.0000000 1.0000000 1.0000000 1.0000000 16.0000000
* 2.0000000 0.9444444 0.8947368 0.9189189 19.0000000
* 3.0000000 1.0000000 1.0000000 1.0000000 17.0000000
* 4.0000000 0.9166667 0.6111111 0.7333333 18.0000000
* 5.0000000 0.9047619 0.9500000 0.9268293 20.0000000
* 6.0000000 0.9000000 1.0000000 0.9473684 18.0000000
* 7.0000000 1.0000000 1.0000000 1.0000000 18.0000000
* 8.0000000 1.0000000 1.0000000 1.0000000 19.0000000
* 9.0000000 0.7272727 0.9411765 0.8205128 17.0000000
* 10.0000000 0.9411765 0.8888889 0.9142857 18.0000000
*
*/
classification_report = function(matrix[double] y_true, matrix[double] y_pred, matrix[double] labels) return (string out) {
num_rows_error_measures = nrow(labels)
error_measures = matrix(0, rows=num_rows_error_measures, cols=5)
for(i in 1:num_rows_error_measures) {
class_i = labels[i,1]
tp = sum( (y_true == y_pred) * (y_true == class_i) )
tp_plus_fp = sum( (y_pred == class_i) )
tp_plus_fn = sum( (y_true == class_i) )
precision = tp / tp_plus_fp
recall = tp / tp_plus_fn
f1Score = 2*precision*recall / (precision+recall)
error_measures[i,1] = class_i
error_measures[i,2] = precision
error_measures[i,3] = recall
error_measures[i,4] = f1Score
error_measures[i,5] = tp_plus_fn
}
# Added num_true_labels to debug whether the input data was randomized or now, which is common requirement of SGD-style algorithms.
# Also, helps debug class-skew related problems.
out = "class \tprecision\trecall \tf1-score\tnum_true_labels\n" + toString(error_measures, decimal=7, sep="\t")
}