Mercurial > repos > bgruening > create_tool_recommendation_model
changeset 3:5b3c08710e47 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
| author | bgruening |
|---|---|
| date | Sat, 09 May 2020 05:38:23 -0400 |
| parents | 76251d1ccdcc |
| children | afec8c595124 |
| 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_workflows utils.py |
| diffstat | 9 files changed, 1788 insertions(+), 1750 deletions(-) [+] |
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--- a/create_tool_recommendation_model.xml Fri Oct 11 18:24:54 2019 -0400 +++ b/create_tool_recommendation_model.xml Sat May 09 05:38:23 2020 -0400 @@ -1,4 +1,4 @@ -<tool id="create_tool_recommendation_model" name="Create a model to recommend tools" version="0.0.1"> +<tool id="create_tool_recommendation_model" name="Create a model to recommend tools" version="0.0.2"> <description>using deep learning</description> <requirements> <requirement type="package" version="3.6">python</requirement> @@ -21,7 +21,6 @@ --optimize_n_epochs '$training_parameters.optimize_n_epochs' --max_evals '$training_parameters.max_evals' --test_share '$training_parameters.test_share' - --validation_share '$training_parameters.validation_share' --batch_size '$nn_parameters.batch_size' --units '$nn_parameters.units' --embedding_size '$nn_parameters.embedding_size' @@ -29,8 +28,6 @@ --spatial_dropout '$nn_parameters.spatial_dropout' --recurrent_dropout '$nn_parameters.recurrent_dropout' --learning_rate '$nn_parameters.learning_rate' - --activation_recurrent '$nn_parameters.activation_recurrent' - --activation_output '$nn_parameters.activation_output' --output_model '$outfile_model' ]]> </command> @@ -45,23 +42,21 @@ </section> <section name="training_parameters" title="Training parameters" expanded="False"> - <param name="max_evals" type="integer" value="50" 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="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="20" 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="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="20" 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="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="0.0" 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="validation_share" type="float" value="0.2" label="Share of the validation data" help="Provide a real number between 0.0 and 1.0. This set of data is used to validate each step of learning while optimising the configurations of parameters. The minimum value can be 0.0 and maximum value should not be more than 0.5."/> + <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."/> </section> <section name="nn_parameters" title="Neural network parameters" expanded="False"> - <param name="batch_size" type="text" value="1,512" 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: 1,512." /> + <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="1,512" 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: 1,512."/> + <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="1,512" 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: 1,512."/> + <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"/> @@ -70,9 +65,6 @@ <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"/> - <param name="activation_recurrent" type="text" value="elu" label="Name of the activation function for recurrent layers" help="It is a mathematical function that transforms the input of recurrent layers to the following neural network layer."/> - - <param name="activation_output" type="text" value="sigmoid" label="Name of the activation function for output layer" help="It is a mathematical function that transforms the input of the last dense layer to the output of the neural network."/> </section> </inputs> <outputs> @@ -83,9 +75,10 @@ <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="test_share" value="0.1"/> + <param name="test_share" value="0.2"/> <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"/> @@ -96,9 +89,9 @@ <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="test_share" value="0.0"/> <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"/> @@ -108,7 +101,7 @@ <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="validation_share" value="0.0"/> + <param name="test_share" value="0.0"/> </test> <test expect_failure="true"> <param name="input_tabular_workflows" value="test_workflows" ftype="tabular"/> @@ -162,14 +155,14 @@ 1. The first file ("dataset containing workflows") contains tool connections for workflows in a tabular format. The workflows are arranged as pairs of tool connections. Each row is a pair of tool connections in a workflow as shown below: - ========== =================== ========= ================= ============= ========== ============ ============== - **wf_id** **wf_updated** **in_id** **in_tool** **in_tool_v** **out_id** **out_tool** **out_tool_v** - ---------- ------------------- --------- ----------------- ------------- ---------- ------------ -------------- - 3 2013-02-07 16:48:00 7 Remove beginning1 1.0.0 5 Grep1 1.0.1 - ========== =================== ========= ================= ============= ========== ============ ============== + ========== ================ ========== ============= ============= =========== ============= ============== ============== =========== ============= + **wf_id** **wf_updated** **in_id** **in_tool** **in_tool_v** **out_id** **out_tool** **out_tool_v** **published** **deleted** **has_error** + ---------- ---------------- ---------- ------------- ------------- ----------- ------------- -------------- -------------- ----------- ------------- + 3 2013-02-07 7 Cut1 1.0.0 5 Grep1 1.0.1 f t f + ========== ================ ========== ============= ============= =========== ============= ============== ============== =========== ============= -The first column (wf_id) is the workflow id, second (wf_updated) is the last updated date timestamp, third (in_id) is the id of the tool which is the input to the tool connection, fourth (in_tool) is the name of the input tool, fifth (in_tool_v) is the version of the input tool, sixth (out_id) is the id of the output tool in the tool connection, seventh (out_tool) is the name of the output tool and the last one (out_tool_v) is the version of the output tool. The tools connections (rows) for each workflow are used to recreate the workflow (directed acyclic graph) and unique tool sequences for each workflow are extracted. These tool sequences are then used to learn higher-order dependencies using a recurrent neural network to recommend tools. - +The first column (wf_id) is the workflow id, second (wf_updated) is the last updated date timestamp, third (in_id) is the id of the tool which is the input to the tool connection, fourth (in_tool) is the name of the input tool, fifth (in_tool_v) is the version of the input tool, sixth (out_id) is the id of the output tool in the tool connection, seventh (out_tool) is the name of the output tool and the last one (out_tool_v) is the version of the output tool. The tools connections (rows) for each workflow are used to recreate the workflow (directed acyclic graph) and unique tool sequences for each workflow are extracted. These tool sequences are then used to learn higher-order dependencies using a recurrent neural network to recommend tools. The last 3 columns give more information about workflows if they are published, non-deleted and has any errors. Collectively, they are useful to determine if the workflows are of good quality. + 2. The second file ("dataset containing usage frequencies of tools") is also a tabular file containing the usage frequencies of tools for a period of time. It has 3 columns: ============================================================================================ ========== === @@ -196,7 +189,6 @@ - "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. - - "validation_share": It specifies the size of the validation set. For example, if it is 0.5, then the validation set is half of the entire data available. It should not be set to more than 0.5. This set is used for computing error while training on the best configuration. 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. @@ -206,17 +198,15 @@ - "spatial_dropout": Similar to dropout, this is used to reduce overfitting in the embedding layer. This parameter should be optimised as well. - "recurrent_dropout": Similar to dropout and spatial dropout, this is used to reduce overfitting in the recurrent layers (hidden). This parameter should be optimised as well. - "learning_rate": The learning rate specifies the speed of learning. A higher value ensures fast learning (the optimiser may diverge) and a lower value causes slow learning (may not reach the optimum). This parameter should be optimised as well. - - "activation_recurrent": Activations are mathematical functions to transform input into output. This takes the name of an activation function from the list of Keras activations (https://keras.io/activations/) for recurrent layers. - - "activation_output": This takes the activation for transforming the input of the last layer to the output of the neural network. It is also taken from Keras activations (https://keras.io/activations/). ----- + **Output file** The output file (model) is an HDF5 file (http://docs.h5py.org/en/latest/high/file.html) containing multiple attributes like a dictionary of tools, neural network configuration and weights for each layer, weights of all tools and so on. After the tool has finished executing, it can be downloaded and placed at "/galaxy/database/" inside a Galaxy instance codebase. To see the recommended tools (enable the UI integrations) in Galaxy, the following changes should be made to "galaxy.yml" file: - Enable and then set the property "enable_tool_recommendation" to "true". - - Enable and then set the property "model_path" to "database/<<model_file_name>>". ]]> </help> @@ -225,7 +215,7 @@ @ARTICLE{anuprulez_galaxytools, Author = {Anup Kumar and Björn Grüning}, keywords = {bioinformatics, recommendation system, deep learning}, - title = {{Tool recommendation system for Galaxy workflows}}, + title = {{Tool recommendation system for Galaxy}}, url = {https://github.com/bgruening/galaxytools} } </citation>
--- a/extract_workflow_connections.py Fri Oct 11 18:24:54 2019 -0400 +++ b/extract_workflow_connections.py Sat May 09 05:38:23 2020 -0400 @@ -11,11 +11,17 @@ class ExtractWorkflowConnections: - @classmethod def __init__(self): """ Init method. """ - @classmethod + 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 read_tabular_file(self, raw_file_path): """ Read tabular file and extract workflow connections @@ -25,7 +31,8 @@ workflow_paths_dup = "" workflow_parents = dict() workflow_paths = list() - unique_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): @@ -35,7 +42,15 @@ 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((in_tool, out_tool)) + workflows[wf_id].append((out_tool, in_tool)) + qc = self.collect_standard_connections(row) + if qc: + i_t = utils.format_tool_id(in_tool) + o_t = utils.format_tool_id(out_tool) + if i_t not in standard_connections: + standard_connections[i_t] = list() + if o_t not in standard_connections[i_t]: + standard_connections[i_t].append(o_t) print("Processing workflows...") wf_ctr = 0 for wf_id in workflows: @@ -54,7 +69,6 @@ if len(paths) > 0: flow_paths.extend(paths) workflow_paths.extend(flow_paths) - print("Workflows processed: %d" % wf_ctr) # remove slashes from the tool ids @@ -75,9 +89,8 @@ print("Finding compatible next tools...") compatible_next_tools = self.set_compatible_next_tools(no_dup_paths) - return unique_paths, compatible_next_tools + return unique_paths, compatible_next_tools, standard_connections - @classmethod def set_compatible_next_tools(self, workflow_paths): """ Find next tools for each tool @@ -97,7 +110,6 @@ next_tools[tool] = ",".join(list(set(next_tools[tool].split(",")))) return next_tools - @classmethod def read_workflow(self, wf_id, workflow_rows): """ Read all connections for a workflow @@ -112,7 +124,6 @@ tool_parents[out_tool].append(in_tool) return tool_parents - @classmethod def get_roots_leaves(self, graph): roots = list() leaves = list() @@ -125,7 +136,6 @@ leaves = list(set(children).difference(set(all_parents))) return roots, leaves - @classmethod def find_tool_paths_workflow(self, graph, start, end, path=[]): path = path + [end] if start == end:
--- a/main.py Fri Oct 11 18:24:54 2019 -0400 +++ b/main.py Sat May 09 05:38:23 2020 -0400 @@ -20,7 +20,6 @@ class PredictTool: - @classmethod def __init__(self, num_cpus): """ Init method. """ # set the number of cpus @@ -32,47 +31,47 @@ ) K.set_session(tf.Session(config=cpu_config)) - @classmethod - def find_train_best_network(self, network_config, reverse_dictionary, train_data, train_labels, test_data, test_labels, n_epochs, class_weights, usage_pred, compatible_next_tools): + 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, l_tool_freq, l_tool_tr_samples): """ Define recurrent neural network and train sequential data """ + # get tools with lowest representation + lowest_tool_ids = utils.get_lowest_tools(l_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, class_weights) + best_params, best_model = hyper_opt.train_model(network_config, reverse_dictionary, train_data, train_labels, test_data, test_labels, l_tool_tr_samples, class_weights) # define callbacks - early_stopping = callbacks.EarlyStopping(monitor='loss', mode='min', verbose=1, min_delta=1e-4, restore_best_weights=True) - predict_callback_test = PredictCallback(test_data, test_labels, reverse_dictionary, n_epochs, compatible_next_tools, usage_pred) + 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() - if len(test_data) > 0: - trained_model = best_model.fit( + trained_model = best_model.fit_generator( + utils.balanced_sample_generator( train_data, train_labels, - batch_size=int(best_params["batch_size"]), - epochs=n_epochs, - verbose=2, - callbacks=callbacks_list, - shuffle="batch", - validation_data=(test_data, test_labels) - ) - 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 - else: - trained_model = best_model.fit( - train_data, - train_labels, - batch_size=int(best_params["batch_size"]), - epochs=n_epochs, - verbose=2, - callbacks=callbacks_list, - shuffle="batch" - ) + batch_size, + l_tool_tr_samples + ), + 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 @@ -80,26 +79,37 @@ class PredictCallback(callbacks.Callback): - def __init__(self, test_data, test_labels, reverse_data_dictionary, n_epochs, next_compatible_tools, usg_scores): + 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.next_compatible_tools = next_compatible_tools 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: - precision, usage_weights = utils.verify_model(self.model, self.test_data, self.test_labels, self.reverse_data_dictionary, self.next_compatible_tools, self.pred_usage_scores) + 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) - print("Epoch %d precision: %s" % (epoch + 1, precision)) + 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)) if __name__ == "__main__": @@ -116,7 +126,6 @@ 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("-vs", "--validation_share", required=True, help="share of data to be used for validation") # 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") @@ -125,8 +134,6 @@ 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("-ar", "--activation_recurrent", required=True, help="activation function for recurrent layers") - arg_parser.add_argument("-ao", "--activation_output", required=True, help="activation function for output layers") # get argument values args = vars(arg_parser.parse_args()) @@ -139,7 +146,6 @@ optimize_n_epochs = int(args["optimize_n_epochs"]) max_evals = int(args["max_evals"]) test_share = float(args["test_share"]) - validation_share = float(args["validation_share"]) batch_size = args["batch_size"] units = args["units"] embedding_size = args["embedding_size"] @@ -147,8 +153,6 @@ spatial_dropout = args["spatial_dropout"] recurrent_dropout = args["recurrent_dropout"] learning_rate = args["learning_rate"] - activation_recurrent = args["activation_recurrent"] - activation_output = args["activation_output"] num_cpus = 16 config = { @@ -158,35 +162,28 @@ 'optimize_n_epochs': optimize_n_epochs, 'max_evals': max_evals, 'test_share': test_share, - 'validation_share': validation_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, - 'activation_recurrent': activation_recurrent, - 'activation_output': activation_output + 'learning_rate': learning_rate } # Extract and process workflows connections = extract_workflow_connections.ExtractWorkflowConnections() - workflow_paths, compatible_next_tools = connections.read_tabular_file(workflows_path) + workflow_paths, compatible_next_tools, standard_connections = connections.read_tabular_file(workflows_path) # 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 = data.get_data_labels_matrices(workflow_paths, tool_usage_path, cutoff_date, compatible_next_tools) + train_data, train_labels, test_data, test_labels, data_dictionary, reverse_dictionary, class_weights, usage_pred, l_tool_freq, l_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, compatible_next_tools) - print() - print("Best parameters \n") - print(results_weighted["best_parameters"]) - print() - utils.save_model(results_weighted, data_dictionary, compatible_next_tools, trained_model_path, class_weights) + 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, l_tool_freq, l_tool_tr_samples) + utils.save_model(results_weighted, data_dictionary, compatible_next_tools, trained_model_path, class_weights, standard_connections) end_time = time.time() print() print("Program finished in %s seconds" % str(end_time - start_time))
--- a/optimise_hyperparameters.py Fri Oct 11 18:24:54 2019 -0400 +++ b/optimise_hyperparameters.py Sat May 09 05:38:23 2020 -0400 @@ -17,18 +17,13 @@ class HyperparameterOptimisation: - @classmethod def __init__(self): """ Init method. """ - @classmethod - def train_model(self, config, reverse_dictionary, train_data, train_labels, class_weights): + def train_model(self, config, reverse_dictionary, train_data, train_labels, test_data, test_labels, l_tool_tr_samples, class_weights): """ Train a model and report accuracy """ - l_recurrent_activations = config["activation_recurrent"].split(",") - l_output_activations = config["activation_output"].split(",") - # convert items to integer l_batch_size = list(map(int, config["batch_size"].split(","))) l_embedding_size = list(map(int, config["embedding_size"].split(","))) @@ -41,20 +36,17 @@ l_recurrent_dropout = list(map(float, config["recurrent_dropout"].split(","))) optimize_n_epochs = int(config["optimize_n_epochs"]) - validation_split = float(config["validation_share"]) # get dimensions dimensions = len(reverse_dictionary) + 1 best_model_params = dict() - early_stopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, min_delta=1e-4) + 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), - "activation_recurrent": hp.choice("activation_recurrent", l_recurrent_activations), - "activation_output": hp.choice("activation_output", l_output_activations), "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]), @@ -65,36 +57,36 @@ 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=params["activation_recurrent"])) + 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(GRU(int(params["units"]), dropout=params["dropout"], recurrent_dropout=params["recurrent_dropout"], return_sequences=False, activation=params["activation_recurrent"])) - model.add(Dropout(params["dropout"])) - model.add(Dense(dimensions, activation=params["activation_output"])) + 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) - model_fit = model.fit( - train_data, - train_labels, - batch_size=int(params["batch_size"]), + print(model.summary()) + model_fit = model.fit_generator( + utils.balanced_sample_generator( + train_data, + train_labels, + batch_size, + l_tool_tr_samples + ), + steps_per_epoch=len(train_data) // batch_size, epochs=optimize_n_epochs, - shuffle="batch", + callbacks=[early_stopping], + validation_data=(test_data, test_labels), verbose=2, - validation_split=validation_split, - callbacks=[early_stopping] + 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] - if item == 'activation_output': - best_model_params[item] = l_output_activations[item_val] - elif item == 'activation_recurrent': - best_model_params[item] = l_recurrent_activations[item_val] - else: - best_model_params[item] = item_val + best_model_params[item] = item_val return best_model_params, best_model
--- a/predict_tool_usage.py Fri Oct 11 18:24:54 2019 -0400 +++ b/predict_tool_usage.py Sat May 09 05:38:23 2020 -0400 @@ -21,11 +21,9 @@ class ToolPopularity: - @classmethod def __init__(self): """ Init method. """ - @classmethod def extract_tool_usage(self, tool_usage_file, cutoff_date, dictionary): """ Extract the tool usage over time for each tool @@ -63,7 +61,6 @@ tool_usage_dict[tool] = collections.OrderedDict(sorted(usage.items())) return tool_usage_dict - @classmethod def learn_tool_popularity(self, x_reshaped, y_reshaped): """ Fit a curve for the tool usage over time to predict future tool usage @@ -93,7 +90,6 @@ except Exception: return epsilon - @classmethod def get_pupularity_prediction(self, tools_usage): """ Get the popularity prediction for each tool
--- a/prepare_data.py Fri Oct 11 18:24:54 2019 -0400 +++ b/prepare_data.py Sat May 09 05:38:23 2020 -0400 @@ -10,19 +10,18 @@ import random import predict_tool_usage +import utils main_path = os.getcwd() class PrepareData: - @classmethod def __init__(self, max_seq_length, test_data_share): """ Init method. """ self.max_tool_sequence_len = max_seq_length self.test_share = test_data_share - @classmethod def process_workflow_paths(self, workflow_paths): """ Get all the tools and complete set of individual paths for each workflow @@ -40,7 +39,6 @@ tokens = np.reshape(tokens, [-1, ]) return tokens, raw_paths - @classmethod def create_new_dict(self, new_data_dict): """ Create new data dictionary @@ -48,7 +46,6 @@ reverse_dict = dict((v, k) for k, v in new_data_dict.items()) return new_data_dict, reverse_dict - @classmethod def assemble_dictionary(self, new_data_dict, old_data_dictionary={}): """ Create/update tools indices in the forward and backward dictionary @@ -56,7 +53,6 @@ new_data_dict, reverse_dict = self.create_new_dict(new_data_dict) return new_data_dict, reverse_dict - @classmethod def create_data_dictionary(self, words, old_data_dictionary={}): """ Create two dictionaries having tools names and their indexes @@ -68,7 +64,6 @@ dictionary, reverse_dictionary = self.assemble_dictionary(dictionary, old_data_dictionary) return dictionary, reverse_dictionary - @classmethod def decompose_paths(self, paths, dictionary): """ Decompose the paths to variable length sub-paths keeping the first tool fixed @@ -86,7 +81,6 @@ sub_paths_pos = list(set(sub_paths_pos)) return sub_paths_pos - @classmethod 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 @@ -116,8 +110,7 @@ paths_labels[item] = ",".join(list(set(paths_labels[item].split(",")))) return paths_labels - @classmethod - def pad_paths(self, paths_dictionary, num_classes): + def pad_test_paths(self, paths_dictionary, num_classes): """ Add padding to the tools sequences and create multi-hot encoded labels """ @@ -135,7 +128,35 @@ train_counter += 1 return data_mat, label_mat - @classmethod + 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 + 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 + train_counter += 1 + return data_mat, label_mat + def split_test_train_data(self, multilabels_paths): """ Split into test and train data randomly for each run @@ -152,15 +173,6 @@ train_dict[path] = multilabels_paths[path] return train_dict, test_dict - @classmethod - def verify_overlap(self, train_paths, test_paths): - """ - Verify the overlapping of samples in train and test data - """ - intersection = list(set(train_paths).intersection(set(test_paths))) - print("Overlap in train and test: %d" % len(intersection)) - - @classmethod def get_predicted_usage(self, data_dictionary, predicted_usage): """ Get predicted usage for tools @@ -180,43 +192,53 @@ continue return usage - @classmethod def assign_class_weights(self, n_classes, predicted_usage): """ Compute class weights using usage """ class_weights = dict() class_weights[str(0)] = 0.0 - for key in range(1, n_classes): + for key in range(1, n_classes + 1): u_score = predicted_usage[key] if u_score < 1.0: u_score += 1.0 - class_weights[key] = np.log(u_score) + class_weights[key] = np.round(np.log(u_score), 6) return class_weights - @classmethod - def get_sample_weights(self, train_data, reverse_dictionary, paths_frequency): + def get_train_last_tool_freq(self, train_paths, reverse_dictionary): """ - Compute the frequency of paths in training data + Get the frequency of last tool of each tool sequence + to estimate the frequency of tool sequences """ - path_weights = np.zeros(len(train_data)) - for path_index, path in enumerate(train_data): - sample_pos = np.where(path > 0)[0] - sample_tool_pos = path[sample_pos[0]:] - path_name = ",".join([reverse_dictionary[int(tool_pos)] for tool_pos in sample_tool_pos]) - try: - path_weights[path_index] = int(paths_frequency[path_name]) - except Exception: - path_weights[path_index] = 1 - return path_weights + last_tool_freq = dict() + inv_freq = dict() + for path in train_paths: + last_tool = path.split(",")[-1] + if last_tool not in last_tool_freq: + last_tool_freq[last_tool] = 0 + last_tool_freq[last_tool] += 1 + max_freq = max(last_tool_freq.values()) + for t in last_tool_freq: + inv_freq[t] = int(np.round(max_freq / float(last_tool_freq[t]), 0)) + return last_tool_freq, inv_freq - @classmethod - def get_data_labels_matrices(self, workflow_paths, tool_usage_path, cutoff_date, compatible_next_tools, old_data_dictionary={}): + def get_toolid_samples(self, train_data, l_tool_freq): + l_tool_tr_samples = dict() + for tool_id in l_tool_freq: + for index, tr_sample in enumerate(train_data): + last_tool_id = str(int(tr_sample[-1])) + if last_tool_id == tool_id: + if last_tool_id not in l_tool_tr_samples: + l_tool_tr_samples[last_tool_id] = list() + 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={}): """ Convert the training and test paths into corresponding numpy matrices """ processed_data, raw_paths = self.process_workflow_paths(workflow_paths) - dictionary, reverse_dictionary = 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)) @@ -227,25 +249,32 @@ random.shuffle(all_unique_paths) print("Creating dictionaries...") - multilabels_paths = self.prepare_paths_labels_dictionary(dictionary, reverse_dictionary, all_unique_paths, compatible_next_tools) + multilabels_paths = self.prepare_paths_labels_dictionary(dictionary, rev_dict, all_unique_paths, compatible_next_tools) print("Complete data: %d" % len(multilabels_paths)) train_paths_dict, test_paths_dict = self.split_test_train_data(multilabels_paths) + # get sample frequency + l_tool_freq, inv_last_tool_freq = self.get_train_last_tool_freq(train_paths_dict, rev_dict) + print("Train data: %d" % len(train_paths_dict)) print("Test data: %d" % len(test_paths_dict)) - test_data, test_labels = self.pad_paths(test_paths_dict, num_classes) - train_data, train_labels = self.pad_paths(train_paths_dict, num_classes) + 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) + + l_tool_tr_samples = self.get_toolid_samples(train_data, l_tool_freq) # 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) tool_usage_prediction = usage_pred.get_pupularity_prediction(usage) - tool_predicted_usage = self.get_predicted_usage(dictionary, tool_usage_prediction) + 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(train_labels.shape[1], tool_predicted_usage) + class_weights = self.assign_class_weights(num_classes, t_pred_usage) - return train_data, train_labels, test_data, test_labels, dictionary, reverse_dictionary, class_weights, tool_predicted_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
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16:00:00 1024 meme_meme 1.0.0 1023 tab2fasta 1.1.0 -116 2013-02-20 16:00:00 1033 gops_subtract_1 1.0.0 1032 cshl_awk_tool 1.0.0 -116 2013-02-20 16:00:00 1037 gops_intersect_1 1.0.0 1032 cshl_awk_tool 1.0.0 -116 2013-02-20 16:00:00 1040 Extract genomic DNA 1 2.2.2 1033 gops_subtract_1 1.0.0 -116 2013-02-20 16:00:00 1051 Count1 1.0.0 1033 gops_subtract_1 1.0.0 -116 2013-02-20 16:00:00 1033 gops_subtract_1 1.0.0 1034 cshl_awk_tool 1.0.0 -116 2013-02-20 16:00:00 1037 gops_intersect_1 1.0.0 1034 cshl_awk_tool 1.0.0 -116 2013-02-20 16:00:00 1034 cshl_awk_tool 1.0.0 1035 get_flanks1 1.0.0 -116 2013-02-20 16:00:00 1035 get_flanks1 1.0.0 1036 -116 2013-02-20 16:00:00 1040 Extract genomic DNA 1 2.2.2 1039 -116 2013-02-20 16:00:00 1041 fasta2tab 1.1.0 1040 Extract genomic DNA 1 2.2.2 -116 2013-02-20 16:00:00 1044 meme_meme 1.0.0 1043 tab2fasta 1.1.0 -116 2013-02-20 16:00:00 1050 barchart_gnuplot 1.0.0 1049 Count1 1.0.0 -117 2013-02-20 16:00:00 1054 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f t f +5 2013-02-07 16:49:04.367628 40 gops_coverage_1 1.0.0 44 Filter1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 40 gops_coverage_1 1.0.0 43 Filter1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 40 gops_coverage_1 1.0.0 42 Filter1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 40 gops_coverage_1 1.0.0 41 cshl_grep_tool 1.0.0 f t f +5 2013-02-07 16:49:04.367628 40 gops_coverage_1 1.0.0 39 Filter1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 41 cshl_grep_tool 1.0.0 52 Summary_Statistics1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 41 cshl_grep_tool 1.0.0 51 Summary_Statistics1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 42 Filter1 1.1.0 50 Summary_Statistics1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 42 Filter1 1.1.0 49 Summary_Statistics1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 43 Filter1 1.1.0 56 Summary_Statistics1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 43 Filter1 1.1.0 55 Summary_Statistics1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 44 Filter1 1.1.0 54 Summary_Statistics1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 44 Filter1 1.1.0 53 Summary_Statistics1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 45 Summary_Statistics1 1.1.0 57 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 46 Summary_Statistics1 1.1.0 58 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 47 addValue 1.0.0 26 cat1 1.0.0 f t f +5 2013-02-07 16:49:04.367628 48 addValue 1.0.0 26 cat1 1.0.0 f t f +5 2013-02-07 16:49:04.367628 49 Summary_Statistics1 1.1.0 32 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 50 Summary_Statistics1 1.1.0 31 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 51 Summary_Statistics1 1.1.0 34 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 52 Summary_Statistics1 1.1.0 33 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 53 Summary_Statistics1 1.1.0 28 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 54 Summary_Statistics1 1.1.0 27 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 55 Summary_Statistics1 1.1.0 30 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 56 Summary_Statistics1 1.1.0 29 Cut1 1.0.1 f t f +5 2013-02-07 16:49:04.367628 57 Cut1 1.0.1 35 Paste1 1.0.0 f t f +5 2013-02-07 16:49:04.367628 58 Cut1 1.0.1 35 Paste1 1.0.0 f t f +5 2013-02-07 16:49:04.367628 59 Paste1 1.0.0 62 Add_a_column1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 60 Paste1 1.0.0 63 Add_a_column1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 61 Add_a_column1 1.1.0 25 addValue 1.0.0 f t f +5 2013-02-07 16:49:04.367628 62 Add_a_column1 1.1.0 38 addValue 1.0.0 f t f +5 2013-02-07 16:49:04.367628 63 Add_a_column1 1.1.0 37 addValue 1.0.0 f t f +5 2013-02-07 16:49:04.367628 64 Add_a_column1 1.1.0 47 addValue 1.0.0 f t f +5 2013-02-07 16:49:04.367628 65 Add_a_column1 1.1.0 48 addValue 1.0.0 f t f +5 2013-02-07 16:49:04.367628 66 Paste1 1.0.0 61 Add_a_column1 1.1.0 f t f +5 2013-02-07 16:49:04.367628 68 40 gops_coverage_1 1.0.0 f t f +5 2013-02-07 16:49:04.367628 69 40 gops_coverage_1 1.0.0 f t f +6 2013-02-07 16:57:18.016313 71 70 cshl_awk_tool1 1.0.0 f t f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 t f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f t f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 82 Grep1 1.0.1 84 Remove beginning1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 t f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f t f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 83 Cut1 1.0.1 85 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 t f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f t f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 86 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 t f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f t f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 84 Remove beginning1 1.0.0 83 Cut1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 t f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f t f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 85 addValue 1.0.0 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 t f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f t f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 86 Cut1 1.0.1 88 Paste1 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 t f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f t f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 88 Paste1 1.0.0 87 addValue 1.0.0 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 t f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f t f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +9 2013-02-07 16:59:41.215802 89 82 Grep1 1.0.1 f f f +11 2013-02-07 17:03:54.795316 135 addValue 1.0.0 136 cat1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 136 cat1 1.0.0 145 barchart_gnuplot 1.0.0 f t f +11 2013-02-07 17:03:54.795316 137 Cut1 1.0.1 170 Paste1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 138 Cut1 1.0.1 170 Paste1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 139 Cut1 1.0.1 177 Paste1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 140 Cut1 1.0.1 177 Paste1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 141 Cut1 1.0.1 147 Paste1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 142 Cut1 1.0.1 147 Paste1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 143 Cut1 1.0.1 171 Paste1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 144 Cut1 1.0.1 171 Paste1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 146 Paste1 1.0.0 176 Add_a_column1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 147 Paste1 1.0.0 175 Add_a_column1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 148 addValue 1.0.0 136 cat1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 149 addValue 1.0.0 136 cat1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 150 Filter1 1.1.0 157 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 150 Filter1 1.1.0 156 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 151 gops_coverage_1 1.0.0 155 Filter1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 151 gops_coverage_1 1.0.0 154 Filter1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 151 gops_coverage_1 1.0.0 153 Filter1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 151 gops_coverage_1 1.0.0 152 cshl_grep_tool 1.0.0 f t f +11 2013-02-07 17:03:54.795316 151 gops_coverage_1 1.0.0 150 Filter1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 152 cshl_grep_tool 1.0.0 163 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 152 cshl_grep_tool 1.0.0 162 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 153 Filter1 1.1.0 161 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 153 Filter1 1.1.0 160 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 154 Filter1 1.1.0 167 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 154 Filter1 1.1.0 166 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 155 Filter1 1.1.0 165 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 155 Filter1 1.1.0 164 Summary_Statistics1 1.1.0 f t f +11 2013-02-07 17:03:54.795316 156 Summary_Statistics1 1.1.0 168 Cut1 1.0.1 f t f +11 2013-02-07 17:03:54.795316 157 Summary_Statistics1 1.1.0 169 Cut1 1.0.1 f t f +11 2013-02-07 17:03:54.795316 158 addValue 1.0.0 136 cat1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 159 addValue 1.0.0 136 cat1 1.0.0 f t f +11 2013-02-07 17:03:54.795316 160 Summary_Statistics1 1.1.0 142 Cut1 1.0.1 f t f +11 2013-02-07 17:03:54.795316 161 Summary_Statistics1 1.1.0 141 Cut1 1.0.1 f t f +11 2013-02-07 17:03:54.795316 162 Summary_Statistics1 1.1.0 144 Cut1 1.0.1 f t f +11 2013-02-07 17:03:54.795316 163 Summary_Statistics1 1.1.0 143 Cut1 1.0.1 f t f +11 2013-02-07 17:03:54.795316 164 Summary_Statistics1 1.1.0 138 Cut1 1.0.1 f t f +11 2013-02-07 17:03:54.795316 165 Summary_Statistics1 1.1.0 137 Cut1 1.0.1 f t f +11 2013-02-07 17:03:54.795316 166 Summary_Statistics1 1.1.0 140 Cut1 1.0.1 f t f +11 2013-02-07 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17:04:38.354863 186 cshl_awk_tool 188 cshl_uniq_tool 1.0.0 f t f +12 2013-02-07 17:04:38.354863 187 Count1 1.0.0 180 cat1 1.0.0 t t f +12 2013-02-07 17:04:38.354863 187 Count1 1.0.0 180 cat1 1.0.0 f t f +12 2013-02-07 17:04:38.354863 188 cshl_uniq_tool 1.0.0 187 Count1 1.0.0 t t f +12 2013-02-07 17:04:38.354863 188 cshl_uniq_tool 1.0.0 187 Count1 1.0.0 f t f +12 2013-02-07 17:04:38.354863 190 182 bedtools_intersectBed t t f +12 2013-02-07 17:04:38.354863 190 182 bedtools_intersectBed f t f +12 2013-02-07 17:04:38.354863 190 181 bedtools_intersectBed t t f +12 2013-02-07 17:04:38.354863 190 181 bedtools_intersectBed f t f +12 2013-02-07 17:04:38.354863 191 182 bedtools_intersectBed t t f +12 2013-02-07 17:04:38.354863 191 182 bedtools_intersectBed f t f +12 2013-02-07 17:04:38.354863 191 181 bedtools_intersectBed t t f +12 2013-02-07 17:04:38.354863 191 181 bedtools_intersectBed f t f +19 2013-02-14 15:51:02.598429 193 194 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f t f +19 2013-02-14 15:51:02.598429 199 205 gops_subtract_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 199 205 gops_subtract_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 199 200 gops_intersect_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 199 200 gops_intersect_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 199 200 gops_intersect_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 199 200 gops_intersect_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 200 gops_intersect_1 1.0.0 215 Count1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 200 gops_intersect_1 1.0.0 215 Count1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 200 gops_intersect_1 1.0.0 215 Count1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 200 gops_intersect_1 1.0.0 215 Count1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 200 gops_intersect_1 1.0.0 210 cshl_awk_tool f t f +19 2013-02-14 15:51:02.598429 200 gops_intersect_1 1.0.0 210 cshl_awk_tool f t f +19 2013-02-14 15:51:02.598429 200 gops_intersect_1 1.0.0 210 cshl_awk_tool f t f +19 2013-02-14 15:51:02.598429 200 gops_intersect_1 1.0.0 210 cshl_awk_tool f t f +19 2013-02-14 15:51:02.598429 201 cshl_find_and_replace 1.0.0 205 gops_subtract_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 201 cshl_find_and_replace 1.0.0 205 gops_subtract_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 201 cshl_find_and_replace 1.0.0 205 gops_subtract_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 201 cshl_find_and_replace 1.0.0 205 gops_subtract_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 201 cshl_find_and_replace 1.0.0 200 gops_intersect_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 201 cshl_find_and_replace 1.0.0 200 gops_intersect_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 201 cshl_find_and_replace 1.0.0 200 gops_intersect_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 201 cshl_find_and_replace 1.0.0 200 gops_intersect_1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 202 get_flanks1 1.0.0 201 cshl_find_and_replace 1.0.0 f t f +19 2013-02-14 15:51:02.598429 202 get_flanks1 1.0.0 201 cshl_find_and_replace 1.0.0 f t f +19 2013-02-14 15:51:02.598429 202 get_flanks1 1.0.0 201 cshl_find_and_replace 1.0.0 f t f +19 2013-02-14 15:51:02.598429 202 get_flanks1 1.0.0 201 cshl_find_and_replace 1.0.0 f t f +19 2013-02-14 15:51:02.598429 203 204 cshl_grep_tool 1.0.0 f t f +19 2013-02-14 15:51:02.598429 203 204 cshl_grep_tool 1.0.0 f t f +19 2013-02-14 15:51:02.598429 203 204 cshl_grep_tool 1.0.0 f t f +19 2013-02-14 15:51:02.598429 203 204 cshl_grep_tool 1.0.0 f t f +19 2013-02-14 15:51:02.598429 203 202 get_flanks1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 203 202 get_flanks1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 203 202 get_flanks1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 203 202 get_flanks1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 205 gops_subtract_1 1.0.0 206 cshl_awk_tool f t f +19 2013-02-14 15:51:02.598429 205 gops_subtract_1 1.0.0 206 cshl_awk_tool f t f +19 2013-02-14 15:51:02.598429 205 gops_subtract_1 1.0.0 206 cshl_awk_tool f t f +19 2013-02-14 15:51:02.598429 205 gops_subtract_1 1.0.0 206 cshl_awk_tool f t f +19 2013-02-14 15:51:02.598429 206 cshl_awk_tool 207 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 206 cshl_awk_tool 207 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 206 cshl_awk_tool 207 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 206 cshl_awk_tool 207 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 207 Extract genomic DNA 1 2.2.2 214 Show beginning1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 207 Extract genomic DNA 1 2.2.2 214 Show beginning1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 207 Extract genomic DNA 1 2.2.2 214 Show beginning1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 207 Extract genomic DNA 1 2.2.2 214 Show beginning1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 208 213 trap f t f +19 2013-02-14 15:51:02.598429 208 213 trap f t f +19 2013-02-14 15:51:02.598429 208 213 trap f t f +19 2013-02-14 15:51:02.598429 208 213 trap f t f +19 2013-02-14 15:51:02.598429 208 209 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 208 209 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 208 209 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 208 209 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 208 207 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 208 207 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 208 207 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 208 207 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 209 Extract genomic DNA 1 2.2.2 211 Show beginning1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 209 Extract genomic DNA 1 2.2.2 211 Show beginning1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 209 Extract genomic DNA 1 2.2.2 211 Show beginning1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 209 Extract genomic DNA 1 2.2.2 211 Show beginning1 1.0.0 f t f +19 2013-02-14 15:51:02.598429 210 cshl_awk_tool 209 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 210 cshl_awk_tool 209 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 210 cshl_awk_tool 209 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 210 cshl_awk_tool 209 Extract genomic DNA 1 2.2.2 f t f +19 2013-02-14 15:51:02.598429 211 Show beginning1 1.0.0 212 meme_meme 1.0.0 f t f +19 2013-02-14 15:51:02.598429 211 Show beginning1 1.0.0 212 meme_meme 1.0.0 f t f +19 2013-02-14 15:51:02.598429 211 Show beginning1 1.0.0 212 meme_meme 1.0.0 f t f +19 2013-02-14 15:51:02.598429 211 Show beginning1 1.0.0 212 meme_meme 1.0.0 f t f +19 2013-02-14 15:51:02.598429 215 Count1 1.0.0 216 barchart_gnuplot 1.0.0 f t f +19 2013-02-14 15:51:02.598429 215 Count1 1.0.0 216 barchart_gnuplot 1.0.0 f t f +19 2013-02-14 15:51:02.598429 215 Count1 1.0.0 216 barchart_gnuplot 1.0.0 f t f +19 2013-02-14 15:51:02.598429 215 Count1 1.0.0 216 barchart_gnuplot 1.0.0 f t f +28 2013-02-14 16:08:08.300397 242 243 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cshl_find_and_replace 1.0.0 f t f +29 2013-02-14 17:12:23.558236 255 256 cshl_grep_tool 1.0.0 f t f +29 2013-02-14 17:12:23.558236 255 254 get_flanks1 1.0.0 f t f +29 2013-02-14 17:12:23.558236 257 gops_subtract_1 1.0.0 258 cshl_awk_tool f t f +29 2013-02-14 17:12:23.558236 258 cshl_awk_tool 259 Extract genomic DNA 1 2.2.2 f t f +29 2013-02-14 17:12:23.558236 259 Extract genomic DNA 1 2.2.2 266 Show beginning1 1.0.0 f t f +29 2013-02-14 17:12:23.558236 260 265 trap f t f +29 2013-02-14 17:12:23.558236 260 261 Extract genomic DNA 1 2.2.2 f t f +29 2013-02-14 17:12:23.558236 260 259 Extract genomic DNA 1 2.2.2 f t f +29 2013-02-14 17:12:23.558236 261 Extract genomic DNA 1 2.2.2 263 Show beginning1 1.0.0 f t f +29 2013-02-14 17:12:23.558236 262 cshl_awk_tool 261 Extract genomic DNA 1 2.2.2 f t f +29 2013-02-14 17:12:23.558236 263 Show beginning1 1.0.0 264 meme_meme 1.0.0 f t f +29 2013-02-14 17:12:23.558236 267 Count1 1.0.0 268 barchart_gnuplot 1.0.0 f t f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f t f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f t f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f t f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f t f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f t f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 274 275 addValue 1.0.0 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f t f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f t f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 addValue 1.0.0 276 mergeCols1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 275 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11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f t f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f t f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f t f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 f f f +31 2013-02-18 11:33:27.210212 276 mergeCols1 1.0.1 277 Cut1 1.0.1 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18:11:23.558371 286 Count1 1.0.0 287 barchart_gnuplot 1.0.0 f f f +34 2013-02-18 11:26:21.436462 288 289 cshl_awk_tool 1.0.0 f t f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f t f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 cshl_awk_tool 1.0.0 f f f +36 2013-02-18 11:32:52.783849 293 292 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+97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f t f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 t t f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f t f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 703 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 t t f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f t f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 722 Count1 1.0.0 t t f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f t f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 704 gops_subtract_1 1.0.0 711 Extract genomic DNA 1 2.2.2 t t f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f t f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 708 gops_intersect_1 1.0.0 t t f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f t f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 705 cshl_awk_tool 1.0.0 704 gops_subtract_1 1.0.0 t t f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f t f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 f f f +97 2013-02-20 10:11:21.312214 706 get_flanks1 1.0.0 705 cshl_awk_tool 1.0.0 t t f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f t f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 707 706 get_flanks1 1.0.0 t t f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f t f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 720 Count1 1.0.0 t t f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f t f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 708 gops_intersect_1 1.0.0 709 Extract genomic DNA 1 2.2.2 t t f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f t f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 709 Extract genomic DNA 1 2.2.2 716 fasta2tab 1.1.0 t t f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f t f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 711 Extract genomic DNA 1 2.2.2 t t f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f t f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 f f f +97 2013-02-20 10:11:21.312214 710 709 Extract genomic DNA 1 2.2.2 t t f +97 2013-02-20 10:11:21.312214 711 Extract genomic DNA 1 2.2.2 712 fasta2tab 1.1.0 f f f +97 2013-02-20 10:11:21.312214 711 Extract genomic DNA 1 2.2.2 712 fasta2tab 1.1.0 f f f
--- a/utils.py Fri Oct 11 18:24:54 2019 -0400 +++ b/utils.py Sat May 09 05:38:23 2020 -0400 @@ -2,6 +2,7 @@ import numpy as np import json import h5py +import random from keras import backend as K @@ -15,23 +16,6 @@ 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_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 @@ -63,17 +47,13 @@ hf_file.close() -def remove_file(file_path): - if os.path.exists(file_path): - os.remove(file_path) - - 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 weighted_binary_crossentropy(y_true, y_pred): # add another dimension to compute dot product @@ -82,46 +62,101 @@ return weighted_binary_crossentropy -def compute_precision(model, x, y, reverse_data_dictionary, next_compatible_tools, usage_scores, actual_classes_pos, topk): +def balanced_sample_generator(train_data, train_labels, batch_size, l_tool_tr_samples): + while True: + dimension = train_data.shape[1] + n_classes = train_labels.shape[1] + tool_ids = list(l_tool_tr_samples.keys()) + generator_batch_data = np.zeros([batch_size, dimension]) + generator_batch_labels = np.zeros([batch_size, n_classes]) + for i in range(batch_size): + random_toolid_index = random.sample(range(0, len(tool_ids)), 1)[0] + random_toolid = tool_ids[random_toolid_index] + 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 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 """ - absolute_precision = 0.0 + 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))) # 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] - - # remove the 0th position as there is no tool at this index - prediction = np.reshape(prediction, (nw_dimension,)) + half_len = int(nw_dimension / 2) - prediction_pos = np.argsort(prediction, axis=-1) - topk_prediction_pos = prediction_pos[-topk:] + # 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:] - # remove the wrong tool position from the predicted list of tool positions - topk_prediction_pos = [x for x in topk_prediction_pos if x > 0] + standard_prediction_pos = np.argsort(standard_pred, axis=-1) + standard_topk_prediction_pos = standard_prediction_pos[-topk] + + normal_prediction_pos = np.argsort(normal_pred, axis=-1) + normal_topk_prediction_pos = normal_prediction_pos[-topk] - # read tool names using reverse dictionary - actual_next_tool_names = [reverse_data_dictionary[int(tool_pos)] for tool_pos in actual_classes_pos] - top_predicted_next_tool_names = [reverse_data_dictionary[int(tool_pos)] for tool_pos in topk_prediction_pos] - - # compute the class weights of predicted tools - mean_usg_score = 0 - usg_wt_scores = list() - for t_id in topk_prediction_pos: - t_name = reverse_data_dictionary[int(t_id)] - if t_id in usage_scores and t_name in actual_next_tool_names: - usg_wt_scores.append(np.log(usage_scores[t_id] + 1.0)) - if len(usg_wt_scores) > 0: - mean_usg_score = np.sum(usg_wt_scores) / float(topk) - false_positives = [tool_name for tool_name in top_predicted_next_tool_names if tool_name not in actual_next_tool_names] - absolute_precision = 1 - (len(false_positives) / float(topk)) - return mean_usg_score, absolute_precision + # 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 + if last_tool_id in lowest_tool_ids: + lowest_pub_prec = 1.0 + if standard_topk_prediction_pos in usage_scores: + usage_wt_score.append(np.log(usage_scores[standard_topk_prediction_pos] + 1.0)) + # 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 + 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 verify_model(model, x, y, reverse_data_dictionary, next_compatible_tools, usage_scores, topk_list=[1, 2, 3]): +def get_lowest_tools(l_tool_freq, fraction=0.25): + l_tool_freq = dict(sorted(l_tool_freq.items(), key=lambda kv: kv[1], reverse=True)) + tool_ids = list(l_tool_freq.keys()) + lowest_ids = tool_ids[-int(len(tool_ids) * fraction):] + 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 """ @@ -130,31 +165,49 @@ 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() + # 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): - abs_mean_usg_score, absolute_precision = compute_precision(model, x[i, :], y, reverse_data_dictionary, next_compatible_tools, usage_scores, actual_classes_pos, abs_topk) + 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] = abs_mean_usg_score + usage_weights[i][index] = usg_wt_score + epo_pub_prec[i][index] = pub_prec + if last_tool_id in lowest_tool_ids: + lowest_pub_topk.append(lowest_p_prec) + lowest_norm_topk.append(lowest_n_prec) + if last_tool_id in lowest_tool_ids: + epo_lowest_tools_pub_prec.append(lowest_pub_topk) + epo_lowest_tools_norm_prec.append(lowest_norm_topk) mean_precision = np.mean(precision, axis=0) mean_usage = np.mean(usage_weights, axis=0) - return mean_precision, mean_usage + mean_pub_prec = np.mean(epo_pub_prec, axis=0) + mean_lowest_pub_prec = np.mean(epo_lowest_tools_pub_prec, axis=0) + mean_lowest_norm_prec = np.mean(epo_lowest_tools_norm_prec, axis=0) + return mean_usage, mean_precision, mean_pub_prec, mean_lowest_pub_prec, mean_lowest_norm_prec, len(epo_lowest_tools_pub_prec) -def save_model(results, data_dictionary, compatible_next_tools, trained_model_path, class_weights): +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 + "class_weights": class_weights, + "standard_connections": standard_connections } set_trained_model(trained_model_path, model_values)