| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| # |
| #------------------------------------------------------------- |
| |
| |
| /** |
| * 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 systemds.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 systemds 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") |
| } |
