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@@ -0,0 +1,79 @@ +from pyspark import SparkContext +from pyspark.sql import SQLContext +from pyspark.sql.types import StructType, StructField, DoubleType +from pyspark.ml.classification import GBTClassifier +from pyspark.ml.feature import VectorAssembler + +def save_model(model_string, filename): + with open(filename, "w") as text_file: + text_file.write(model_string) + +if __name__ == '__main__': + sc = SparkContext() + sqlContext = SQLContext(sc) + + schema = StructType([ \ + StructField("label", DoubleType(),True), \ + StructField("f0", DoubleType(),True), \ + StructField("f1", DoubleType(),True), \ + StructField("f2", DoubleType(),True), \ + StructField("f3", DoubleType(), True), \ + StructField("f4", DoubleType(), True), \ + StructField("f5", DoubleType(), True), \ + StructField("f6", DoubleType(), True), \ + StructField("f7", DoubleType(), True), \ + StructField("f8", DoubleType(), True), \ + StructField("f9", DoubleType(), True), \ + StructField("f10", DoubleType(), True), \ + StructField("f11", DoubleType(),True), \ + StructField("f12", DoubleType(),True), \ + StructField("f13", DoubleType(),True), \ + StructField("f14", DoubleType(), True), \ + StructField("f15", DoubleType(), True), \ + StructField("f16", DoubleType(), True), \ + StructField("f17", DoubleType(), True), \ + StructField("f18", DoubleType(), True), \ + StructField("f19", DoubleType(), True), \ + StructField("f20", DoubleType(), True), \ + StructField("f21", DoubleType(),True), \ + StructField("f22", DoubleType(),True), \ + StructField("f23", DoubleType(),True), \ + StructField("f24", DoubleType(), True), \ + StructField("f25", DoubleType(), True), \ + StructField("f26", DoubleType(), True), \ + StructField("f27", DoubleType(), True), \ + ]) + + df = sqlContext.read.format('com.databricks.spark.csv').schema(schema).load("HIGGS_100.csv/part-00000-758749fa-cbbd-473e-b1cd-4d06fd714edd-c000.csv").limit(100) + features_column = 'features' + label_column = 'label' + feature_cols = ['f0', 'f1', 'f2', 'f3','f4','f5', + 'f6','f7','f8','f9','f10', + 'f11','f12','f13','f14','f15', + 'f16','f17','f18','f19','f20', + 'f21', 'f22', 'f23', 'f24', 'f25', + 'f26', 'f27'] + assembler = VectorAssembler(inputCols=feature_cols, outputCol=features_column) + df = assembler.transform(df) + + rf = GBTClassifier(featuresCol=features_column, labelCol=label_column, maxIter=2) + train, test = df.randomSplit([0.7, 0.3], seed = 2018) + rf_model = rf.fit(train) + + # export model as string + model_string = rf_model.toDebugString + rf_model.setThresholds([0.5, 0.5]) + thresholds = rf_model.getThresholds() + thresholds_string = " ".join(str(x) for x in thresholds) + save_model(thresholds_string + '\n' + model_string, 'higgs_spark_model.txt') + + # save model using spark + rf_model.write().overwrite().save('higgs_spark_model') + + # print predictions + predictions = rf_model.transform(test) + predictions.select(label_column, "prediction", 'probability').show() + + # # save test rows as csv for validation + # test = test.drop(features_column) + # test.coalesce(1).write.mode("overwrite").option("header","true").csv("testdata_higgs_spark_model") \ No newline at end of file diff --git a/python/spark-to-scikit/RandomForestClassifierXGBoost/metadata/higgs_scikit_model.txt b/python/spark-to-scikit/RandomForestClassifierXGBoost/metadata/higgs_scikit_model.txt new file mode 100644 index 00000000..089753f0 --- /dev/null +++ b/python/spark-to-scikit/RandomForestClassifierXGBoost/metadata/higgs_scikit_model.txt @@ -0,0 +1,76 @@ +|--- feature_25 <= 1.54 +| |--- feature_26 <= 0.77 +| | |--- feature_18 <= -0.23 +| | | |--- value: [-1.00] +| | |--- feature_18 > -0.23 +| | | |--- feature_3 <= 1.17 +| | | | |--- value: [1.00] +| | | |--- feature_3 > 1.17 +| | | | |--- value: [-1.00] +| |--- feature_26 > 0.77 +| | |--- feature_3 <= 1.98 +| | | |--- feature_23 <= 0.98 +| | | | |--- value: [-1.00] +| | | |--- feature_23 > 0.98 +| | | | |--- feature_7 <= -1.71 +| | | | | |--- value: [0.00] +| | | | |--- feature_7 > -1.71 +| | | | | |--- value: [0.94] +| | |--- feature_3 > 1.98 +| | | |--- feature_6 <= 0.27 +| | | | |--- value: [-1.00] +| | | |--- feature_6 > 0.27 +| | | | |--- value: [1.00] +|--- feature_25 > 1.54 +| |--- feature_17 <= 1.55 +| | |--- feature_2 <= -1.55 +| | | |--- feature_0 <= 1.77 +| | | | |--- value: [1.00] +| | | |--- feature_0 > 1.77 +| | | | |--- value: [-1.00] +| | |--- feature_2 > -1.55 +| | | |--- value: [-1.00] +| |--- feature_17 > 1.55 +| | |--- feature_0 <= 0.53 +| | | |--- value: [-1.00] +| | |--- feature_0 > 0.53 +| | | |--- value: [1.00] + +|--- feature_9 <= 0.34 +| |--- feature_0 <= 0.68 +| | |--- value: [-3.47] +| |--- feature_0 > 0.68 +| | |--- value: [0.53] +|--- feature_9 > 0.34 +| |--- feature_25 <= 1.21 +| | |--- feature_26 <= 0.77 +| | | |--- feature_18 <= -0.23 +| | | | |--- value: [-0.48] +| | | |--- feature_18 > -0.23 +| | | | |--- feature_3 <= 1.17 +| | | | | |--- value: [0.48] +| | | | |--- feature_3 > 1.17 +| | | | | |--- value: [-0.48] +| | |--- feature_26 > 0.77 +| | | |--- feature_7 <= -1.71 +| | | | |--- value: [2.00] +| | | |--- feature_7 > -1.71 +| | | | |--- feature_3 <= 1.98 +| | | | | |--- value: [0.53] +| | | | |--- feature_3 > 1.98 +| | | | | |--- value: [-0.10] +| |--- feature_25 > 1.21 +| | |--- feature_7 <= -1.71 +| | | |--- value: [-2.00] +| | |--- feature_7 > -1.71 +| | | |--- feature_11 <= 0.02 +| | | | |--- feature_25 <= 1.63 +| | | | | |--- value: [0.51] +| | | | |--- feature_25 > 1.63 +| | | | | |--- value: [-0.24] +| | | |--- feature_11 > 0.02 +| | | | |--- feature_13 <= 1.53 +| | | | | |--- value: [-0.48] +| | | | |--- feature_13 > 1.53 +| | | | | |--- value: [0.18] + diff --git a/python/spark-to-scikit/RandomForestClassifierXGBoost/metadata/scikit_trees_visualization/scikit_tree_image_0.png b/python/spark-to-scikit/RandomForestClassifierXGBoost/metadata/scikit_trees_visualization/scikit_tree_image_0.png new file mode 100644 index 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max_depth=None): + if max_depth is None: + max_depth = self.get_max_depth(self.root) + return 2 ** max_depth + + +def separate_trees(tree_file): + tree = '' + tree_contents = [] + tree_list = [] + for line in tree_file: + line = line.strip().rstrip() + if line.find('Tree') != -1: + if tree: + tree_list.append({'Tree': tree, 'Contents': tree_contents}) + tree = line + tree_contents = [] + else: + tree_contents.append(line) + tree_list.append({'Tree': tree, 'Contents': tree_contents}) + return tree_list + +def read_columns(columns_file): + column_name_dict = {} + with open(columns_file) as csvfile: + reader = csv.DictReader(csvfile) + for row in reader: + column_name_dict[row['INDEX']] = row['COLUMN_NAME'] + return column_name_dict + + +def parse_text(trees_file): + with open(trees_file) as treesfile: + threshold = x = [float(i) for i in treesfile.readline().split()] + numFeatures = int(treesfile.readline().split(',')[3].split('=')[1]) + return threshold, numFeatures, separate_trees(treesfile.readlines()) + +###############################################################################################################################################33 + +def add_index_dfs(root, current_index): + if root is None: + return + + root['index'] = current_index['index'] + current_index['index'] += 1 + children = root['children'] + + for child in children: + add_index_dfs(child, current_index) + +def serialize_tree(root, left, right, features, thresholds, values, number_classes): + if root is None: + return + + name = root['name'] + + # if not leaf node + if isinstance(name, str): + feature_name, threshold_string = name.split('<=') + feature, feature_num = feature_name.strip().split('_') + + features.append(int(feature_num)) + thresholds.append(float(threshold_string)) + values.append([0]) # since we don't care about number of samples with belonging to threshold + else: + feature_num = -2 + threshold = -2.0 + class_name = name + value = [class_name] + + features.append(feature_num) + thresholds.append(float(threshold)) + values.append(value) + + + children = root['children'] + + # left child + left_index = -1 + if len(children) > 0: + left_index = children[0]['index'] + left.append(left_index) + + # right child + right_index = -1 + if len(children) > 1: + right_index = children[1]['index'] + right.append(right_index) + + for child in children: + serialize_tree(child, left, right, features, thresholds, values, number_classes) + +def save_to_json(scikit_dict, filename): + with open(filename, 'w') as fp: + json.dump(scikit_dict, fp) + + +def main(): + parser = argparse.ArgumentParser(description='Parse a random forest') + parser.add_argument('--filepath', dest='trees', help='Path to file holding the trees.', required=True) + parser.add_argument('--columns', dest='columns', default=None, + help='Path to csv file holding column index and column name.') + + args = parser.parse_args() + column_name_dict = {} + if args.columns: + column_name_dict = read_columns(args.columns) + threshold_values, num_features, trees = parse_text(args.trees) + tree_list = [] + for index, tree in enumerate(trees): + tree_obj = Tree() + tree_obj.create_tree(tree, column_name_dict) + js_struct = tree_obj.get_js_struct(tree_obj.root) + node_dict = {'tree': [js_struct], 'max_depth': tree_obj.max_depth, 'max_breadth': tree_obj.max_breadth} + tree_list.append(node_dict) + + # Meta Data about model + number_classes = 2 + number_features = num_features + n_outputs = 1 + + max_depth = 0 + for tree in tree_list: + max_depth = max(tree['max_depth'], max_depth) + + # Populate each decision tree + estimators = [] + for tree in tree_list: + left, right, features, threshold, values = [], [], [], [], [] + sampleTree = tree['tree'] + currentIndex = {'index': 0} + add_index_dfs(sampleTree[0], currentIndex) + serialize_tree(sampleTree[0], left, right, features, threshold, values, number_classes) + + nodes = [] + values_nested = [] + for i in range (0, len(features)): + impurity, n_node_samples, weighted_n_node_samples = 0, 0, 0 # not important for prediction + nodes.append([left[i], right[i], features[i], threshold[i], impurity, n_node_samples, weighted_n_node_samples]) + values_nested.append([values[i]]) + + current_dict = { + 'meta': "decision-tree-regression", + 'feature_importances_': [0] * number_features, + 'max_features_': 0, # not sure about it's usage in prediction + 'n_features_': number_features, + 'n_outputs_': n_outputs, + 'tree_': { + 'max_depth': tree['max_depth'], + 'node_count': len(features), + 'nodes': nodes, + 'values': values_nested, + "nodes_dtype": [ # data types of each entry in nodes i.e left, right, features, threshold, ... + "5NnGT$ZJtM1 aV5Nbs4ueQ?p&kT=`_gLfue*ko-u7r!-aDQE literal 0 HcmV?d00001 diff --git a/python/spark-to-scikit/RandomForestClassifierXGBoost/testdata_higgs_spark_model/_SUCCESS b/python/spark-to-scikit/RandomForestClassifierXGBoost/testdata_higgs_spark_model/_SUCCESS new file mode 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