--- a/utils.py	Wed Sep 25 06:42:18 2019 -0400
+++ b/utils.py	Sat May 09 09:38:04 2020 +0000
@@ -2,12 +2,8 @@
 import numpy as np
 import json
 import h5py
+import random
 
-from keras.models import model_from_json, Sequential
-from keras.layers import Dense, GRU, Dropout
-from keras.layers.embeddings import Embedding
-from keras.layers.core import SpatialDropout1D
-from keras.optimizers import RMSprop
 from keras import backend as K
 
 
@@ -20,34 +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 load_saved_model(model_config, model_weights):
-    """
-    Load the saved trained model using the saved network and its weights
-    """
-    # load the network
-    loaded_model = model_from_json(model_config)
-    # load the saved weights into the model
-    loaded_model.set_weights(model_weights)
-    return loaded_model
-
-
 def format_tool_id(tool_link):
     """
     Extract tool id from tool link
@@ -57,22 +25,6 @@
     return tool_id
 
 
-def get_HDF5(hf, d_key):
-    """
-    Read h5 file to get train and test data
-    """
-    return hf.get(d_key).value
-
-
-def save_HDF5(hf_file, d_key, data, d_type=""):
-    """
-    Save datasets as h5 file
-    """
-    if (d_type == 'json'):
-        data = json.dumps(data)
-    hf_file.create_dataset(d_key, data=data)
-
-
 def set_trained_model(dump_file, model_values):
     """
     Create an h5 file with the trained weights and associated dicts
@@ -95,55 +47,13 @@
     hf_file.close()
 
 
-def remove_file(file_path):
-    if os.path.exists(file_path):
-        os.remove(file_path)
-
-
-def extract_configuration(config_object):
-    config_loss = dict()
-    for index, item in enumerate(config_object):
-        config_loss[index] = list()
-        d_config = dict()
-        d_config['loss'] = item['result']['loss']
-        d_config['params_config'] = item['misc']['vals']
-        config_loss[index].append(d_config)
-    return config_loss
-
-
-def get_best_parameters(mdl_dict):
-    """
-    Get param values (defaults as well)
-    """
-    lr = float(mdl_dict.get("learning_rate", "0.001"))
-    embedding_size = int(mdl_dict.get("embedding_size", "512"))
-    dropout = float(mdl_dict.get("dropout", "0.2"))
-    recurrent_dropout = float(mdl_dict.get("recurrent_dropout", "0.2"))
-    spatial_dropout = float(mdl_dict.get("spatial_dropout", "0.2"))
-    units = int(mdl_dict.get("units", "512"))
-    batch_size = int(mdl_dict.get("batch_size", "512"))
-    activation_recurrent = mdl_dict.get("activation_recurrent", "elu")
-    activation_output = mdl_dict.get("activation_output", "sigmoid")
-
-    return {
-        "lr": lr,
-        "embedding_size": embedding_size,
-        "dropout": dropout,
-        "recurrent_dropout": recurrent_dropout,
-        "spatial_dropout": spatial_dropout,
-        "units": units,
-        "batch_size": batch_size,
-        "activation_recurrent": activation_recurrent,
-        "activation_output": activation_output,
-    }
-
-
 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
@@ -152,67 +62,101 @@
     return weighted_binary_crossentropy
 
 
-def set_recurrent_network(mdl_dict, reverse_dictionary, class_weights):
-    """
-    Create a RNN network and set its parameters
-    """
-    dimensions = len(reverse_dictionary) + 1
-    model_params = get_best_parameters(mdl_dict)
-
-    # define the architecture of the neural network
-    model = Sequential()
-    model.add(Embedding(dimensions, model_params["embedding_size"], mask_zero=True))
-    model.add(SpatialDropout1D(model_params["spatial_dropout"]))
-    model.add(GRU(model_params["units"], dropout=model_params["spatial_dropout"], recurrent_dropout=model_params["recurrent_dropout"], activation=model_params["activation_recurrent"], return_sequences=True))
-    model.add(Dropout(model_params["dropout"]))
-    model.add(GRU(model_params["units"], dropout=model_params["spatial_dropout"], recurrent_dropout=model_params["recurrent_dropout"], activation=model_params["activation_recurrent"], return_sequences=False))
-    model.add(Dropout(model_params["dropout"]))
-    model.add(Dense(dimensions, activation=model_params["activation_output"]))
-    optimizer = RMSprop(lr=model_params["lr"])
-    model.compile(loss=weighted_loss(class_weights), optimizer=optimizer)
-    return model, model_params
+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, next_compatible_tools, usage_scores, actual_classes_pos, topk):
+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
     """
@@ -221,31 +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)