Mercurial > repos > bgruening > create_tool_recommendation_model
changeset 6:e94dc7945639 draft default tip
planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 24bab7a797f53fe4bcc668b18ee0326625486164
author | bgruening |
---|---|
date | Sun, 16 Oct 2022 11:52:10 +0000 |
parents | 4f7e6612906b |
children | |
files | create_tool_recommendation_model.xml extract_workflow_connections.py main.py optimise_hyperparameters.py predict_tool_usage.py prepare_data.py test-data/test_tool_usage test-data/test_workflow_connections test-data/test_workflows train_transformer.py transformer_network.py utils.py |
diffstat | 12 files changed, 3726 insertions(+), 1584 deletions(-) [+] |
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--- a/create_tool_recommendation_model.xml Fri May 06 09:05:18 2022 +0000 +++ b/create_tool_recommendation_model.xml Sun Oct 16 11:52:10 2022 +0000 @@ -1,32 +1,32 @@ -<tool id="create_tool_recommendation_model" name="Create a model to recommend tools" version="0.0.4"> +<tool id="create_tool_recommendation_model" name="Create a model to recommend tools" version="0.0.5"> <description>using deep learning</description> <requirements> - <requirement type="package" version="3.9.7">python</requirement> - <requirement type="package" version="2.7.0">tensorflow</requirement> - <requirement type="package" version="2.7.0">keras</requirement> + <requirement type="package" version="3.9.12">python</requirement> + <requirement type="package" version="2.9.1">tensorflow</requirement> + <requirement type="package" version="2.9.0">keras</requirement> <requirement type="package" version="1.0.2">scikit-learn</requirement> + <requirement type="package" version="1.4.2">pandas</requirement> <requirement type="package" version="3.6.0">h5py</requirement> - <requirement type="package" version="1.0.4">csvkit</requirement> - <requirement type="package" version="0.2.5">hyperopt</requirement> </requirements> <version_command>echo "@VERSION@"</version_command> <command detect_errors="aggressive"> <![CDATA[ python '$__tool_directory__/main.py' + --workflow_file '$input_tabular_workflows' --tool_usage_file '$input_tabular_tool_usage' --cutoff_date '$data_parameters.input_cutoff_date' --maximum_path_length '$data_parameters.input_maximum_path_length' - --n_epochs '$training_parameters.n_epochs' - --optimize_n_epochs '$training_parameters.optimize_n_epochs' - --max_evals '$training_parameters.max_evals' - --test_share '$training_parameters.test_share' - --batch_size '$nn_parameters.batch_size' - --units '$nn_parameters.units' - --embedding_size '$nn_parameters.embedding_size' + --te_share '$training_parameters.te_share' + --tr_batch_size '$training_parameters.tr_batch_size' + --te_batch_size '$training_parameters.te_batch_size' + --n_train_iter '$training_parameters.n_train_iter' + --tr_logging_step '$training_parameters.tr_logging_step' + --te_logging_step '$training_parameters.te_logging_step' + --n_heads '$nn_parameters.n_heads' + --n_embed_dim '$nn_parameters.n_embed_dim' + --n_feed_forward_dim '$nn_parameters.n_feed_forward_dim' --dropout '$nn_parameters.dropout' - --spatial_dropout '$nn_parameters.spatial_dropout' - --recurrent_dropout '$nn_parameters.recurrent_dropout' --learning_rate '$nn_parameters.learning_rate' --output_model '$outfile_model' ]]> @@ -34,37 +34,26 @@ <inputs> <param name="input_tabular_workflows" type="data" format="tabular" label="Dataset containing workflows" help="Please provide Galaxy workflows as a tabular file."/> <param name="input_tabular_tool_usage" type="data" format="tabular" label="Dataset containing usage frequencies of tools" help="Please provide tools usage frequencies as a tabular file."/> + <section name="data_parameters" title="Data parameters" expanded="False"> - <param name="input_cutoff_date" type="text" value="2017-12-01" label="Cutoff date" help="Provide a date (in the past) in yyyy-mm-dd format. The earliest date from which usage of tools will be extracted. For example, 2017-12-01 specifies that the usage of tools from this date until the data extraction date is extracted. The usage of tools before this date is not considered."/> - <param name="input_maximum_path_length" type="integer" value="25" label="Maximum number of tools in a tool sequence" help="Provide an integer between 1 and 25. A workflow is divided into unique paths and this number specifies the maximum number of tools a path can have. Paths longer than this number are ignored and are not included in the deep learning training."/> - </section> <section name="training_parameters" title="Training parameters" expanded="False"> - <param name="max_evals" type="integer" value="20" label="Maximum number of evaluations of different configurations of parameters" help="Provide an integer. Different combinations of parameters are sampled and optimized to find the best one. This number specifies the number of different configurations sampled and tested."/> - - <param name="optimize_n_epochs" type="integer" value="5" label="Number of training iterations to optimize the neural network parameters" help="Provide an integer. This number specifies the number of training iterations done for each sampled configuration while optimising the parameters."/> - - <param name="n_epochs" type="integer" value="10" label="Number of training iterations" help="Provide an integer. This specifies the number of deep learning training iterations done after finding the best/optimised configuration of neural network parameters."/> - - <param name="test_share" type="float" value="0.2" label="Share of the test data" help="Provide a real number between 0.0 and 1.0. This set of data is used to look through the prediction accuracy on unseen data after neural network training on an optimised configuration of parameters. It should be set to 0.0 while training for a model to be deployed to production. The minimum value can be 0.0 and maximum value should not be more than 0.5."/> - + <param name="n_train_iter" type="integer" value="20" label="Maximum number of evaluations of different configurations of parameters" help="Provide an integer. Different combinations of parameters are sampled and optimized to find the best one. This number specifies the number of different configurations sampled and tested."/> + <param name="tr_batch_size" type="integer" value="128" label="Training batch size" help="" /> + <param name="te_batch_size" type="integer" value="128" label="Test batch size" help=""/> + <param name="tr_logging_step" type="integer" value="5" label="Training log step" help="" /> + <param name="te_logging_step" type="integer" value="5" label="Test log step" help="" /> + <param name="te_share" type="text" value="0.2" label="Share of the test data" help="Provide a real number between 0.0 and 1.0. This set of data is used to look through the prediction accuracy on unseen data after neural network training on an optimised configuration of parameters."/> </section> - <section name="nn_parameters" title="Neural network parameters" expanded="False"> - <param name="batch_size" type="text" value="32,256" label="Training batch size" help="Provide a comma-separated range to sample the batch size from. The training of the neural network is done using batch learning in this work. The training data is divided into equal batches and for each epoch (a training iteration), all the batches of data are trained one after another. An example: 32,256." /> - <param name="units" type="text" value="32,512" label="Number of hidden recurrent units" help="Provide a comma-separated range to sample the number of hidden recurrent units. A higher value provide stronger neural network model (may lead to overfitting in case of smaller data) and smaller value leads to weaker model (may lead to underfitting in case of larger data). An example: 32,512."/> - - <param name="embedding_size" type="text" value="32,512" label="Embedding vector size" help="Provide a comma-separated range to sample the embedding size for tools. A fixed-size vector is learned for each tool. This number specifies the fixed-size. An example: 32,512."/> - - <param name="dropout" type="text" value="0.0,0.5" label="Dropout between neural network layers" help="Provide a comma-separated range to sample the amount of dropout to be used after neural netwrok layers. The minimum value should be 0.0 and the maximum value should be 1.0. Dropout is used to prevent or minimize overfitting after each neural network layer. An example: 0.0,0.5"/> - - <param name="spatial_dropout" type="text" value="0.0,0.5" label="Dropout for the embedding layer" help="Provide a comma-separated range to sample the amount of dropout to be used after embedding layer. The minimum value should be 0.0 and the maximum value should be 1.0. Dropout is used to prevent or minimize overfitting in the embedding layer. An example: 0.0,0.5"/> - - <param name="recurrent_dropout" type="text" value="0.0,0.5" label="Dropout for recurrent layers" help="Provide a comma-separated range to sample the amount of dropout to be used for the recurrent layers. The minimum value should be 0.0 and the maximum value should be 1.0. Dropout is used to prevent or minimize overfitting in the recurrent layers. An example: 0.0,0.5"/> - - <param name="learning_rate" type="text" value="0.0001,0.1" label="Learning rate" help="Provide a range of positive real numbers to sample the learning rate. Learning rate defines the speed of neural network learning. A higher value will ensure fast learning and smaller value will ensure slower learning. An example: 0.0001,0.1"/> + <section name="nn_parameters" title="Neural network parameters" expanded="False"> + <param name="n_heads" type="integer" value="4" label="Number of heads in Multihead attention network"/> + <param name="n_embed_dim" type="integer" value="128" label="Embedding dimensions"/> + <param name="n_feed_forward_dim" type="text" value="128" label="Number of units for feed forward network" help=""/> + <param name="dropout" type="text" value="0.2" label="Amount of dropout between neural network layers. E.g. 0.2 means 20%" help=""/> + <param name="learning_rate" type="text" value="0.001" label="Learning rate" help=""/> </section> </inputs> <outputs> @@ -72,71 +61,85 @@ </outputs> <tests> <test> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="max_evals" value="1"/> - <param name="batch_size" value="1,2"/> - <param name="optimize_n_epochs" value="1"/> - <param name="n_epochs" value="1"/> - <param name="test_share" value="0.2"/> + <param name="tr_batch_size" value="4"/> + <param name="te_batch_size" value="4"/> + <param name="n_train_iter" value="20"/> + <param name="tr_logging_step" value="10"/> + <param name="te_logging_step" value="10"/> + <param name="te_share" value="0.4"/> <output name="outfile_model"> <assert_contents> - <has_h5_keys keys="best_parameters,class_weights,compatible_tools,data_dictionary,model_config,weight_0,weight_1,weight_2,weight_3,weight_4,weight_5,weight_6,weight_7,weight_8"/> + <has_h5_keys keys="class_weights,compatible_tools,dense_2,dense_3,dropout_2,dropout_3,global_average_pooling1d,input_1,reverse_dict,standard_connections,token_and_position_embedding,top_level_model_weights,transformer_block"/> </assert_contents> </output> </test> <test> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> - <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="max_evals" value="1"/> - <param name="batch_size" value="1,2"/> - <param name="optimize_n_epochs" value="1"/> - <param name="n_epochs" value="1"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> + <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> + <param name="tr_batch_size" value="4"/> + <param name="te_batch_size" value="4"/> + <param name="n_train_iter" value="20"/> + <param name="tr_logging_step" value="10"/> + <param name="te_logging_step" value="10"/> + <param name="te_share" value="0.4"/> + <param name="n_heads" value="2"/> + <param name="n_embed_dim" value="32"/> + <param name="n_feed_forward_dim" value="32"/> + <param name="dropout" value="0.05"/> + <param name="learning_rate" value="0.0001"/> <output name="outfile_model"> <assert_contents> - <has_h5_keys keys="best_parameters,class_weights,compatible_tools,data_dictionary,model_config,weight_0,weight_1,weight_2,weight_3,weight_4,weight_5,weight_6,weight_7,weight_8"/> + <has_h5_keys keys="class_weights,compatible_tools,dense_2,dense_3,dropout_2,dropout_3,global_average_pooling1d,input_1,reverse_dict,standard_connections,token_and_position_embedding,top_level_model_weights,transformer_block"/> </assert_contents> </output> </test> + <test expect_failure="true"> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="test_share" value="0.0"/> + <param name="te_share" value="0.0,0.1"/> </test> <test expect_failure="true"> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> - <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="batch_size" value="1"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> + <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> + <param name="tr_batch_size" value="1,9"/> </test> <test expect_failure="true"> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="units" value="1"/> + <param name="te_batch_size" value="1,1"/> + </test> + <test expect_failure="true"> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> + <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> + <param name="tr_logging_step" value="1,6"/> </test> <test expect_failure="true"> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> - <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="embedding_size" value="1"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> + <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> + <param name="n_embed_dim" value="1,6"/> </test> <test expect_failure="true"> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="dropout" value="0.1"/> + <param name="n_feed_forward_dim" value="1,6"/> </test> <test expect_failure="true"> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> - <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="spatial_dropout" value="0.1"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> + <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> + <param name="dropout" value="0.1,0.2"/> </test> <test expect_failure="true"> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="recurrent_dropout" value="0.1"/> + <param name="n_heads" value="0.1"/> </test> <test expect_failure="true"> - <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> + <param name="input_tabular_workflows" value="test_workflow_connections" ftype="tabular"/> <param name="input_tabular_tool_usage" value="test_tool_usage" ftype="tabular"/> - <param name="learning_rate" value="0.0001"/> + <param name="learning_rate" value="0.0001,0.04"/> </test> </tests> <help> @@ -188,7 +191,7 @@ - "max_evals": The hyperparameters of the neural network are tuned using a Bayesian optimisation approach and multiple configurations are sampled from different ranges of parameters. The number specified in this parameter is the number of configurations of hyperparameters evaluated to optimise them. Higher the number, the longer is the running time of the tool. - "optimize_n_epochs": This number specifies how many iterations would the neural network executes to evaluate each sampled configuration. - "n_epochs": Once the best configuration of hyperparameters has been found, the neural network takes this configuration and runs for "n_epochs" number of times minimising the error to produce a model at the end. - - "test_share": It specifies the size of the test set. For example, if it is 0.5, then the test set is half of the entire data available. It should not be set to more than 0.5. This set is used for evaluating the precision on an unseen set. + - "te_share": It specifies the size of the test set. For example, if it is 0.5, then the test set is half of the entire data available. It should not be set to more than 0.5. This set is used for evaluating the precision on an unseen set. 3. Neural network parameters: - "batch_size": The training of the neural network is done using batch learning in this work. The training data is divided into equal batches and for each epoch (a training iteration), all batches of data are trained one after another. A higher or lower value can unsettle the training. Therefore, this parameter should be optimised.
--- a/extract_workflow_connections.py Fri May 06 09:05:18 2022 +0000 +++ b/extract_workflow_connections.py Sun Oct 16 11:52:10 2022 +0000 @@ -2,26 +2,26 @@ Extract workflow paths from the tabular file containing input and output tools """ - -import csv import random import utils class ExtractWorkflowConnections: + def __init__(self): """ Init method. """ - def collect_standard_connections(self, row): - published = row[8] - deleted = row[9] - has_errors = row[10] - if published == "t" and deleted == "f" and has_errors == "f": - return True - return False + def process_raw_files(self, wf_path, tool_popu_path, config): + """ + Remove pipe from workflows and popularity tabular files + """ + print("Removing pipe from tabular datasets...") + wf_frame = utils.remove_pipe(wf_path) + tool_popu_frame = utils.remove_pipe(tool_popu_path) + return wf_frame, tool_popu_frame - def read_tabular_file(self, raw_file_path): + def read_tabular_file(self, wf_dataframe, config): """ Read tabular file and extract workflow connections """ @@ -32,17 +32,18 @@ workflow_paths = list() unique_paths = dict() standard_connections = dict() - with open(raw_file_path, "rt") as workflow_connections_file: - workflow_connections = csv.reader(workflow_connections_file, delimiter="\t") - for index, row in enumerate(workflow_connections): - wf_id = str(row[0]) - in_tool = row[3].strip() - out_tool = row[6].strip() + for index, row in wf_dataframe.iterrows(): + row = row.tolist() + row = [str(item).strip() for item in row] + wf_id = str(row[0]) + if row[1] > config["cutoff_date"]: + in_tool = row[3] + out_tool = row[6] if wf_id not in workflows: workflows[wf_id] = list() if out_tool and in_tool and out_tool != in_tool: workflows[wf_id].append((out_tool, in_tool)) - qc = self.collect_standard_connections(row) + qc = self.__collect_standard_connections(row) if qc: i_t = utils.format_tool_id(in_tool) o_t = utils.format_tool_id(out_tool) @@ -54,15 +55,15 @@ wf_ctr = 0 for wf_id in workflows: wf_ctr += 1 - workflow_parents[wf_id] = self.read_workflow(wf_id, workflows[wf_id]) + workflow_parents[wf_id] = self.__read_workflow(wf_id, workflows[wf_id]) for wf_id in workflow_parents: flow_paths = list() parents_graph = workflow_parents[wf_id] - roots, leaves = self.get_roots_leaves(parents_graph) + roots, leaves = self.__get_roots_leaves(parents_graph) for root in roots: for leaf in leaves: - paths = self.find_tool_paths_workflow(parents_graph, root, leaf) + paths = self.__find_tool_paths_workflow(parents_graph, root, leaf) # reverse the paths as they are computed from leaves to roots leaf paths = [tool_path for tool_path in paths] if len(paths) > 0: @@ -84,13 +85,20 @@ unique_paths = list(workflow_paths_dup.split("\n")) unique_paths = list(filter(None, unique_paths)) random.shuffle(unique_paths) + print("unique_paths: {}".format(len(unique_paths))) no_dup_paths = list(set(unique_paths)) + print("no_dup_paths: {}".format(len(no_dup_paths))) + return no_dup_paths, standard_connections - print("Finding compatible next tools...") - compatible_next_tools = self.set_compatible_next_tools(no_dup_paths) - return unique_paths, compatible_next_tools, standard_connections + def __collect_standard_connections(self, row): + published = row[8].strip() + deleted = row[9].strip() + has_errors = row[10].strip() + if published == "t" and deleted == "f" and has_errors == "f": + return True + return False - def set_compatible_next_tools(self, workflow_paths): + def __set_compatible_next_tools(self, workflow_paths): """ Find next tools for each tool """ @@ -109,7 +117,7 @@ next_tools[tool] = ",".join(list(set(next_tools[tool].split(",")))) return next_tools - def read_workflow(self, wf_id, workflow_rows): + def __read_workflow(self, wf_id, workflow_rows): """ Read all connections for a workflow """ @@ -123,7 +131,7 @@ tool_parents[out_tool].append(in_tool) return tool_parents - def get_roots_leaves(self, graph): + def __get_roots_leaves(self, graph): roots = list() leaves = list() all_parents = list() @@ -135,7 +143,7 @@ leaves = list(set(children).difference(set(all_parents))) return roots, leaves - def find_tool_paths_workflow(self, graph, start, end, path=[]): + def __find_tool_paths_workflow(self, graph, start, end, path=[]): path = path + [end] if start == end: return [path] @@ -143,9 +151,7 @@ if end in graph: for node in graph[end]: if node not in path: - new_tools_paths = self.find_tool_paths_workflow( - graph, start, node, path - ) + new_tools_paths = self.__find_tool_paths_workflow(graph, start, node, path) for tool_path in new_tools_paths: path_list.append(tool_path) return path_list
--- a/main.py Fri May 06 09:05:18 2022 +0000 +++ b/main.py Sun Oct 16 11:52:10 2022 +0000 @@ -1,260 +1,36 @@ """ Predict next tools in the Galaxy workflows -using machine learning (recurrent neural network) +using deep learning learning (Transformers) """ - import argparse import time import extract_workflow_connections -import keras.callbacks as callbacks -import numpy as np -import optimise_hyperparameters import prepare_data -import utils - - -class PredictTool: - def __init__(self, num_cpus): - """ Init method. """ - - def find_train_best_network( - self, - network_config, - reverse_dictionary, - train_data, - train_labels, - test_data, - test_labels, - n_epochs, - class_weights, - usage_pred, - standard_connections, - tool_freq, - tool_tr_samples, - ): - """ - Define recurrent neural network and train sequential data - """ - # get tools with lowest representation - lowest_tool_ids = utils.get_lowest_tools(tool_freq) - - print("Start hyperparameter optimisation...") - hyper_opt = optimise_hyperparameters.HyperparameterOptimisation() - best_params, best_model = hyper_opt.train_model( - network_config, - reverse_dictionary, - train_data, - train_labels, - test_data, - test_labels, - tool_tr_samples, - class_weights, - ) - - # define callbacks - early_stopping = callbacks.EarlyStopping( - monitor="loss", - mode="min", - verbose=1, - min_delta=1e-1, - restore_best_weights=True, - ) - predict_callback_test = PredictCallback( - test_data, - test_labels, - reverse_dictionary, - n_epochs, - usage_pred, - standard_connections, - lowest_tool_ids, - ) - - callbacks_list = [predict_callback_test, early_stopping] - batch_size = int(best_params["batch_size"]) - - print("Start training on the best model...") - train_performance = dict() - trained_model = best_model.fit_generator( - utils.balanced_sample_generator( - train_data, - train_labels, - batch_size, - tool_tr_samples, - reverse_dictionary, - ), - steps_per_epoch=len(train_data) // batch_size, - epochs=n_epochs, - callbacks=callbacks_list, - validation_data=(test_data, test_labels), - verbose=2, - shuffle=True, - ) - train_performance["validation_loss"] = np.array( - trained_model.history["val_loss"] - ) - train_performance["precision"] = predict_callback_test.precision - train_performance["usage_weights"] = predict_callback_test.usage_weights - train_performance[ - "published_precision" - ] = predict_callback_test.published_precision - train_performance[ - "lowest_pub_precision" - ] = predict_callback_test.lowest_pub_precision - train_performance[ - "lowest_norm_precision" - ] = predict_callback_test.lowest_norm_precision - train_performance["train_loss"] = np.array(trained_model.history["loss"]) - train_performance["model"] = best_model - train_performance["best_parameters"] = best_params - return train_performance - - -class PredictCallback(callbacks.Callback): - def __init__( - self, - test_data, - test_labels, - reverse_data_dictionary, - n_epochs, - usg_scores, - standard_connections, - lowest_tool_ids, - ): - self.test_data = test_data - self.test_labels = test_labels - self.reverse_data_dictionary = reverse_data_dictionary - self.precision = list() - self.usage_weights = list() - self.published_precision = list() - self.n_epochs = n_epochs - self.pred_usage_scores = usg_scores - self.standard_connections = standard_connections - self.lowest_tool_ids = lowest_tool_ids - self.lowest_pub_precision = list() - self.lowest_norm_precision = list() - - def on_epoch_end(self, epoch, logs={}): - """ - Compute absolute and compatible precision for test data - """ - if len(self.test_data) > 0: - ( - usage_weights, - precision, - precision_pub, - low_pub_prec, - low_norm_prec, - low_num, - ) = utils.verify_model( - self.model, - self.test_data, - self.test_labels, - self.reverse_data_dictionary, - self.pred_usage_scores, - self.standard_connections, - self.lowest_tool_ids, - ) - self.precision.append(precision) - self.usage_weights.append(usage_weights) - self.published_precision.append(precision_pub) - self.lowest_pub_precision.append(low_pub_prec) - self.lowest_norm_precision.append(low_norm_prec) - print("Epoch %d usage weights: %s" % (epoch + 1, usage_weights)) - print("Epoch %d normal precision: %s" % (epoch + 1, precision)) - print("Epoch %d published precision: %s" % (epoch + 1, precision_pub)) - print("Epoch %d lowest published precision: %s" % (epoch + 1, low_pub_prec)) - print("Epoch %d lowest normal precision: %s" % (epoch + 1, low_norm_prec)) - print( - "Epoch %d number of test samples with lowest tool ids: %s" - % (epoch + 1, low_num) - ) - +import train_transformer if __name__ == "__main__": start_time = time.time() arg_parser = argparse.ArgumentParser() - arg_parser.add_argument( - "-wf", "--workflow_file", required=True, help="workflows tabular file" - ) - arg_parser.add_argument( - "-tu", "--tool_usage_file", required=True, help="tool usage file" - ) - arg_parser.add_argument( - "-om", "--output_model", required=True, help="trained model file" - ) + arg_parser.add_argument("-wf", "--workflow_file", required=True, help="workflows tabular file") + arg_parser.add_argument("-tu", "--tool_usage_file", required=True, help="tool usage file") # data parameters - arg_parser.add_argument( - "-cd", - "--cutoff_date", - required=True, - help="earliest date for taking tool usage", - ) - arg_parser.add_argument( - "-pl", - "--maximum_path_length", - required=True, - help="maximum length of tool path", - ) - arg_parser.add_argument( - "-ep", - "--n_epochs", - required=True, - help="number of iterations to run to create model", - ) - arg_parser.add_argument( - "-oe", - "--optimize_n_epochs", - required=True, - help="number of iterations to run to find best model parameters", - ) - arg_parser.add_argument( - "-me", - "--max_evals", - required=True, - help="maximum number of configuration evaluations", - ) - arg_parser.add_argument( - "-ts", - "--test_share", - required=True, - help="share of data to be used for testing", - ) + arg_parser.add_argument("-cd", "--cutoff_date", required=True, help="earliest date for taking tool usage") + arg_parser.add_argument("-pl", "--maximum_path_length", required=True, help="maximum length of tool path") + arg_parser.add_argument("-om", "--output_model", required=True, help="trained model path") # neural network parameters - arg_parser.add_argument( - "-bs", - "--batch_size", - required=True, - help="size of the tranining batch i.e. the number of samples per batch", - ) - arg_parser.add_argument( - "-ut", "--units", required=True, help="number of hidden recurrent units" - ) - arg_parser.add_argument( - "-es", - "--embedding_size", - required=True, - help="size of the fixed vector learned for each tool", - ) - arg_parser.add_argument( - "-dt", "--dropout", required=True, help="percentage of neurons to be dropped" - ) - arg_parser.add_argument( - "-sd", - "--spatial_dropout", - required=True, - help="1d dropout used for embedding layer", - ) - arg_parser.add_argument( - "-rd", - "--recurrent_dropout", - required=True, - help="dropout for the recurrent layers", - ) - arg_parser.add_argument( - "-lr", "--learning_rate", required=True, help="learning rate" - ) + arg_parser.add_argument("-ti", "--n_train_iter", required=True, help="Number of training iterations run to create model") + arg_parser.add_argument("-nhd", "--n_heads", required=True, help="Number of head in transformer's multi-head attention") + arg_parser.add_argument("-ed", "--n_embed_dim", required=True, help="Embedding dimension") + arg_parser.add_argument("-fd", "--n_feed_forward_dim", required=True, help="Feed forward network dimension") + arg_parser.add_argument("-dt", "--dropout", required=True, help="Percentage of neurons to be dropped") + arg_parser.add_argument("-lr", "--learning_rate", required=True, help="Learning rate") + arg_parser.add_argument("-ts", "--te_share", required=True, help="Share of data to be used for testing") + arg_parser.add_argument("-trbs", "--tr_batch_size", required=True, help="Train batch size") + arg_parser.add_argument("-trlg", "--tr_logging_step", required=True, help="Train logging frequency") + arg_parser.add_argument("-telg", "--te_logging_step", required=True, help="Test logging frequency") + arg_parser.add_argument("-tebs", "--te_batch_size", required=True, help="Test batch size") # get argument values args = vars(arg_parser.parse_args()) @@ -262,89 +38,48 @@ workflows_path = args["workflow_file"] cutoff_date = args["cutoff_date"] maximum_path_length = int(args["maximum_path_length"]) + + n_train_iter = int(args["n_train_iter"]) + te_share = float(args["te_share"]) + tr_batch_size = int(args["tr_batch_size"]) + te_batch_size = int(args["te_batch_size"]) + + n_heads = int(args["n_heads"]) + feed_forward_dim = int(args["n_feed_forward_dim"]) + embedding_dim = int(args["n_embed_dim"]) + dropout = float(args["dropout"]) + learning_rate = float(args["learning_rate"]) + te_logging_step = int(args["te_logging_step"]) + tr_logging_step = int(args["tr_logging_step"]) trained_model_path = args["output_model"] - n_epochs = int(args["n_epochs"]) - optimize_n_epochs = int(args["optimize_n_epochs"]) - max_evals = int(args["max_evals"]) - test_share = float(args["test_share"]) - batch_size = args["batch_size"] - units = args["units"] - embedding_size = args["embedding_size"] - dropout = args["dropout"] - spatial_dropout = args["spatial_dropout"] - recurrent_dropout = args["recurrent_dropout"] - learning_rate = args["learning_rate"] - num_cpus = 16 config = { - "cutoff_date": cutoff_date, - "maximum_path_length": maximum_path_length, - "n_epochs": n_epochs, - "optimize_n_epochs": optimize_n_epochs, - "max_evals": max_evals, - "test_share": test_share, - "batch_size": batch_size, - "units": units, - "embedding_size": embedding_size, - "dropout": dropout, - "spatial_dropout": spatial_dropout, - "recurrent_dropout": recurrent_dropout, - "learning_rate": learning_rate, + 'cutoff_date': cutoff_date, + 'maximum_path_length': maximum_path_length, + 'n_train_iter': n_train_iter, + 'n_heads': n_heads, + 'feed_forward_dim': feed_forward_dim, + 'embedding_dim': embedding_dim, + 'dropout': dropout, + 'learning_rate': learning_rate, + 'te_share': te_share, + 'te_logging_step': te_logging_step, + 'tr_logging_step': tr_logging_step, + 'tr_batch_size': tr_batch_size, + 'te_batch_size': te_batch_size, + 'trained_model_path': trained_model_path } - + print("Preprocessing workflows...") # Extract and process workflows connections = extract_workflow_connections.ExtractWorkflowConnections() - ( - workflow_paths, - compatible_next_tools, - standard_connections, - ) = connections.read_tabular_file(workflows_path) + # Process raw workflow file + wf_dataframe, usage_df = connections.process_raw_files(workflows_path, tool_usage_path, config) + workflow_paths, pub_conn = connections.read_tabular_file(wf_dataframe, config) # Process the paths from workflows print("Dividing data...") - data = prepare_data.PrepareData(maximum_path_length, test_share) - ( - train_data, - train_labels, - test_data, - test_labels, - data_dictionary, - reverse_dictionary, - class_weights, - usage_pred, - train_tool_freq, - tool_tr_samples, - ) = data.get_data_labels_matrices( - workflow_paths, - tool_usage_path, - cutoff_date, - compatible_next_tools, - standard_connections, - ) - # find the best model and start training - predict_tool = PredictTool(num_cpus) - # start training with weighted classes - print("Training with weighted classes and samples ...") - results_weighted = predict_tool.find_train_best_network( - config, - reverse_dictionary, - train_data, - train_labels, - test_data, - test_labels, - n_epochs, - class_weights, - usage_pred, - standard_connections, - train_tool_freq, - tool_tr_samples, - ) - utils.save_model( - results_weighted, - data_dictionary, - compatible_next_tools, - trained_model_path, - class_weights, - standard_connections, - ) + data = prepare_data.PrepareData(maximum_path_length, te_share) + train_data, train_labels, test_data, test_labels, f_dict, r_dict, c_wts, c_tools, tr_tool_freq = data.get_data_labels_matrices(workflow_paths, usage_df, cutoff_date, pub_conn) + print(train_data.shape, train_labels.shape, test_data.shape, test_labels.shape) + train_transformer.create_enc_transformer(train_data, train_labels, test_data, test_labels, f_dict, r_dict, c_wts, c_tools, pub_conn, tr_tool_freq, config) end_time = time.time() print("Program finished in %s seconds" % str(end_time - start_time))
--- a/optimise_hyperparameters.py Fri May 06 09:05:18 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,147 +0,0 @@ -""" -Find the optimal combination of hyperparameters -""" - -import numpy as np -import utils -from hyperopt import fmin, hp, STATUS_OK, tpe, Trials -from tensorflow.keras.callbacks import EarlyStopping -from tensorflow.keras.layers import Dense, Dropout, Embedding, GRU, SpatialDropout1D -from tensorflow.keras.models import Sequential -from tensorflow.keras.optimizers import RMSprop - - -class HyperparameterOptimisation: - def __init__(self): - """ Init method. """ - - def train_model( - self, - config, - reverse_dictionary, - train_data, - train_labels, - test_data, - test_labels, - tool_tr_samples, - class_weights, - ): - """ - Train a model and report accuracy - """ - # convert items to integer - l_batch_size = list(map(int, config["batch_size"].split(","))) - l_embedding_size = list(map(int, config["embedding_size"].split(","))) - l_units = list(map(int, config["units"].split(","))) - - # convert items to float - l_learning_rate = list(map(float, config["learning_rate"].split(","))) - l_dropout = list(map(float, config["dropout"].split(","))) - l_spatial_dropout = list(map(float, config["spatial_dropout"].split(","))) - l_recurrent_dropout = list(map(float, config["recurrent_dropout"].split(","))) - - optimize_n_epochs = int(config["optimize_n_epochs"]) - - # get dimensions - dimensions = len(reverse_dictionary) + 1 - best_model_params = dict() - early_stopping = EarlyStopping( - monitor="val_loss", - mode="min", - verbose=1, - min_delta=1e-1, - restore_best_weights=True, - ) - - # specify the search space for finding the best combination of parameters using Bayesian optimisation - params = { - "embedding_size": hp.quniform( - "embedding_size", l_embedding_size[0], l_embedding_size[1], 1 - ), - "units": hp.quniform("units", l_units[0], l_units[1], 1), - "batch_size": hp.quniform( - "batch_size", l_batch_size[0], l_batch_size[1], 1 - ), - "learning_rate": hp.loguniform( - "learning_rate", np.log(l_learning_rate[0]), np.log(l_learning_rate[1]) - ), - "dropout": hp.uniform("dropout", l_dropout[0], l_dropout[1]), - "spatial_dropout": hp.uniform( - "spatial_dropout", l_spatial_dropout[0], l_spatial_dropout[1] - ), - "recurrent_dropout": hp.uniform( - "recurrent_dropout", l_recurrent_dropout[0], l_recurrent_dropout[1] - ), - } - - def create_model(params): - model = Sequential() - model.add( - Embedding(dimensions, int(params["embedding_size"]), mask_zero=True) - ) - model.add(SpatialDropout1D(params["spatial_dropout"])) - model.add( - GRU( - int(params["units"]), - dropout=params["dropout"], - recurrent_dropout=params["recurrent_dropout"], - return_sequences=True, - activation="elu", - ) - ) - model.add(Dropout(params["dropout"])) - model.add( - GRU( - int(params["units"]), - dropout=params["dropout"], - recurrent_dropout=params["recurrent_dropout"], - return_sequences=False, - activation="elu", - ) - ) - model.add(Dropout(params["dropout"])) - model.add(Dense(2 * dimensions, activation="sigmoid")) - optimizer_rms = RMSprop(lr=params["learning_rate"]) - batch_size = int(params["batch_size"]) - model.compile( - loss=utils.weighted_loss(class_weights), optimizer=optimizer_rms - ) - print(model.summary()) - model_fit = model.fit( - utils.balanced_sample_generator( - train_data, - train_labels, - batch_size, - tool_tr_samples, - reverse_dictionary, - ), - steps_per_epoch=len(train_data) // batch_size, - epochs=optimize_n_epochs, - callbacks=[early_stopping], - validation_data=(test_data, test_labels), - verbose=2, - shuffle=True, - ) - return { - "loss": model_fit.history["val_loss"][-1], - "status": STATUS_OK, - "model": model, - } - - # minimize the objective function using the set of parameters above - trials = Trials() - learned_params = fmin( - create_model, - params, - trials=trials, - algo=tpe.suggest, - max_evals=int(config["max_evals"]), - ) - best_model = trials.results[np.argmin([r["loss"] for r in trials.results])][ - "model" - ] - # set the best params with respective values - for item in learned_params: - item_val = learned_params[item] - best_model_params[item] = item_val - return best_model_params, best_model
--- a/predict_tool_usage.py Fri May 06 09:05:18 2022 +0000 +++ b/predict_tool_usage.py Sun Oct 16 11:52:10 2022 +0000 @@ -3,9 +3,6 @@ """ import collections -import csv -import os -import warnings import numpy as np import utils @@ -13,40 +10,36 @@ from sklearn.pipeline import Pipeline from sklearn.svm import SVR -warnings.filterwarnings("ignore") - -main_path = os.getcwd() - class ToolPopularity: + def __init__(self): """ Init method. """ - def extract_tool_usage(self, tool_usage_file, cutoff_date, dictionary): + def extract_tool_usage(self, tool_usage_df, cutoff_date, dictionary): """ Extract the tool usage over time for each tool """ tool_usage_dict = dict() all_dates = list() all_tool_list = list(dictionary.keys()) - with open(tool_usage_file, "rt") as usage_file: - tool_usage = csv.reader(usage_file, delimiter="\t") - for index, row in enumerate(tool_usage): - row = [item.strip() for item in row] - if (str(row[1]).strip() > cutoff_date) is True: - tool_id = utils.format_tool_id(row[0]) - if tool_id in all_tool_list: - all_dates.append(row[1]) - if tool_id not in tool_usage_dict: - tool_usage_dict[tool_id] = dict() - tool_usage_dict[tool_id][row[1]] = int(row[2]) + for index, row in tool_usage_df.iterrows(): + row = row.tolist() + row = [str(item).strip() for item in row] + if (row[1] > cutoff_date) is True: + tool_id = utils.format_tool_id(row[0]) + if tool_id in all_tool_list: + all_dates.append(row[1]) + if tool_id not in tool_usage_dict: + tool_usage_dict[tool_id] = dict() + tool_usage_dict[tool_id][row[1]] = int(float(row[2])) + else: + curr_date = row[1] + # merge the usage of different version of tools into one + if curr_date in tool_usage_dict[tool_id]: + tool_usage_dict[tool_id][curr_date] += int(float(row[2])) else: - curr_date = row[1] - # merge the usage of different version of tools into one - if curr_date in tool_usage_dict[tool_id]: - tool_usage_dict[tool_id][curr_date] += int(row[2]) - else: - tool_usage_dict[tool_id][curr_date] = int(row[2]) + tool_usage_dict[tool_id][curr_date] = int(float(row[2])) # get unique dates unique_dates = list(set(all_dates)) for tool in tool_usage_dict: @@ -66,25 +59,17 @@ """ epsilon = 0.0 cv = 5 - s_typ = "neg_mean_absolute_error" + s_typ = 'neg_mean_absolute_error' n_jobs = 4 s_error = 1 tr_score = False try: - pipe = Pipeline(steps=[("regressor", SVR(gamma="scale"))]) + pipe = Pipeline(steps=[('regressor', SVR(gamma='scale'))]) param_grid = { - "regressor__kernel": ["rbf", "poly", "linear"], - "regressor__degree": [2, 3], + 'regressor__kernel': ['rbf', 'poly', 'linear'], + 'regressor__degree': [2, 3] } - search = GridSearchCV( - pipe, - param_grid, - cv=cv, - scoring=s_typ, - n_jobs=n_jobs, - error_score=s_error, - return_train_score=tr_score, - ) + search = GridSearchCV(pipe, param_grid, cv=cv, scoring=s_typ, n_jobs=n_jobs, error_score=s_error, return_train_score=tr_score) search.fit(x_reshaped, y_reshaped.ravel()) model = search.best_estimator_ # set the next time point to get prediction for
--- a/prepare_data.py Fri May 06 09:05:18 2022 +0000 +++ b/prepare_data.py Sun Oct 16 11:52:10 2022 +0000 @@ -5,16 +5,15 @@ """ import collections -import os import random import numpy as np import predict_tool_usage - -main_path = os.getcwd() +from sklearn.model_selection import train_test_split class PrepareData: + def __init__(self, max_seq_length, test_data_share): """ Init method. """ self.max_tool_sequence_len = max_seq_length @@ -26,7 +25,7 @@ """ tokens = list() raw_paths = workflow_paths - raw_paths = [x.replace("\n", "") for x in raw_paths] + raw_paths = [x.replace("\n", '') for x in raw_paths] for item in raw_paths: split_items = item.split(",") for token in split_items: @@ -34,12 +33,7 @@ tokens.append(token) tokens = list(set(tokens)) tokens = np.array(tokens) - tokens = np.reshape( - tokens, - [ - -1, - ], - ) + tokens = np.reshape(tokens, [-1, ]) return tokens, raw_paths def create_new_dict(self, new_data_dict): @@ -62,116 +56,110 @@ """ count = collections.Counter(words).most_common() dictionary = dict() - for word, _ in count: + for index, (word, _) in enumerate(count): + word = word.lstrip() + word = word.rstrip() dictionary[word] = len(dictionary) + 1 - word = word.strip() - dictionary, reverse_dictionary = self.assemble_dictionary( - dictionary, old_data_dictionary - ) + dictionary, reverse_dictionary = self.assemble_dictionary(dictionary, old_data_dictionary) return dictionary, reverse_dictionary def decompose_paths(self, paths, dictionary): """ Decompose the paths to variable length sub-paths keeping the first tool fixed """ + max_len = 0 sub_paths_pos = list() for index, item in enumerate(paths): tools = item.split(",") len_tools = len(tools) - if len_tools <= self.max_tool_sequence_len: - for window in range(1, len_tools): - sequence = tools[0: window + 1] - tools_pos = [ - str(dictionary[str(tool_item)]) for tool_item in sequence - ] - if len(tools_pos) > 1: - sub_paths_pos.append(",".join(tools_pos)) + if len_tools > max_len: + max_len = len_tools + if len_tools < self.max_tool_sequence_len: + sequence = tools[0: len_tools] + tools_pos = [str(dictionary[str(tool_item)]) for tool_item in sequence] + if len(tools_pos) > 1: + sub_paths_pos.append(",".join(tools_pos)) sub_paths_pos = list(set(sub_paths_pos)) + print("Max length of tools: ", max_len) return sub_paths_pos - def prepare_paths_labels_dictionary( - self, dictionary, reverse_dictionary, paths, compatible_next_tools - ): - """ - Create a dictionary of sequences with their labels for training and test paths - """ - paths_labels = dict() - random.shuffle(paths) - for item in paths: - if item and item not in "": - tools = item.split(",") - label = tools[-1] - train_tools = tools[: len(tools) - 1] - last_but_one_name = reverse_dictionary[int(train_tools[-1])] - try: - compatible_tools = compatible_next_tools[last_but_one_name].split( - "," - ) - except Exception: - continue - if len(compatible_tools) > 0: - compatible_tools_ids = [ - str(dictionary[x]) for x in compatible_tools - ] - compatible_tools_ids.append(label) - composite_labels = ",".join(compatible_tools_ids) - train_tools = ",".join(train_tools) - if train_tools in paths_labels: - paths_labels[train_tools] += "," + composite_labels - else: - paths_labels[train_tools] = composite_labels - for item in paths_labels: - paths_labels[item] = ",".join(list(set(paths_labels[item].split(",")))) - return paths_labels + def prepare_input_one_target_paths(self, dictionary, reverse_dictionary, paths): + input_target_paths = dict() + compatible_tools = dict() + d_size = 0 + for i, item in enumerate(paths): + input_tools = item.split(",") + tool_seq = input_tools + i_tools = ",".join(tool_seq[0:-1]) + last_i_tool = i_tools.split(",")[-1] + if last_i_tool not in compatible_tools: + compatible_tools[last_i_tool] = list() + t_tools = tool_seq[-1] + if t_tools not in compatible_tools[last_i_tool]: + compatible_tools[last_i_tool].append(t_tools) + if i_tools not in input_target_paths: + input_target_paths[i_tools] = list() + if t_tools not in input_target_paths[i_tools]: + input_target_paths[i_tools].append(t_tools) + if i_tools not in input_target_paths: + input_target_paths[i_tools] = list() + if t_tools not in input_target_paths[i_tools]: + input_target_paths[i_tools].append(t_tools) + for item in input_target_paths: + d_size += len(input_target_paths[item]) + print("Dataset size:", d_size) + return input_target_paths, compatible_tools, d_size - def pad_test_paths(self, paths_dictionary, num_classes): - """ - Add padding to the tools sequences and create multi-hot encoded labels - """ - size_data = len(paths_dictionary) - data_mat = np.zeros([size_data, self.max_tool_sequence_len]) - label_mat = np.zeros([size_data, num_classes + 1]) - train_counter = 0 - for train_seq, train_label in list(paths_dictionary.items()): - positions = train_seq.split(",") - start_pos = self.max_tool_sequence_len - len(positions) - for id_pos, pos in enumerate(positions): - data_mat[train_counter][start_pos + id_pos] = int(pos) - for label_item in train_label.split(","): - label_mat[train_counter][int(label_item)] = 1.0 - train_counter += 1 - return data_mat, label_mat + def prepare_input_target_paths(self, dictionary, reverse_dictionary, paths): + input_target_paths = dict() + compatible_tools = dict() + d_size = 0 + for i, item in enumerate(paths): + input_tools = item.split(",") + ctr = 0 + for ctr in range(len(input_tools) - 1): + # uncomment this for one token target idea + tool_seq = input_tools[0: ctr + 2] + i_tools = ",".join(tool_seq[0:-1]) + last_i_tool = i_tools.split(",")[-1] + if last_i_tool not in compatible_tools: + compatible_tools[last_i_tool] = list() + t_tools = tool_seq[-1] + if t_tools not in compatible_tools[last_i_tool]: + compatible_tools[last_i_tool].append(t_tools) + if i_tools not in input_target_paths: + input_target_paths[i_tools] = list() + if t_tools not in input_target_paths[i_tools]: + input_target_paths[i_tools].append(t_tools) + if i_tools not in input_target_paths: + input_target_paths[i_tools] = list() + if t_tools not in input_target_paths[i_tools]: + input_target_paths[i_tools].append(t_tools) + for item in input_target_paths: + d_size += len(input_target_paths[item]) + print("Dataset size:", d_size) + return input_target_paths, compatible_tools, d_size - def pad_paths( - self, paths_dictionary, num_classes, standard_connections, reverse_dictionary - ): - """ - Add padding to the tools sequences and create multi-hot encoded labels - """ - size_data = len(paths_dictionary) - data_mat = np.zeros([size_data, self.max_tool_sequence_len]) - label_mat = np.zeros([size_data, 2 * (num_classes + 1)]) - pos_flag = 1.0 + def pad_paths_one_tool_target(self, multi_paths, compatible_tools, d_size, rev_dict, dictionary): + d_size = len(multi_paths) + input_mat = np.zeros([d_size, self.max_tool_sequence_len]) + target_mat = np.zeros([d_size, len(dictionary) + 1]) train_counter = 0 - for train_seq, train_label in list(paths_dictionary.items()): - pub_connections = list() - positions = train_seq.split(",") - last_tool_id = positions[-1] - last_tool_name = reverse_dictionary[int(last_tool_id)] - start_pos = self.max_tool_sequence_len - len(positions) - for id_pos, pos in enumerate(positions): - data_mat[train_counter][start_pos + id_pos] = int(pos) - if last_tool_name in standard_connections: - pub_connections = standard_connections[last_tool_name] - for label_item in train_label.split(","): - label_pos = int(label_item) - label_row = label_mat[train_counter] - if reverse_dictionary[label_pos] in pub_connections: - label_row[label_pos] = pos_flag - else: - label_row[label_pos + num_classes + 1] = pos_flag + for input_seq, target_seq_tools in list(multi_paths.items()): + input_seq_tools = input_seq.split(",") + last_i_tool = input_seq_tools[-1] + for id_pos, pos in enumerate(input_seq_tools): + input_mat[train_counter][id_pos] = int(pos) + if last_i_tool in compatible_tools: + compatible_targets = compatible_tools[last_i_tool] + for k, t_label in enumerate(target_seq_tools): + target_mat[train_counter][int(t_label)] = 1 + for c_tool in compatible_targets: + target_mat[train_counter][int(c_tool)] = 1 train_counter += 1 - return data_mat, label_mat + print("Final data size: ", input_mat.shape, target_mat.shape) + train_data, test_data, train_labels, test_labels = train_test_split(input_mat, target_mat, test_size=self.test_share, random_state=42) + return train_data, train_labels, test_data, test_labels def split_test_train_data(self, multilabels_paths): """ @@ -221,6 +209,27 @@ class_weights[key] = np.round(np.log(u_score), 6) return class_weights + def get_train_tool_labels_freq(self, train_paths, reverse_dictionary): + """ + Get the frequency of last tool of each tool sequence + to estimate the frequency of tool sequences + """ + last_tool_freq = dict() + freq_dict_names = dict() + for path in train_paths: + tools_pos = np.where(path > 0)[0] + path_pos = tools_pos + path_pos = [str(int(item)) for item in path_pos] + + for tool_pos in path_pos: + if tool_pos not in last_tool_freq: + last_tool_freq[tool_pos] = 0 + freq_dict_names[reverse_dictionary[int(tool_pos)]] = 0 + last_tool_freq[tool_pos] += 1 + freq_dict_names[reverse_dictionary[int(tool_pos)]] += 1 + sorted_dict = dict(sorted(last_tool_freq.items(), key=lambda kv: kv[1], reverse=True)) + return sorted_dict + def get_train_last_tool_freq(self, train_paths, reverse_dictionary): """ Get the frequency of last tool of each tool sequence @@ -229,13 +238,17 @@ last_tool_freq = dict() freq_dict_names = dict() for path in train_paths: - last_tool = path.split(",")[-1] + tools_pos = np.where(path > 0)[0] + path_pos = path[tools_pos] + path_pos = [str(int(item)) for item in path_pos] + last_tool = path_pos[-1] if last_tool not in last_tool_freq: last_tool_freq[last_tool] = 0 freq_dict_names[reverse_dictionary[int(last_tool)]] = 0 last_tool_freq[last_tool] += 1 freq_dict_names[reverse_dictionary[int(last_tool)]] += 1 - return last_tool_freq + sorted_dict = dict(sorted(last_tool_freq.items(), key=lambda kv: kv[1], reverse=True)) + return sorted_dict def get_toolid_samples(self, train_data, l_tool_freq): l_tool_tr_samples = dict() @@ -248,22 +261,13 @@ l_tool_tr_samples[last_tool_id].append(index) return l_tool_tr_samples - def get_data_labels_matrices( - self, - workflow_paths, - tool_usage_path, - cutoff_date, - compatible_next_tools, - standard_connections, - old_data_dictionary={}, - ): + def get_data_labels_matrices(self, workflow_paths, usage_df, cutoff_date, standard_connections, old_data_dictionary={}): """ Convert the training and test paths into corresponding numpy matrices """ processed_data, raw_paths = self.process_workflow_paths(workflow_paths) - dictionary, rev_dict = self.create_data_dictionary( - processed_data, old_data_dictionary - ) + dictionary, rev_dict = self.create_data_dictionary(processed_data, old_data_dictionary) + num_classes = len(dictionary) print("Raw paths: %d" % len(raw_paths)) @@ -274,50 +278,26 @@ random.shuffle(all_unique_paths) print("Creating dictionaries...") - multilabels_paths = self.prepare_paths_labels_dictionary( - dictionary, rev_dict, all_unique_paths, compatible_next_tools - ) + multilabels_paths, compatible_tools, d_size = self.prepare_input_target_paths(dictionary, rev_dict, all_unique_paths) - print("Complete data: %d" % len(multilabels_paths)) - train_paths_dict, test_paths_dict = self.split_test_train_data( - multilabels_paths - ) - - print("Train data: %d" % len(train_paths_dict)) - print("Test data: %d" % len(test_paths_dict)) + print("Complete data: %d" % d_size) print("Padding train and test data...") - # pad training and test data with leading zeros - test_data, test_labels = self.pad_paths( - test_paths_dict, num_classes, standard_connections, rev_dict - ) - train_data, train_labels = self.pad_paths( - train_paths_dict, num_classes, standard_connections, rev_dict - ) + # pad training and test data with trailing zeros + train_data, train_labels, test_data, test_labels = self.pad_paths_one_tool_target(multilabels_paths, compatible_tools, d_size, rev_dict, dictionary) + + print("Train data: ", train_data.shape) + print("Test data: ", test_data.shape) print("Estimating sample frequency...") - l_tool_freq = self.get_train_last_tool_freq(train_paths_dict, rev_dict) - l_tool_tr_samples = self.get_toolid_samples(train_data, l_tool_freq) + tr_tool_freq = self.get_train_tool_labels_freq(train_labels, rev_dict) # Predict tools usage print("Predicting tools' usage...") usage_pred = predict_tool_usage.ToolPopularity() - usage = usage_pred.extract_tool_usage(tool_usage_path, cutoff_date, dictionary) + usage = usage_pred.extract_tool_usage(usage_df, cutoff_date, dictionary) tool_usage_prediction = usage_pred.get_pupularity_prediction(usage) t_pred_usage = self.get_predicted_usage(dictionary, tool_usage_prediction) - # get class weights using the predicted usage for each tool class_weights = self.assign_class_weights(num_classes, t_pred_usage) - - return ( - train_data, - train_labels, - test_data, - test_labels, - dictionary, - rev_dict, - class_weights, - t_pred_usage, - l_tool_freq, - l_tool_tr_samples, - ) + return train_data, train_labels, test_data, test_labels, dictionary, rev_dict, class_weights, compatible_tools, tr_tool_freq
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5 | 2013-02-07 |35.0| Paste1 | 1.0.0 |65.0| Add_a_column1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |36.0| Paste1 | 1.0.0 |64.0| Add_a_column1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |37.0| addValue | 1.0.0 |26.0| cat1 | 1.0.0 | f | t | f + 5 | 2013-02-07 |38.0| addValue | 1.0.0 |26.0| cat1 | 1.0.0 | f | t | f + 5 | 2013-02-07 |39.0| Filter1 | 1.1.0 |45.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |39.0| Filter1 | 1.1.0 |46.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |40.0| gops_coverage_1 | 1.0.0 |39.0| Filter1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |40.0| gops_coverage_1 | 1.0.0 |41.0| cshl_grep_tool | 1.0.0 | f | t | f + 5 | 2013-02-07 |40.0| gops_coverage_1 | 1.0.0 |42.0| Filter1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |40.0| gops_coverage_1 | 1.0.0 |43.0| Filter1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |40.0| gops_coverage_1 | 1.0.0 |44.0| Filter1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |41.0| cshl_grep_tool | 1.0.0 |51.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |41.0| cshl_grep_tool | 1.0.0 |52.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |42.0| Filter1 | 1.1.0 |49.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |42.0| Filter1 | 1.1.0 |50.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |43.0| Filter1 | 1.1.0 |55.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |43.0| Filter1 | 1.1.0 |56.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |44.0| Filter1 | 1.1.0 |53.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |44.0| Filter1 | 1.1.0 |54.0| Summary_Statistics1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |45.0| Summary_Statistics1 | 1.1.0 |57.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |46.0| Summary_Statistics1 | 1.1.0 |58.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |47.0| addValue | 1.0.0 |26.0| cat1 | 1.0.0 | f | t | f + 5 | 2013-02-07 |48.0| addValue | 1.0.0 |26.0| cat1 | 1.0.0 | f | t | f + 5 | 2013-02-07 |49.0| Summary_Statistics1 | 1.1.0 |32.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |50.0| Summary_Statistics1 | 1.1.0 |31.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |51.0| Summary_Statistics1 | 1.1.0 |34.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |52.0| Summary_Statistics1 | 1.1.0 |33.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |53.0| Summary_Statistics1 | 1.1.0 |28.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |54.0| Summary_Statistics1 | 1.1.0 |27.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |55.0| Summary_Statistics1 | 1.1.0 |30.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |56.0| Summary_Statistics1 | 1.1.0 |29.0| Cut1 | 1.0.1 | f | t | f + 5 | 2013-02-07 |57.0| Cut1 | 1.0.1 |35.0| Paste1 | 1.0.0 | f | t | f + 5 | 2013-02-07 |58.0| Cut1 | 1.0.1 |35.0| Paste1 | 1.0.0 | f | t | f + 5 | 2013-02-07 |59.0| Paste1 | 1.0.0 |62.0| Add_a_column1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |60.0| Paste1 | 1.0.0 |63.0| Add_a_column1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |61.0| Add_a_column1 | 1.1.0 |25.0| addValue | 1.0.0 | f | t | f + 5 | 2013-02-07 |62.0| Add_a_column1 | 1.1.0 |38.0| addValue | 1.0.0 | f | t | f + 5 | 2013-02-07 |63.0| Add_a_column1 | 1.1.0 |37.0| addValue | 1.0.0 | f | t | f + 5 | 2013-02-07 |64.0| Add_a_column1 | 1.1.0 |47.0| addValue | 1.0.0 | f | t | f + 5 | 2013-02-07 |65.0| Add_a_column1 | 1.1.0 |48.0| addValue | 1.0.0 | f | t | f + 5 | 2013-02-07 |66.0| Paste1 | 1.0.0 |61.0| Add_a_column1 | 1.1.0 | f | t | f + 5 | 2013-02-07 |68.0| | |40.0| gops_coverage_1 | 1.0.0 | f | t | f + 5 | 2013-02-07 |69.0| | |40.0| gops_coverage_1 | 1.0.0 | f | t | f + 6 | 2013-02-07 |71.0| | |70.0| cshl_awk_tool1 | 1.0.0 | f | t | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | t | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | t | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |82.0| Grep1 | 1.0.1 |84.0| Remove beginning1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | t | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | t | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |83.0| Cut1 | 1.0.1 |85.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | t | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | t | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |83.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | t | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | t | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |84.0| Remove beginning1 | 1.0.0 |86.0| Cut1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | t | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | t | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |85.0| addValue | 1.0.0 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | t | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | t | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |86.0| Cut1 | 1.0.1 |88.0| Paste1 | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | t | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | t | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |88.0| Paste1 | 1.0.0 |87.0| addValue | 1.0.0 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | t | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | t | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 9 | 2013-02-07 |89.0| | |82.0| Grep1 | 1.0.1 | f | f | f + 11 | 2013-02-07 |135.0| addValue | 1.0.0 |136.0| cat1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |136.0| cat1 | 1.0.0 |145.0| barchart_gnuplot | 1.0.0 | f | t | f + 11 | 2013-02-07 |137.0| Cut1 | 1.0.1 |170.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |138.0| Cut1 | 1.0.1 |170.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |139.0| Cut1 | 1.0.1 |177.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |140.0| Cut1 | 1.0.1 |177.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |141.0| Cut1 | 1.0.1 |147.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |142.0| Cut1 | 1.0.1 |147.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |143.0| Cut1 | 1.0.1 |171.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |144.0| Cut1 | 1.0.1 |171.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |146.0| Paste1 | 1.0.0 |176.0| Add_a_column1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |147.0| Paste1 | 1.0.0 |175.0| Add_a_column1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |148.0| addValue | 1.0.0 |136.0| cat1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |149.0| addValue | 1.0.0 |136.0| cat1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |150.0| Filter1 | 1.1.0 |156.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |150.0| Filter1 | 1.1.0 |157.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |151.0| gops_coverage_1 | 1.0.0 |150.0| Filter1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |151.0| gops_coverage_1 | 1.0.0 |152.0| cshl_grep_tool | 1.0.0 | f | t | f + 11 | 2013-02-07 |151.0| gops_coverage_1 | 1.0.0 |153.0| Filter1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |151.0| gops_coverage_1 | 1.0.0 |154.0| Filter1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |151.0| gops_coverage_1 | 1.0.0 |155.0| Filter1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |152.0| cshl_grep_tool | 1.0.0 |162.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |152.0| cshl_grep_tool | 1.0.0 |163.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |153.0| Filter1 | 1.1.0 |160.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |153.0| Filter1 | 1.1.0 |161.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |154.0| Filter1 | 1.1.0 |166.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |154.0| Filter1 | 1.1.0 |167.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |155.0| Filter1 | 1.1.0 |164.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |155.0| Filter1 | 1.1.0 |165.0| Summary_Statistics1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |156.0| Summary_Statistics1 | 1.1.0 |168.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |157.0| Summary_Statistics1 | 1.1.0 |169.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |158.0| addValue | 1.0.0 |136.0| cat1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |159.0| addValue | 1.0.0 |136.0| cat1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |160.0| Summary_Statistics1 | 1.1.0 |142.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |161.0| Summary_Statistics1 | 1.1.0 |141.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |162.0| Summary_Statistics1 | 1.1.0 |144.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |163.0| Summary_Statistics1 | 1.1.0 |143.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |164.0| Summary_Statistics1 | 1.1.0 |138.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |165.0| Summary_Statistics1 | 1.1.0 |137.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |166.0| Summary_Statistics1 | 1.1.0 |140.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |167.0| Summary_Statistics1 | 1.1.0 |139.0| Cut1 | 1.0.1 | f | t | f + 11 | 2013-02-07 |168.0| Cut1 | 1.0.1 |146.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |169.0| Cut1 | 1.0.1 |146.0| Paste1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |170.0| Paste1 | 1.0.0 |173.0| Add_a_column1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |171.0| Paste1 | 1.0.0 |174.0| Add_a_column1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |172.0| Add_a_column1 | 1.1.0 |135.0| addValue | 1.0.0 | f | t | f + 11 | 2013-02-07 |173.0| Add_a_column1 | 1.1.0 |149.0| addValue | 1.0.0 | f | t | f + 11 | 2013-02-07 |174.0| Add_a_column1 | 1.1.0 |148.0| addValue | 1.0.0 | f | t | f + 11 | 2013-02-07 |175.0| Add_a_column1 | 1.1.0 |158.0| addValue | 1.0.0 | f | t | f + 11 | 2013-02-07 |176.0| Add_a_column1 | 1.1.0 |159.0| addValue | 1.0.0 | f | t | f + 11 | 2013-02-07 |177.0| Paste1 | 1.0.0 |172.0| Add_a_column1 | 1.1.0 | f | t | f + 11 | 2013-02-07 |178.0| | |151.0| gops_coverage_1 | 1.0.0 | f | t | f + 11 | 2013-02-07 |179.0| | |151.0| gops_coverage_1 | 1.0.0 | f | t | f + 12 | 2013-02-07 |180.0| cat1 | 1.0.0 |189.0| barchart_gnuplot | 1.0.0 | t | t | f + 12 | 2013-02-07 |180.0| cat1 | 1.0.0 |189.0| barchart_gnuplot | 1.0.0 | f | t | f + 12 | 2013-02-07 |181.0| bedtools_intersectBed | |183.0| wc_gnu | 1.0.0 | t | t | f + 12 | 2013-02-07 |181.0| bedtools_intersectBed | |183.0| wc_gnu | 1.0.0 | f | t | f + 12 | 2013-02-07 |182.0| bedtools_intersectBed | |184.0| sort1 | 1.0.1 | t | t | f + 12 | 2013-02-07 |182.0| bedtools_intersectBed | |184.0| sort1 | 1.0.1 | f | t | f + 12 | 2013-02-07 |183.0| wc_gnu | 1.0.0 |185.0| addValue | 1.0.0 | t | t | f + 12 | 2013-02-07 |183.0| wc_gnu | 1.0.0 |185.0| addValue | 1.0.0 | f | t | f + 12 | 2013-02-07 |184.0| sort1 | 1.0.1 |186.0| cshl_awk_tool | | t | t | f + 12 | 2013-02-07 |184.0| sort1 | 1.0.1 |186.0| cshl_awk_tool | | f | t | f + 12 | 2013-02-07 |185.0| addValue | 1.0.0 |180.0| cat1 | 1.0.0 | t | t | f + 12 | 2013-02-07 |185.0| addValue | 1.0.0 |180.0| cat1 | 1.0.0 | f | t | f + 12 | 2013-02-07 |186.0| cshl_awk_tool | |188.0| cshl_uniq_tool | 1.0.0 | t | t | f + 12 | 2013-02-07 |186.0| cshl_awk_tool | |188.0| cshl_uniq_tool | 1.0.0 | f | t | f + 12 | 2013-02-07 |187.0| Count1 | 1.0.0 |180.0| cat1 | 1.0.0 | t | t | f + 12 | 2013-02-07 |187.0| Count1 | 1.0.0 |180.0| cat1 | 1.0.0 | f | t | f + 12 | 2013-02-07 |188.0| cshl_uniq_tool | 1.0.0 |187.0| Count1 | 1.0.0 | t | t | f + 12 | 2013-02-07 |188.0| cshl_uniq_tool | 1.0.0 |187.0| Count1 | 1.0.0 | f | t | f + 12 | 2013-02-07 |190.0| | |181.0| bedtools_intersectBed | | t | t | f + 12 | 2013-02-07 |190.0| | |181.0| bedtools_intersectBed | | f | t | f + 12 | 2013-02-07 |190.0| | |182.0| bedtools_intersectBed | | t | t | f + 12 | 2013-02-07 |190.0| | |182.0| bedtools_intersectBed | | f | t | f + 12 | 2013-02-07 |191.0| | |181.0| bedtools_intersectBed | | t | t | f + 12 | 2013-02-07 |191.0| | |181.0| bedtools_intersectBed | | f | t | f + 12 | 2013-02-07 |191.0| | |182.0| bedtools_intersectBed | | t | t | f + 12 | 2013-02-07 |191.0| | |182.0| bedtools_intersectBed | | f | t | f + 19 | 2013-02-14 |193.0| | |194.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | | f | t | f + 19 | 2013-02-14 |193.0| | |194.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | | f | t | f + 19 | 2013-02-14 |193.0| | |194.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | | f | t | f + 19 | 2013-02-14 |193.0| | |194.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | | f | t | f + 19 | 2013-02-14 |195.0| | |194.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | | f | t | f + 19 | 2013-02-14 |195.0| | |194.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | | f | t | f + 19 | 2013-02-14 |195.0| | |194.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | | f | t | f + 19 | 2013-02-14 |195.0| | |194.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | | f | t | f + 19 | 2013-02-14 |199.0| | |200.0| gops_intersect_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |199.0| | |200.0| gops_intersect_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |199.0| | |200.0| gops_intersect_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |199.0| | |200.0| gops_intersect_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |199.0| | |205.0| gops_subtract_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |199.0| | |205.0| gops_subtract_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |199.0| | |205.0| gops_subtract_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |199.0| | |205.0| gops_subtract_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |200.0| gops_intersect_1 | 1.0.0 |210.0| cshl_awk_tool | | f | t | f + 19 | 2013-02-14 |200.0| gops_intersect_1 | 1.0.0 |210.0| cshl_awk_tool | | f | t | f + 19 | 2013-02-14 |200.0| gops_intersect_1 | 1.0.0 |210.0| cshl_awk_tool | | f | t | f + 19 | 2013-02-14 |200.0| gops_intersect_1 | 1.0.0 |210.0| cshl_awk_tool | | f | t | f + 19 | 2013-02-14 |200.0| gops_intersect_1 | 1.0.0 |215.0| Count1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |200.0| gops_intersect_1 | 1.0.0 |215.0| Count1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |200.0| gops_intersect_1 | 1.0.0 |215.0| Count1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |200.0| gops_intersect_1 | 1.0.0 |215.0| Count1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |201.0| cshl_find_and_replace | 1.0.0 |200.0| gops_intersect_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |201.0| cshl_find_and_replace | 1.0.0 |200.0| gops_intersect_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |201.0| cshl_find_and_replace | 1.0.0 |200.0| gops_intersect_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |201.0| cshl_find_and_replace | 1.0.0 |200.0| gops_intersect_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |201.0| cshl_find_and_replace | 1.0.0 |205.0| gops_subtract_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |201.0| cshl_find_and_replace | 1.0.0 |205.0| gops_subtract_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |201.0| cshl_find_and_replace | 1.0.0 |205.0| gops_subtract_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |201.0| cshl_find_and_replace | 1.0.0 |205.0| gops_subtract_1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |202.0| get_flanks1 | 1.0.0 |201.0| cshl_find_and_replace | 1.0.0 | f | t | f + 19 | 2013-02-14 |202.0| get_flanks1 | 1.0.0 |201.0| cshl_find_and_replace | 1.0.0 | f | t | f + 19 | 2013-02-14 |202.0| get_flanks1 | 1.0.0 |201.0| cshl_find_and_replace | 1.0.0 | f | t | f + 19 | 2013-02-14 |202.0| get_flanks1 | 1.0.0 |201.0| cshl_find_and_replace | 1.0.0 | f | t | f + 19 | 2013-02-14 |203.0| | |202.0| get_flanks1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |203.0| | |202.0| get_flanks1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |203.0| | |202.0| get_flanks1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |203.0| | |202.0| get_flanks1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |203.0| | |204.0| cshl_grep_tool | 1.0.0 | f | t | f + 19 | 2013-02-14 |203.0| | |204.0| cshl_grep_tool | 1.0.0 | f | t | f + 19 | 2013-02-14 |203.0| | |204.0| cshl_grep_tool | 1.0.0 | f | t | f + 19 | 2013-02-14 |203.0| | |204.0| cshl_grep_tool | 1.0.0 | f | t | f + 19 | 2013-02-14 |205.0| gops_subtract_1 | 1.0.0 |206.0| cshl_awk_tool | | f | t | f + 19 | 2013-02-14 |205.0| gops_subtract_1 | 1.0.0 |206.0| cshl_awk_tool | | f | t | f + 19 | 2013-02-14 |205.0| gops_subtract_1 | 1.0.0 |206.0| cshl_awk_tool | | f | t | f + 19 | 2013-02-14 |205.0| gops_subtract_1 | 1.0.0 |206.0| cshl_awk_tool | | f | t | f + 19 | 2013-02-14 |206.0| cshl_awk_tool | |207.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |206.0| cshl_awk_tool | |207.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |206.0| cshl_awk_tool | |207.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |206.0| cshl_awk_tool | |207.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |207.0| Extract genomic DNA 1 | 2.2.2 |214.0| Show beginning1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |207.0| Extract genomic DNA 1 | 2.2.2 |214.0| Show beginning1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |207.0| Extract genomic DNA 1 | 2.2.2 |214.0| Show beginning1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |207.0| Extract genomic DNA 1 | 2.2.2 |214.0| Show beginning1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |208.0| | |207.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |208.0| | |207.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |208.0| | |207.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |208.0| | |207.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |208.0| | |209.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |208.0| | |209.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |208.0| | |209.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |208.0| | |209.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |208.0| | |213.0| trap | | f | t | f + 19 | 2013-02-14 |208.0| | |213.0| trap | | f | t | f + 19 | 2013-02-14 |208.0| | |213.0| trap | | f | t | f + 19 | 2013-02-14 |208.0| | |213.0| trap | | f | t | f + 19 | 2013-02-14 |209.0| Extract genomic DNA 1 | 2.2.2 |211.0| Show beginning1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |209.0| Extract genomic DNA 1 | 2.2.2 |211.0| Show beginning1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |209.0| Extract genomic DNA 1 | 2.2.2 |211.0| Show beginning1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |209.0| Extract genomic DNA 1 | 2.2.2 |211.0| Show beginning1 | 1.0.0 | f | t | f + 19 | 2013-02-14 |210.0| cshl_awk_tool | |209.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |210.0| cshl_awk_tool | |209.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |210.0| cshl_awk_tool | |209.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |210.0| cshl_awk_tool | |209.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 19 | 2013-02-14 |211.0| Show beginning1 | 1.0.0 |212.0| meme_meme | 1.0.0 | f | t | f + 19 | 2013-02-14 |211.0| Show beginning1 | 1.0.0 |212.0| meme_meme | 1.0.0 | f | t | f + 19 | 2013-02-14 |211.0| Show beginning1 | 1.0.0 |212.0| meme_meme | 1.0.0 | f | t | f + 19 | 2013-02-14 |211.0| Show beginning1 | 1.0.0 |212.0| meme_meme | 1.0.0 | f | t | f + 19 | 2013-02-14 |215.0| Count1 | 1.0.0 |216.0| barchart_gnuplot | 1.0.0 | f | t | f + 19 | 2013-02-14 |215.0| Count1 | 1.0.0 |216.0| barchart_gnuplot | 1.0.0 | f | t | f + 19 | 2013-02-14 |215.0| Count1 | 1.0.0 |216.0| barchart_gnuplot | 1.0.0 | f | t | f + 19 | 2013-02-14 |215.0| Count1 | 1.0.0 |216.0| barchart_gnuplot | 1.0.0 | f | t | f + 28 | 2013-02-14 |242.0| | |243.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | t | f + 28 | 2013-02-14 |244.0| | |243.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | t | f + 29 | 2013-02-14 |245.0| | |246.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | t | f + 29 | 2013-02-14 |247.0| | |246.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | t | f + 29 | 2013-02-14 |251.0| | |252.0| gops_intersect_1 | 1.0.0 | f | t | f + 29 | 2013-02-14 |251.0| | |257.0| gops_subtract_1 | 1.0.0 | f | t | f + 29 | 2013-02-14 |252.0| gops_intersect_1 | 1.0.0 |262.0| cshl_awk_tool | | f | t | f + 29 | 2013-02-14 |252.0| gops_intersect_1 | 1.0.0 |267.0| Count1 | 1.0.0 | f | t | f + 29 | 2013-02-14 |253.0| cshl_find_and_replace | 1.0.0 |252.0| gops_intersect_1 | 1.0.0 | f | t | f + 29 | 2013-02-14 |253.0| cshl_find_and_replace | 1.0.0 |257.0| gops_subtract_1 | 1.0.0 | f | t | f + 29 | 2013-02-14 |254.0| get_flanks1 | 1.0.0 |253.0| cshl_find_and_replace | 1.0.0 | f | t | f + 29 | 2013-02-14 |255.0| | |254.0| get_flanks1 | 1.0.0 | f | t | f + 29 | 2013-02-14 |255.0| | |256.0| cshl_grep_tool | 1.0.0 | f | t | f + 29 | 2013-02-14 |257.0| gops_subtract_1 | 1.0.0 |258.0| cshl_awk_tool | | f | t | f + 29 | 2013-02-14 |258.0| cshl_awk_tool | |259.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 29 | 2013-02-14 |259.0| Extract genomic DNA 1 | 2.2.2 |266.0| Show beginning1 | 1.0.0 | f | t | f + 29 | 2013-02-14 |260.0| | |259.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 29 | 2013-02-14 |260.0| | |261.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 29 | 2013-02-14 |260.0| | |265.0| trap | | f | t | f + 29 | 2013-02-14 |261.0| Extract genomic DNA 1 | 2.2.2 |263.0| Show beginning1 | 1.0.0 | f | t | f + 29 | 2013-02-14 |262.0| cshl_awk_tool | |261.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 29 | 2013-02-14 |263.0| Show beginning1 | 1.0.0 |264.0| meme_meme | 1.0.0 | f | t | f + 29 | 2013-02-14 |267.0| Count1 | 1.0.0 |268.0| barchart_gnuplot | 1.0.0 | f | t | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | t | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | t | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | t | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | t | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | t | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |274.0| | |275.0| addValue | 1.0.0 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |275.0| addValue | 1.0.0 |276.0| mergeCols1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | t | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 31 | 2013-02-18 |276.0| mergeCols1 | 1.0.1 |277.0| Cut1 | 1.0.1 | f | f | f + 33 | 2013-02-16 |283.0| | |284.0| gops_intersect_1 | 1.0.0 | f | f | f + 33 | 2013-02-16 |284.0| gops_intersect_1 | 1.0.0 |286.0| Count1 | 1.0.0 | f | f | f + 33 | 2013-02-16 |285.0| | |284.0| gops_intersect_1 | 1.0.0 | f | f | f + 33 | 2013-02-16 |286.0| Count1 | 1.0.0 |287.0| barchart_gnuplot | 1.0.0 | f | f | f + 34 | 2013-02-18 |288.0| | |289.0| cshl_awk_tool | 1.0.0 | f | t | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | t | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | f | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | t | f + 36 | 2013-02-18 |293.0| | |292.0| cshl_awk_tool | 1.0.0 | f | t | f + 37 | 2013-02-18 |294.0| | |295.0| cshl_sed_tool | 1.0.0 | f | f | f + 37 | 2013-02-18 |294.0| | |295.0| cshl_sed_tool | 1.0.0 | f | f | f + 37 | 2013-02-18 |294.0| | |295.0| cshl_sed_tool | 1.0.0 | f | f | f + 37 | 2013-02-18 |294.0| | |295.0| cshl_sed_tool | 1.0.0 | f | t | f + 38 | 2013-02-18 |296.0| gops_intersect_1 | |297.0| Count1 | | f | t | + 38 | 2013-02-18 |297.0| Count1 | |298.0| barchart_gnuplot | | f | t | + 39 | 2013-02-18 |301.0| gops_intersect_1 | |302.0| Count1 | | f | t | + 39 | 2013-02-18 |302.0| Count1 | |303.0| barchart_gnuplot | | f | t | + 41 | 2013-02-18 |309.0| | |310.0| gops_intersect_1 | 1.0.0 | f | f | f + 41 | 2013-02-18 |310.0| gops_intersect_1 | 1.0.0 |312.0| Count1 | 1.0.0 | f | f | f + 41 | 2013-02-18 |311.0| | |310.0| gops_intersect_1 | 1.0.0 | f | f | f + 41 | 2013-02-18 |312.0| Count1 | 1.0.0 |313.0| barchart_gnuplot | 1.0.0 | f | f | f + 51 | 2013-02-18 |344.0| Count1 | |345.0| barchart_gnuplot | | f | f | + 62 | 2013-02-18 |395.0| | |396.0| Count1 | 1.0.0 | f | t | f + 62 | 2013-02-18 |396.0| Count1 | 1.0.0 |397.0| barchart_gnuplot | 1.0.0 | f | t | f + 64 | 2013-02-18 |401.0| | |402.0| Count1 | 1.0.0 | f | f | f + 64 | 2013-02-18 |402.0| Count1 | 1.0.0 |403.0| barchart_gnuplot | 1.0.0 | f | f | f + 66 | 2013-02-18 |407.0| | |408.0| Count1 | 1.0.0 | f | f | f + 66 | 2013-02-18 |408.0| Count1 | 1.0.0 |409.0| barchart_gnuplot | 1.0.0 | f | f | f + 68 | 2013-02-18 |413.0| | |414.0| Count1 | 1.0.0 | f | f | f + 68 | 2013-02-18 |414.0| Count1 | 1.0.0 |415.0| barchart_gnuplot | 1.0.0 | f | f | f + 69 | 2013-02-18 |416.0| | |417.0| Count1 | 1.0.0 | f | f | f + 69 | 2013-02-18 |417.0| Count1 | 1.0.0 |418.0| barchart_gnuplot | 1.0.0 | f | f | f + 70 | 2013-02-18 |419.0| | |420.0| Count1 | 1.0.0 | f | f | f + 70 | 2013-02-18 |420.0| Count1 | 1.0.0 |421.0| barchart_gnuplot | 1.0.0 | f | f | f + 72 | 2013-02-18 |425.0| | |426.0| Count1 | 1.0.0 | f | f | f + 72 | 2013-02-18 |426.0| Count1 | 1.0.0 |427.0| barchart_gnuplot | 1.0.0 | f | f | f + 73 | 2013-02-18 |428.0| | |429.0| Count1 | 1.0.0 | f | f | f + 73 | 2013-02-18 |429.0| Count1 | 1.0.0 |430.0| barchart_gnuplot | 1.0.0 | f | f | f + 74 | 2013-02-18 |431.0| | |432.0| Count1 | 1.0.0 | f | f | f + 74 | 2013-02-18 |432.0| Count1 | 1.0.0 |433.0| barchart_gnuplot | 1.0.0 | f | f | f + 75 | 2013-02-18 |436.0| Count1 | |437.0| barchart_gnuplot | | f | f | + 76 | 2013-02-18 |438.0| | |439.0| Count1 | 1.0.0 | f | t | f + 76 | 2013-02-18 |439.0| Count1 | 1.0.0 |440.0| barchart_gnuplot | 1.0.0 | f | t | f + 77 | 2013-02-18 |442.0| | |443.0| Count1 | 1.0.0 | f | f | f + 77 | 2013-02-18 |443.0| Count1 | 1.0.0 |444.0| barchart_gnuplot | 1.0.0 | f | f | f + 78 | 2013-02-18 |445.0| | |446.0| Count1 | 1.0.0 | f | f | f + 78 | 2013-02-18 |446.0| Count1 | 1.0.0 |447.0| barchart_gnuplot | 1.0.0 | f | f | f + 80 | 2013-02-18 |462.0| | |463.0| Count1 | 1.0.0 | f | t | f + 80 | 2013-02-18 |463.0| Count1 | 1.0.0 |464.0| barchart_gnuplot | 1.0.0 | f | t | f + 81 | 2013-02-18 |465.0| | |466.0| addValue | 1.0.0 | f | f | f + 81 | 2013-02-18 |466.0| addValue | 1.0.0 |467.0| mergeCols1 | 1.0.1 | f | f | f + 81 | 2013-02-18 |467.0| mergeCols1 | 1.0.1 |468.0| Cut1 | 1.0.1 | f | f | f + 81 | 2013-02-18 |468.0| Cut1 | 1.0.1 |469.0| cshl_find_and_replace | 1.0.0 | f | f | f + 81 | 2013-02-18 |469.0| cshl_find_and_replace | 1.0.0 |470.0| cshl_find_and_replace | 1.0.0 | f | f | f + 81 | 2013-02-18 |472.0| gops_intersect_1 | 1.0.0 |473.0| Count1 | 1.0.0 | f | f | f + 81 | 2013-02-18 |473.0| Count1 | 1.0.0 |474.0| barchart_gnuplot | 1.0.0 | f | f | f + 82 | 2013-02-18 |475.0| | |476.0| CONVERTER_interval_to_bedstrict_0 | 1.0.0 | f | f | f + 82 | 2013-02-18 |476.0| CONVERTER_interval_to_bedstrict_0 | 1.0.0 |477.0| addValue | 1.0.0 | f | f | f + 82 | 2013-02-18 |477.0| addValue | 1.0.0 |478.0| mergeCols1 | 1.0.1 | f | f | f + 82 | 2013-02-18 |478.0| mergeCols1 | 1.0.1 |479.0| Cut1 | 1.0.1 | f | f | f + 82 | 2013-02-18 |481.0| | |482.0| get_flanks1 | 1.0.0 | f | f | f + 82 | 2013-02-18 |482.0| get_flanks1 | 1.0.0 |483.0| gops_intersect_1 | 1.0.0 | f | f | f + 82 | 2013-02-18 |483.0| gops_intersect_1 | 1.0.0 |487.0| Count1 | 1.0.0 | f | f | f + 82 | 2013-02-18 |484.0| cshl_find_and_replace | 1.0.0 |483.0| gops_intersect_1 | 1.0.0 | f | f | f + 82 | 2013-02-18 |484.0| cshl_find_and_replace | 1.0.0 |485.0| CONVERTER_interval_to_bed_0 | 1.0.0 | f | f | f + 82 | 2013-02-18 |485.0| CONVERTER_interval_to_bed_0 | 1.0.0 |486.0| CONVERTER_interval_to_bedstrict_0 | 1.0.0 | f | f | f + 82 | 2013-02-18 |487.0| Count1 | 1.0.0 |488.0| barchart_gnuplot | 1.0.0 | f | f | f + 83 | 2013-02-18 |489.0| | |490.0| gops_intersect_1 | 1.0.0 | f | f | f + 83 | 2013-02-18 |490.0| gops_intersect_1 | 1.0.0 |492.0| Count1 | 1.0.0 | f | f | f + 83 | 2013-02-18 |491.0| | |490.0| gops_intersect_1 | 1.0.0 | f | f | f + 83 | 2013-02-18 |492.0| Count1 | 1.0.0 |493.0| barchart_gnuplot | 1.0.0 | f | f | f + 84 | 2013-02-19 |495.0| gops_intersect_1 | |496.0| Count1 | | f | f | + 84 | 2013-02-19 |496.0| Count1 | |497.0| barchart_gnuplot | | f | f | + 86 | 2013-02-19 |504.0| | |505.0| gops_intersect_1 | 1.0.0 | f | f | f + 86 | 2013-02-19 |505.0| gops_intersect_1 | 1.0.0 |507.0| Count1 | 1.0.0 | f | f | f + 86 | 2013-02-19 |506.0| | |505.0| gops_intersect_1 | 1.0.0 | f | f | f + 86 | 2013-02-19 |507.0| Count1 | 1.0.0 |508.0| barchart_gnuplot | 1.0.0 | f | f | f + 95 | 2013-02-20 |671.0| | |672.0| gops_intersect_1 | 1.0.0 | f | f | f + 95 | 2013-02-20 |672.0| gops_intersect_1 | 1.0.0 |674.0| Count1 | 1.0.0 | f | f | f + 95 | 2013-02-20 |673.0| | |672.0| gops_intersect_1 | 1.0.0 | f | f | f + 95 | 2013-02-20 |674.0| Count1 | 1.0.0 |675.0| barchart_gnuplot | 1.0.0 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| 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toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 | 1.4.1 | f | f | f + 97 | 2013-02-20 |700.0| | |701.0| 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2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | f | t | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |704.0| gops_subtract_1 | 1.0.0 | t | t | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | f | t | f + 97 | 2013-02-20 |705.0| cshl_awk_tool | 1.0.0 |708.0| gops_intersect_1 | 1.0.0 | t | t | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | f | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | f | t | f + 97 | 2013-02-20 |706.0| get_flanks1 | 1.0.0 |705.0| cshl_awk_tool | 1.0.0 | t | t | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | f | t | f + 97 | 2013-02-20 |707.0| | |706.0| get_flanks1 | 1.0.0 | t | t | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |709.0| Extract genomic DNA 1 | 2.2.2 | t | t | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | f | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | f | t | f + 97 | 2013-02-20 |708.0| gops_intersect_1 | 1.0.0 |720.0| Count1 | 1.0.0 | t | t | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | f | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | f | t | f + 97 | 2013-02-20 |709.0| Extract genomic DNA 1 | 2.2.2 |716.0| fasta2tab | 1.1.0 | t | t | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | f | t | f + 97 | 2013-02-20 |710.0| | |709.0| Extract genomic DNA 1 | 2.2.2 | t | t | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 |710.0| | |711.0| Extract genomic DNA 1 | 2.2.2 | f | f | f + 97 | 2013-02-20 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1.0.0 86 Cut1 1.0.1 f f f -9 2013-02-07 85 addValue 1.0.0 88 Paste1 1.0.0 f f f -9 2013-02-07 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f -9 2013-02-07 88 Paste1 1.0.0 87 addValue 1.0.0 f f f -9 2013-02-07 89 82 Grep1 1.0.1 f f f -9 2013-02-07 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f -9 2013-02-07 83 Cut1 1.0.1 85 addValue 1.0.0 f f f -9 2013-02-07 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f -9 2013-02-07 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f -9 2013-02-07 85 addValue 1.0.0 88 Paste1 1.0.0 f f f -9 2013-02-07 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f -9 2013-02-07 88 Paste1 1.0.0 87 addValue 1.0.0 f f f -9 2013-02-07 89 82 Grep1 1.0.1 f f f -9 2013-02-07 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f -9 2013-02-07 83 Cut1 1.0.1 85 addValue 1.0.0 f f f -9 2013-02-07 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f -9 2013-02-07 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f -9 2013-02-07 85 addValue 1.0.0 88 Paste1 1.0.0 f f f -9 2013-02-07 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f -9 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bedtools_intersectBed f t f -12 2013-02-07 190 182 bedtools_intersectBed f t f -12 2013-02-07 191 181 bedtools_intersectBed f t f -12 2013-02-07 191 182 bedtools_intersectBed f t f -19 2013-02-14 193 194 toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 f t f -19 2013-02-14 195 194 toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 f t f -19 2013-02-14 199 200 gops_intersect_1 1.0.0 f t f -19 2013-02-14 199 205 gops_subtract_1 1.0.0 f t f -19 2013-02-14 200 gops_intersect_1 1.0.0 210 cshl_awk_tool f t f -19 2013-02-14 200 gops_intersect_1 1.0.0 215 Count1 1.0.0 f t f -19 2013-02-14 201 cshl_find_and_replace 1.0.0 200 gops_intersect_1 1.0.0 f t f -19 2013-02-14 201 cshl_find_and_replace 1.0.0 205 gops_subtract_1 1.0.0 f t f -19 2013-02-14 202 get_flanks1 1.0.0 201 cshl_find_and_replace 1.0.0 f t f -19 2013-02-14 203 202 get_flanks1 1.0.0 f t f -19 2013-02-14 203 204 cshl_grep_tool 1.0.0 f t f -19 2013-02-14 205 gops_subtract_1 1.0.0 206 cshl_awk_tool 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Extract genomic DNA 1 2.2.2 f t f -19 2013-02-14 208 209 Extract genomic DNA 1 2.2.2 f t f -19 2013-02-14 208 213 trap f t f -19 2013-02-14 209 Extract genomic DNA 1 2.2.2 211 Show beginning1 1.0.0 f t f -19 2013-02-14 210 cshl_awk_tool 209 Extract genomic DNA 1 2.2.2 f t f -19 2013-02-14 211 Show beginning1 1.0.0 212 meme_meme 1.0.0 f t f -19 2013-02-14 215 Count1 1.0.0 216 barchart_gnuplot 1.0.0 f t f -19 2013-02-14 193 194 toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 f t f -19 2013-02-14 195 194 toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 f t f -19 2013-02-14 199 200 gops_intersect_1 1.0.0 f t f -19 2013-02-14 199 205 gops_subtract_1 1.0.0 f t f -19 2013-02-14 200 gops_intersect_1 1.0.0 210 cshl_awk_tool f t f -19 2013-02-14 200 gops_intersect_1 1.0.0 215 Count1 1.0.0 f t f -19 2013-02-14 201 cshl_find_and_replace 1.0.0 200 gops_intersect_1 1.0.0 f t f -19 2013-02-14 201 cshl_find_and_replace 1.0.0 205 gops_subtract_1 1.0.0 f t f -19 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toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 1.4.1 f t f -29 2013-02-14 245 246 toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 1.4.1 f t f -29 2013-02-14 247 246 toolshed.g2.bx.psu.edu/repos/ryo-tas/macs14/peakcalling_macs14/1.4.1 1.4.1 f t f -29 2013-02-14 251 252 gops_intersect_1 1.0.0 f t f -29 2013-02-14 251 257 gops_subtract_1 1.0.0 f t f -29 2013-02-14 252 gops_intersect_1 1.0.0 262 cshl_awk_tool f t f -29 2013-02-14 252 gops_intersect_1 1.0.0 267 Count1 1.0.0 f t f -29 2013-02-14 253 cshl_find_and_replace 1.0.0 252 gops_intersect_1 1.0.0 f t f -29 2013-02-14 253 cshl_find_and_replace 1.0.0 257 gops_subtract_1 1.0.0 f t f -29 2013-02-14 254 get_flanks1 1.0.0 253 cshl_find_and_replace 1.0.0 f t f -29 2013-02-14 255 254 get_flanks1 1.0.0 f t f -29 2013-02-14 255 256 cshl_grep_tool 1.0.0 f t f -29 2013-02-14 257 gops_subtract_1 1.0.0 258 cshl_awk_tool f t f -29 2013-02-14 258 cshl_awk_tool 259 Extract genomic DNA 1 2.2.2 f t f -29 2013-02-14 259 Extract genomic DNA 1 2.2.2 266 Show beginning1 1.0.0 f t f -29 2013-02-14 260 259 Extract genomic DNA 1 2.2.2 f t f -29 2013-02-14 260 261 Extract genomic DNA 1 2.2.2 f t f -29 2013-02-14 260 265 trap f t f -29 2013-02-14 261 Extract genomic DNA 1 2.2.2 263 Show beginning1 1.0.0 f t f -29 2013-02-14 262 cshl_awk_tool 261 Extract genomic DNA 1 2.2.2 f t f -29 2013-02-14 263 Show beginning1 1.0.0 264 meme_meme 1.0.0 f t f -29 2013-02-14 267 Count1 1.0.0 268 barchart_gnuplot 1.0.0 f t f -31 2013-02-18 274 275 addValue 1.0.0 f f f -31 2013-02-18 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f -31 2013-02-18 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f -31 2013-02-18 274 275 addValue 1.0.0 f f f -31 2013-02-18 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f -31 2013-02-18 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f -31 2013-02-18 274 275 addValue 1.0.0 f f f -31 2013-02-18 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f -31 2013-02-18 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f -31 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/train_transformer.py Sun Oct 16 11:52:10 2022 +0000 @@ -0,0 +1,73 @@ +import tensorflow as tf +import transformer_network +import utils +from tensorflow.keras.layers import (Dense, Dropout, GlobalAveragePooling1D, + Input) +from tensorflow.keras.models import Model + + +def create_model(vocab_size, config): + embed_dim = config["embedding_dim"] + ff_dim = config["feed_forward_dim"] + max_len = config["maximum_path_length"] + dropout = config["dropout"] + + inputs = Input(shape=(max_len,)) + embedding_layer = transformer_network.TokenAndPositionEmbedding(max_len, vocab_size, embed_dim) + x = embedding_layer(inputs) + transformer_block = transformer_network.TransformerBlock(embed_dim, config["n_heads"], ff_dim) + x, weights = transformer_block(x) + x = GlobalAveragePooling1D()(x) + x = Dropout(dropout)(x) + x = Dense(ff_dim, activation="relu")(x) + x = Dropout(dropout)(x) + outputs = Dense(vocab_size, activation="sigmoid")(x) + return Model(inputs=inputs, outputs=[outputs, weights]) + + +def create_enc_transformer(train_data, train_labels, test_data, test_labels, f_dict, r_dict, c_wts, c_tools, pub_conn, tr_t_freq, config): + print("Train transformer...") + vocab_size = len(f_dict) + 1 + + enc_optimizer = tf.keras.optimizers.Adam(learning_rate=config["learning_rate"]) + + model = create_model(vocab_size, config) + + u_tr_y_labels, u_tr_y_labels_dict = utils.get_u_tr_labels(train_labels) + u_te_y_labels, u_te_y_labels_dict = utils.get_u_tr_labels(test_labels) + + trained_on_labels = [int(item) for item in list(u_tr_y_labels_dict.keys())] + + epo_tr_batch_loss = list() + epo_tr_batch_acc = list() + all_sel_tool_ids = list() + + te_lowest_t_ids = utils.get_low_freq_te_samples(test_data, test_labels, tr_t_freq) + tr_log_step = config["tr_logging_step"] + te_log_step = config["te_logging_step"] + n_train_steps = config["n_train_iter"] + te_batch_size = config["te_batch_size"] + tr_batch_size = config["tr_batch_size"] + sel_tools = list() + for batch in range(n_train_steps): + x_train, y_train, sel_tools = utils.sample_balanced_tr_y(train_data, train_labels, u_tr_y_labels_dict, tr_batch_size, tr_t_freq, sel_tools) + all_sel_tool_ids.extend(sel_tools) + with tf.GradientTape() as model_tape: + prediction, att_weights = model(x_train, training=True) + tr_loss, tr_cat_loss = utils.compute_loss(y_train, prediction) + tr_acc = tf.reduce_mean(utils.compute_acc(y_train, prediction)) + trainable_vars = model.trainable_variables + model_gradients = model_tape.gradient(tr_loss, trainable_vars) + enc_optimizer.apply_gradients(zip(model_gradients, trainable_vars)) + epo_tr_batch_loss.append(tr_loss.numpy()) + epo_tr_batch_acc.append(tr_acc.numpy()) + if (batch + 1) % tr_log_step == 0: + print("Total train data size: ", train_data.shape, train_labels.shape) + print("Batch train data size: ", x_train.shape, y_train.shape) + print("At Step {}/{} training loss:".format(str(batch + 1), str(n_train_steps))) + print(tr_loss.numpy()) + if (batch + 1) % te_log_step == 0: + print("Predicting on test data...") + utils.validate_model(test_data, test_labels, te_batch_size, model, f_dict, r_dict, u_te_y_labels_dict, trained_on_labels, te_lowest_t_ids) + print("Saving model after training for {} steps".format(n_train_steps)) + utils.save_model_file(model, r_dict, c_wts, c_tools, pub_conn, config["trained_model_path"])
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/transformer_network.py Sun Oct 16 11:52:10 2022 +0000 @@ -0,0 +1,39 @@ +import tensorflow as tf +from tensorflow.keras.layers import (Dense, Dropout, Embedding, Layer, + LayerNormalization, MultiHeadAttention) +from tensorflow.keras.models import Sequential + + +class TransformerBlock(Layer): + def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1): + super(TransformerBlock, self).__init__() + self.att = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, dropout=rate) + self.ffn = Sequential( + [Dense(ff_dim, activation="relu"), Dense(embed_dim)] + ) + self.layernorm1 = LayerNormalization(epsilon=1e-6) + self.layernorm2 = LayerNormalization(epsilon=1e-6) + self.dropout1 = Dropout(rate) + self.dropout2 = Dropout(rate) + + def call(self, inputs, training): + attn_output, attention_scores = self.att(inputs, inputs, inputs, return_attention_scores=True, training=training) + attn_output = self.dropout1(attn_output, training=training) + out1 = self.layernorm1(inputs + attn_output) + ffn_output = self.ffn(out1) + ffn_output = self.dropout2(ffn_output, training=training) + return self.layernorm2(out1 + ffn_output), attention_scores + + +class TokenAndPositionEmbedding(Layer): + def __init__(self, maxlen, vocab_size, embed_dim): + super(TokenAndPositionEmbedding, self).__init__() + self.token_emb = Embedding(input_dim=vocab_size, output_dim=embed_dim, mask_zero=True) + self.pos_emb = Embedding(input_dim=maxlen, output_dim=embed_dim, mask_zero=True) + + def call(self, x): + maxlen = tf.shape(x)[-1] + positions = tf.range(start=0, limit=maxlen, delta=1) + positions = self.pos_emb(positions) + x = self.token_emb(x) + return x + positions
--- a/utils.py Fri May 06 09:05:18 2022 +0000 +++ b/utils.py Sun Oct 16 11:52:10 2022 +0000 @@ -1,11 +1,15 @@ import json +import os import random import h5py import numpy as np +import pandas as pd import tensorflow as tf -from numpy.random import choice -from tensorflow.keras import backend + +binary_ce = tf.keras.losses.BinaryCrossentropy() +binary_acc = tf.keras.metrics.BinaryAccuracy() +categorical_ce = tf.keras.metrics.CategoricalCrossentropy(from_logits=True) def read_file(file_path): @@ -17,6 +21,43 @@ return file_content +def write_file(file_path, content): + """ + Write a file + """ + remove_file(file_path) + with open(file_path, "w") as json_file: + json_file.write(json.dumps(content)) + + +def save_h5_data(inp, tar, filename): + hf_file = h5py.File(filename, 'w') + hf_file.create_dataset("input", data=inp) + hf_file.create_dataset("target", data=tar) + hf_file.close() + + +def get_low_freq_te_samples(te_data, te_target, tr_freq_dict): + lowest_tool_te_ids = list() + lowest_t_ids = get_lowest_tools(tr_freq_dict) + for i, te_labels in enumerate(te_target): + tools_pos = np.where(te_labels > 0)[0] + tools_pos = [str(int(item)) for item in tools_pos] + intersection = list(set(tools_pos).intersection(set(lowest_t_ids))) + if len(intersection) > 0: + lowest_tool_te_ids.append(i) + lowest_t_ids = [item for item in lowest_t_ids if item not in intersection] + return lowest_tool_te_ids + + +def save_processed_workflows(file_path, unique_paths): + workflow_paths_unique = "" + for path in unique_paths: + workflow_paths_unique += path + "\n" + with open(file_path, "w") as workflows_file: + workflows_file.write(workflow_paths_unique) + + def format_tool_id(tool_link): """ Extract tool id from tool link @@ -26,158 +67,166 @@ return tool_id -def set_trained_model(dump_file, model_values): - """ - Create an h5 file with the trained weights and associated dicts - """ - hf_file = h5py.File(dump_file, "w") - for key in model_values: - value = model_values[key] - if key == "model_weights": - for idx, item in enumerate(value): - w_key = "weight_" + str(idx) - if w_key in hf_file: - hf_file.modify(w_key, item) - else: - hf_file.create_dataset(w_key, data=item) - else: - if key in hf_file: - hf_file.modify(key, json.dumps(value)) - else: - hf_file.create_dataset(key, data=json.dumps(value)) +def save_model_file(model, r_dict, c_wts, c_tools, s_conn, model_file): + model.save_weights(model_file, save_format="h5") + hf_file = h5py.File(model_file, 'r+') + model_values = { + "reverse_dict": r_dict, + "class_weights": c_wts, + "compatible_tools": c_tools, + "standard_connections": s_conn + } + for k in model_values: + hf_file.create_dataset(k, data=json.dumps(model_values[k])) hf_file.close() -def weighted_loss(class_weights): - """ - Create a weighted loss function. Penalise the misclassification - of classes more with the higher usage - """ - weight_values = list(class_weights.values()) - weight_values.extend(weight_values) +def remove_file(file_path): + if os.path.exists(file_path): + os.remove(file_path) + - def weighted_binary_crossentropy(y_true, y_pred): - # add another dimension to compute dot product - expanded_weights = tf.expand_dims(weight_values, axis=-1) - bce = backend.binary_crossentropy(y_true, y_pred) - return backend.dot(bce, expanded_weights) - - return weighted_binary_crossentropy +def verify_oversampling_freq(oversampled_tr_data, rev_dict): + """ + Compute the frequency of tool sequences after oversampling + """ + freq_dict = dict() + freq_dict_names = dict() + for tr_data in oversampled_tr_data: + t_pos = np.where(tr_data > 0)[0] + last_tool_id = str(int(tr_data[t_pos[-1]])) + if last_tool_id not in freq_dict: + freq_dict[last_tool_id] = 0 + freq_dict_names[rev_dict[int(last_tool_id)]] = 0 + freq_dict[last_tool_id] += 1 + freq_dict_names[rev_dict[int(last_tool_id)]] += 1 + s_freq = dict(sorted(freq_dict_names.items(), key=lambda kv: kv[1], reverse=True)) + return s_freq -def balanced_sample_generator( - train_data, train_labels, batch_size, l_tool_tr_samples, reverse_dictionary -): - while True: - dimension = train_data.shape[1] - n_classes = train_labels.shape[1] - tool_ids = list(l_tool_tr_samples.keys()) - random.shuffle(tool_ids) - generator_batch_data = np.zeros([batch_size, dimension]) - generator_batch_labels = np.zeros([batch_size, n_classes]) - generated_tool_ids = choice(tool_ids, batch_size) - for i in range(batch_size): - random_toolid = generated_tool_ids[i] - sample_indices = l_tool_tr_samples[str(random_toolid)] - random_index = random.sample(range(0, len(sample_indices)), 1)[0] - random_tr_index = sample_indices[random_index] - generator_batch_data[i] = train_data[random_tr_index] - generator_batch_labels[i] = train_labels[random_tr_index] - yield generator_batch_data, generator_batch_labels +def collect_sampled_tool_freq(collected_dict, c_freq): + for t in c_freq: + if t not in collected_dict: + collected_dict[t] = int(c_freq[t]) + else: + collected_dict[t] += int(c_freq[t]) + return collected_dict + + +def save_data_as_dict(f_dict, r_dict, inp, tar, save_path): + inp_tar = dict() + for index, (i, t) in enumerate(zip(inp, tar)): + i_pos = np.where(i > 0)[0] + i_seq = ",".join([str(int(item)) for item in i[1:i_pos[-1] + 1]]) + t_pos = np.where(t > 0)[0] + t_seq = ",".join([str(int(item)) for item in t[1:t_pos[-1] + 1]]) + if i_seq not in inp_tar: + inp_tar[i_seq] = list() + inp_tar[i_seq].append(t_seq) + size = 0 + for item in inp_tar: + size += len(inp_tar[item]) + print("Size saved file: ", size) + write_file(save_path, inp_tar) + + +def read_train_test(datapath): + file_obj = h5py.File(datapath, 'r') + data_input = np.array(file_obj["input"]) + data_target = np.array(file_obj["target"]) + return data_input, data_target -def compute_precision( - model, - x, - y, - reverse_data_dictionary, - usage_scores, - actual_classes_pos, - topk, - standard_conn, - last_tool_id, - lowest_tool_ids, -): - """ - Compute absolute and compatible precision - """ - pred_t_name = "" - top_precision = 0.0 - mean_usage = 0.0 - usage_wt_score = list() - pub_precision = 0.0 - lowest_pub_prec = 0.0 - lowest_norm_prec = 0.0 - pub_tools = list() - actual_next_tool_names = list() - test_sample = np.reshape(x, (1, len(x))) +def sample_balanced_tr_y(x_seqs, y_labels, ulabels_tr_y_dict, b_size, tr_t_freq, prev_sel_tools): + batch_y_tools = list(ulabels_tr_y_dict.keys()) + random.shuffle(batch_y_tools) + label_tools = list() + rand_batch_indices = list() + sel_tools = list() + + unselected_tools = [t for t in batch_y_tools if t not in prev_sel_tools] + rand_selected_tools = unselected_tools[:b_size] - # predict next tools for a test path - prediction = model.predict(test_sample, verbose=0) - - # divide the predicted vector into two halves - one for published and - # another for normal workflows - nw_dimension = prediction.shape[1] - half_len = int(nw_dimension / 2) + for l_tool in rand_selected_tools: + seq_indices = ulabels_tr_y_dict[l_tool] + random.shuffle(seq_indices) + rand_s_index = np.random.randint(0, len(seq_indices), 1)[0] + rand_sample = seq_indices[rand_s_index] + sel_tools.append(l_tool) + rand_batch_indices.append(rand_sample) + label_tools.append(l_tool) - # predict tools - prediction = np.reshape(prediction, (nw_dimension,)) - # get predictions of tools from published workflows - standard_pred = prediction[:half_len] - # get predictions of tools from normal workflows - normal_pred = prediction[half_len:] + x_batch_train = x_seqs[rand_batch_indices] + y_batch_train = y_labels[rand_batch_indices] - standard_prediction_pos = np.argsort(standard_pred, axis=-1) - standard_topk_prediction_pos = standard_prediction_pos[-topk] + unrolled_x = tf.convert_to_tensor(x_batch_train, dtype=tf.int64) + unrolled_y = tf.convert_to_tensor(y_batch_train, dtype=tf.int64) + return unrolled_x, unrolled_y, sel_tools + - normal_prediction_pos = np.argsort(normal_pred, axis=-1) - normal_topk_prediction_pos = normal_prediction_pos[-topk] +def sample_balanced_te_y(x_seqs, y_labels, ulabels_tr_y_dict, b_size): + batch_y_tools = list(ulabels_tr_y_dict.keys()) + random.shuffle(batch_y_tools) + label_tools = list() + rand_batch_indices = list() + sel_tools = list() + for l_tool in batch_y_tools: + seq_indices = ulabels_tr_y_dict[l_tool] + random.shuffle(seq_indices) + rand_s_index = np.random.randint(0, len(seq_indices), 1)[0] + rand_sample = seq_indices[rand_s_index] + sel_tools.append(l_tool) + if rand_sample not in rand_batch_indices: + rand_batch_indices.append(rand_sample) + label_tools.append(l_tool) + if len(rand_batch_indices) == b_size: + break + x_batch_train = x_seqs[rand_batch_indices] + y_batch_train = y_labels[rand_batch_indices] + + unrolled_x = tf.convert_to_tensor(x_batch_train, dtype=tf.int64) + unrolled_y = tf.convert_to_tensor(y_batch_train, dtype=tf.int64) + return unrolled_x, unrolled_y, sel_tools + - # get true tools names - for a_t_pos in actual_classes_pos: - if a_t_pos > half_len: - t_name = reverse_data_dictionary[int(a_t_pos - half_len)] - else: - t_name = reverse_data_dictionary[int(a_t_pos)] - actual_next_tool_names.append(t_name) - last_tool_name = reverse_data_dictionary[x[-1]] - # compute scores for published recommendations - if standard_topk_prediction_pos in reverse_data_dictionary: - pred_t_name = reverse_data_dictionary[int(standard_topk_prediction_pos)] - if last_tool_name in standard_conn: - pub_tools = standard_conn[last_tool_name] - if pred_t_name in pub_tools: - pub_precision = 1.0 - # count precision only when there is actually true published tools - if last_tool_id in lowest_tool_ids: - lowest_pub_prec = 1.0 - else: - lowest_pub_prec = np.nan - if standard_topk_prediction_pos in usage_scores: - usage_wt_score.append( - np.log(usage_scores[standard_topk_prediction_pos] + 1.0) - ) - else: - # count precision only when there is actually true published tools - # else set to np.nan. Set to 0 only when there is wrong prediction - pub_precision = np.nan - lowest_pub_prec = np.nan - # compute scores for normal recommendations - if normal_topk_prediction_pos in reverse_data_dictionary: - pred_t_name = reverse_data_dictionary[int(normal_topk_prediction_pos)] - if pred_t_name in actual_next_tool_names: - if normal_topk_prediction_pos in usage_scores: - usage_wt_score.append( - np.log(usage_scores[normal_topk_prediction_pos] + 1.0) - ) - top_precision = 1.0 - if last_tool_id in lowest_tool_ids: - lowest_norm_prec = 1.0 - else: - lowest_norm_prec = np.nan - if len(usage_wt_score) > 0: - mean_usage = np.mean(usage_wt_score) - return mean_usage, top_precision, pub_precision, lowest_pub_prec, lowest_norm_prec +def get_u_tr_labels(y_tr): + labels = list() + labels_pos_dict = dict() + for i, item in enumerate(y_tr): + label_pos = np.where(item > 0)[0] + labels.extend(label_pos) + for label in label_pos: + if label not in labels_pos_dict: + labels_pos_dict[label] = list() + labels_pos_dict[label].append(i) + u_labels = list(set(labels)) + for item in labels_pos_dict: + labels_pos_dict[item] = list(set(labels_pos_dict[item])) + return u_labels, labels_pos_dict + + +def compute_loss(y_true, y_pred, class_weights=None): + y_true = tf.cast(y_true, dtype=tf.float32) + loss = binary_ce(y_true, y_pred) + categorical_loss = categorical_ce(y_true, y_pred) + if class_weights is None: + return tf.reduce_mean(loss), categorical_loss + return tf.tensordot(loss, class_weights, axes=1), categorical_loss + + +def compute_acc(y_true, y_pred): + return binary_acc(y_true, y_pred) + + +def validate_model(te_x, te_y, te_batch_size, model, f_dict, r_dict, ulabels_te_dict, tr_labels, lowest_t_ids): + te_x_batch, y_train_batch, _ = sample_balanced_te_y(te_x, te_y, ulabels_te_dict, te_batch_size) + print("Total test data size: ", te_x.shape, te_y.shape) + print("Batch test data size: ", te_x_batch.shape, y_train_batch.shape) + te_pred_batch, _ = model(te_x_batch, training=False) + test_err, _ = compute_loss(y_train_batch, te_pred_batch) + print("Test loss:") + print(test_err.numpy()) + print("Test finished") def get_lowest_tools(l_tool_freq, fraction=0.25): @@ -187,98 +236,7 @@ return lowest_ids -def verify_model( - model, - x, - y, - reverse_data_dictionary, - usage_scores, - standard_conn, - lowest_tool_ids, - topk_list=[1, 2, 3], -): - """ - Verify the model on test data - """ - print("Evaluating performance on test data...") - print("Test data size: %d" % len(y)) - size = y.shape[0] - precision = np.zeros([len(y), len(topk_list)]) - usage_weights = np.zeros([len(y), len(topk_list)]) - epo_pub_prec = np.zeros([len(y), len(topk_list)]) - epo_lowest_tools_pub_prec = list() - epo_lowest_tools_norm_prec = list() - lowest_counter = 0 - # loop over all the test samples and find prediction precision - for i in range(size): - lowest_pub_topk = list() - lowest_norm_topk = list() - actual_classes_pos = np.where(y[i] > 0)[0] - test_sample = x[i, :] - last_tool_id = str(int(test_sample[-1])) - for index, abs_topk in enumerate(topk_list): - ( - usg_wt_score, - absolute_precision, - pub_prec, - lowest_p_prec, - lowest_n_prec, - ) = compute_precision( - model, - test_sample, - y, - reverse_data_dictionary, - usage_scores, - actual_classes_pos, - abs_topk, - standard_conn, - last_tool_id, - lowest_tool_ids, - ) - precision[i][index] = absolute_precision - usage_weights[i][index] = usg_wt_score - epo_pub_prec[i][index] = pub_prec - lowest_pub_topk.append(lowest_p_prec) - lowest_norm_topk.append(lowest_n_prec) - epo_lowest_tools_pub_prec.append(lowest_pub_topk) - epo_lowest_tools_norm_prec.append(lowest_norm_topk) - if last_tool_id in lowest_tool_ids: - lowest_counter += 1 - mean_precision = np.mean(precision, axis=0) - mean_usage = np.mean(usage_weights, axis=0) - mean_pub_prec = np.nanmean(epo_pub_prec, axis=0) - mean_lowest_pub_prec = np.nanmean(epo_lowest_tools_pub_prec, axis=0) - mean_lowest_norm_prec = np.nanmean(epo_lowest_tools_norm_prec, axis=0) - return ( - mean_usage, - mean_precision, - mean_pub_prec, - mean_lowest_pub_prec, - mean_lowest_norm_prec, - lowest_counter, - ) - - -def save_model( - results, - data_dictionary, - compatible_next_tools, - trained_model_path, - class_weights, - standard_connections, -): - # save files - trained_model = results["model"] - best_model_parameters = results["best_parameters"] - model_config = trained_model.to_json() - model_weights = trained_model.get_weights() - model_values = { - "data_dictionary": data_dictionary, - "model_config": model_config, - "best_parameters": best_model_parameters, - "model_weights": model_weights, - "compatible_tools": compatible_next_tools, - "class_weights": class_weights, - "standard_connections": standard_connections, - } - set_trained_model(trained_model_path, model_values) +def remove_pipe(file_path): + dataframe = pd.read_csv(file_path, sep="|", header=None) + dataframe = dataframe[1:len(dataframe.index) - 1] + return dataframe[1:]