public function test_scenario1() { $data = array(array("predictions" => "./data/predictions_c.json", "method" => 0, "prediction" => "a", "confidence" => 0.450471270879), array("predictions" => "./data/predictions_c.json", "method" => 1, "prediction" => "a", "confidence" => 0.552021302649), array("predictions" => "./data/predictions_c.json", "method" => 2, "prediction" => "a", "confidence" => 0.403632421178), array("predictions" => "./data/predictions_r.json", "method" => 0, "prediction" => 1.55555556667, "confidence" => 0.400079152063), array("predictions" => "./data/predictions_r.json", "method" => 1, "prediction" => 1.59376845074, "confidence" => 0.248366474212), array("predictions" => "./data/predictions_r.json", "method" => 2, "prediction" => 1.55555556667, "confidence" => 0.400079152063)); foreach ($data as $item) { print "\nSuccessfully computing predictions combinations\n"; $predictions = json_decode(file_get_contents($item["predictions"])); print "Given I create a MultiVote for the set of predictions in file " . $item["predictions"] . "\n"; $multivote = new MultiVote($predictions); print "When I compute the prediction with confidence using method " . $item["method"] . "\n"; $combined_results = $multivote->combine($item["method"], true); print "And I compute the prediction without confidence using method " . $item["method"] . "\n"; $combined_results_no_confidence = $multivote->combine($item["method"]); if ($multivote->is_regression()) { print "Then the combined prediction is " . $item["prediction"] . "\n"; $this->assertEquals(round($combined_results[0], 6), round($item["prediction"], 6)); print "And the combined prediction without confidence is " . $item["prediction"] . "\n"; $this->assertEquals(round($combined_results_no_confidence, 6), round($item["prediction"], 6)); } else { print "Then the combined prediction is " . $item["prediction"] . "\n"; $this->assertEquals($combined_results[0], $item["prediction"]); print "And the combined prediction without confidence is " . $item["prediction"] . "\n"; $this->assertEquals($combined_results_no_confidence, $item["prediction"]); } print "And the confidence for the combined prediction is " . $item["confidence"] . "\n"; $this->assertEquals(round($combined_results[1], 6), round($item["confidence"], 6)); } }
public function test_scenario1() { $data = array(array("predictions" => "./data/predictions_c.json", "method" => 0, "prediction" => "a", "confidence" => 0.450471270879), array("predictions" => "./data/predictions_c.json", "method" => 1, "prediction" => "a", "confidence" => 0.552021302649), array("predictions" => "./data/predictions_c.json", "method" => 2, "prediction" => "a", "confidence" => 0.403632421178), array("predictions" => "./data/predictions_r.json", "method" => 0, "prediction" => 1.55555556667, "confidence" => 0.400079152063), array("predictions" => "./data/predictions_r.json", "method" => 1, "prediction" => 1.59376845074, "confidence" => 0.248366474212), array("predictions" => "./data/predictions_r.json", "method" => 2, "prediction" => 1.55555556667, "confidence" => 0.400079152063)); foreach ($data as $item) { $predictions = json_decode(file_get_contents($item["predictions"])); $multivote = new MultiVote($predictions); $combined_results = $multivote->combine($item["method"], true); $combined_results_no_confidence = $multivote->combine($item["method"]); if ($multivote->is_regression()) { $this->assertEquals(round($combined_results[0], 6), round($item["prediction"], 6)); $this->assertEquals(round($combined_results_no_confidence, 6), round($item["prediction"], 6)); } else { $this->assertEquals($combined_results[0], $item["prediction"]); $this->assertEquals($combined_results_no_confidence, $item["prediction"]); } $this->assertEquals(round($combined_results[1], 6), round($item["confidence"], 6)); } }
function predict($input_data, $by_name = true, $method = MultiVote::PLURALITY_CODE, $with_confidence = false, $add_confidence = false, $add_distribution = false, $add_count = false, $add_median = false, $add_min = false, $add_max = false, $options = null, $missing_strategy = Tree::LAST_PREDICTION, $median = false) { /* Makes a prediction based on the prediction made by every model. The method parameter is a numeric key to the following combination methods in classifications/regressions: 0 - majority vote (plurality)/ average: PLURALITY_CODE 1 - confidence weighted majority vote / error weighted: CONFIDENCE_CODE 2 - probability weighted majority vote / average: PROBABILITY_CODE 3 - threshold filtered vote / doesn't apply: THRESHOLD_CODE */ if (count($this->models_splits) > 1) { $votes = new MultiVote(array()); $models = array(); $api = $this->api; $order = 0; foreach ($this->models_splits as $model_split) { $models = array(); foreach ($model_split as $model_id) { array_push($models, $api::retrieve_resource($model_id, $api::ONLY_MODEL)); } $multi_model = new MultiModel($models, $this->api); $votes_split = $multi_model->generate_votes($input_data, $by_name, $missing_strategy, $add_median || $median, $add_min, $add_max); if ($median) { foreach ($votes_split->predictions as $prediction) { $prediction['prediction'] = $prediction['median']; } } $votes->extend($votes_split->predictions); } #return $votes->combine($method, $with_confidence, $options); } else { # When only one group of models is found you use the # corresponding multimodel to predict $votes_split = $this->multi_model->generate_votes($input_data, $by_name, $missing_strategy, $add_median || $median, $add_min, $add_max); $votes = new MultiVote($votes_split->predictions); if ($median) { $new_predictions = array(); foreach ($votes->predictions as $prediction) { $prediction->prediction = $prediction->median; array_push($new_predictions, $prediction); } $votes->predictions = $new_predictions; } } return $votes->combine($method, $with_confidence, $add_confidence, $add_distribution, $add_count, $add_median, $add_min, $add_max, $options); }
function _predict($input_data, $by_name = true, $method = MultiVote::PLURALITY_CODE, $with_confidence = false, $add_confidence = false, $add_distribution = false, $add_count = false, $add_median = false, $add_unused_fields = false, $add_min = false, $add_max = false, $options = null, $missing_strategy = Tree::LAST_PREDICTION, $median = false) { /* Makes a prediction based on the prediction made by every model. :param input_data: Test data to be used as input :param by_name: Boolean that is set to true if field_names (as alternative to field ids) are used in the input_data dict :param method: numeric key code for the following combination methods in classifications/regressions: 0 - majority vote (plurality)/ average: PLURALITY_CODE 1 - confidence weighted majority vote / error weighted: CONFIDENCE_CODE 2 - probability weighted majority vote / average: PROBABILITY_CODE 3 - threshold filtered vote / doesn't apply: THRESHOLD_CODE The following parameter causes the result to be returned as a list :param add_confidence: Adds confidence to the prediction :param add_distribution: Adds the predicted node's distribution to the prediction :param add_count: Adds the predicted nodes' instances to the prediction :param add_median: Adds the median of the predicted nodes' distribution to the prediction :param add_min: Boolean, if true adds the minimum value in the prediction's distribution (for regressions only) :param add_max: Boolean, if true adds the maximum value in the prediction's distribution (for regressions only) :param add_unused_fields: Boolean, if true adds the information about the fields in the input_data that are not being used in the model as predictors. :param options: Options to be used in threshold filtered votes. :param missing_strategy: numeric key for the individual model's prediction method. See the model predict method. :param median: Uses the median of each individual model's predicted node as individual prediction for the specified combination method. */ if (count($this->models_splits) > 1) { $votes = new MultiVote(array()); $models = array(); $api = $this->api; $order = 0; foreach ($this->models_splits as $model_split) { $models = array(); foreach ($model_split as $model_id) { array_push($models, $api::retrieve_resource($model_id, $api::ONLY_MODEL)); } $multi_model = new MultiModel($models, $this->api); $votes_split = $multi_model->generate_votes($input_data, $by_name, $missing_strategy, $add_median || $median, $add_min, $add_max, $add_unused_fields); if ($median) { foreach ($votes_split->predictions as $prediction) { $prediction['prediction'] = $prediction['median']; } } $votes->extend($votes_split->predictions); } #return $votes->combine($method, $with_confidence, $options); } else { # When only one group of models is found you use the # corresponding multimodel to predict $votes_split = $this->multi_model->generate_votes($input_data, $by_name, $missing_strategy, $add_median || $median, $add_min, $add_max, $add_unused_fields); $votes = new MultiVote($votes_split->predictions); if ($median) { $new_predictions = array(); foreach ($votes->predictions as $prediction) { $prediction->prediction = $prediction->median; array_push($new_predictions, $prediction); } $votes->predictions = $new_predictions; } } $result = $votes->combine($method, $with_confidence, $add_confidence, $add_distribution, $add_count, $add_median, $add_min, $add_max, $options); if ($add_unused_fields) { $unused_fields = array_unique(array_keys($input_data)); foreach ($votes->predictions as $index => $prediction) { $unused_fields = array_intersect($unused_fields, array_unique($prediction->unused_fields)); } if (!is_array($result)) { $result = array("prediction" => $result); } $result['unused_fields'] = $unused_fields; } return $result; }