Preparing ml input for cross-context

src/data-hub/proactive-community-detection-microservice/app/db/repository.py

@@ -117,8 +117,8 @@ class Repository(MongoRepositoryBase):
     def add_layer_pair(self, layer_pair: LayerPairDao):
         super().insert_entry(self._layer_pair_collection, layer_pair.__dict__)
 
-    def get_layer_pairs(self, use_case: str, use_case_table: str) -> List[LayerPairDao]:
-        entries = super().get_entries(self._layer_pair_collection)
+    def get_layer_pairs(self, use_case: str) -> List[LayerPairDao]:
+        entries = super().get_entries(self._layer_pair_collection, selection={'use_case': use_case})
         return [LayerPairDao.create_from_dict(e) for e in entries]
 
 #endregion

src/data-hub/proactive-community-detection-microservice/app/processing/data_prep/cluster_metrics_main.py → src/data-hub/proactive-community-detection-microservice/app/processing/data_prep/cluster_metrics_calc.py

@@ -7,7 +7,7 @@ import json
 import os
 from entities import TimeWindow, Cluster
 
-def store_metrics_for_clusters(use_case:str, layer_name: str, feature_names: List[str]):
+def store_metrics_for_clusters(use_case: str, layer_name: str, feature_names: List[str]):
     '''
     :param layer_name: Name of the layer for which multiple time windows exist
     :param feature_names: Features of the layer
@@ -119,7 +119,7 @@ import collections
 import statistics as stat
 
 def balance_dataset(df: DataFrame) -> DataFrame:
-    # nothing happening here, balance only on real training not during prep
+    # nothing happening here, balance only on real training, not during prep
     return df
 
 def store_training_data(use_case: str, layer_name: str):
@@ -139,48 +139,25 @@ def store_training_data(use_case: str, layer_name: str):
     df.to_csv(f'data/{use_case}/ml_input/single_context/{layer_name}.csv')
 
 #######################
+
 from db.repository import Repository
 from db.dao import LayerDao
-
-repo = Repository()
-
 from pathlib import Path
 
-def store_clusters_as_files(use_case):
-    path_ = f'data/{use_case}/raw/clusters/'
-    Path(path_).mkdir(parents=True, exist_ok=True)
-
-    layers = repo.get_layers_for_use_case(use_case)
-    for l in layers:
-        clusters = repo.get_clusters_for_layer(use_case, l.use_case_table, l.layer_name)
-
-        with open(os.path.join(path_, f'{l.layer_name}.json'), 'w') as file_:
-            file_.write(json.dumps([c.to_serializable_dict() for c in clusters]))   
-            
-def store_time_slices_as_files(use_case):
-    path_ = f'data/{use_case}/raw/timeslices/'
-
-    layers = repo.get_layers_for_use_case(use_case)
-    for l in layers:
-        Path(os.path.join(path_, l.layer_name)).mkdir(parents=True, exist_ok=True)
-
-        time_slices = repo.get_time_slices_for_layer(use_case, l.use_case_table, l.layer_name)
-        for ts in time_slices:
-            with open(os.path.join(path_, l.layer_name, f'{ts.time}.json'), 'w') as file_:
-                file_.write(json.dumps(ts.to_serializable_dict()))   
-
-        
+repo = Repository()
 
-def start(use_case=None):
+def run(use_case=None):
+    '''
+    Requires raw jsons for clusters and time slices.
+    Working directory: data/
+    '''
     if use_case is not None:
         use_cases = [use_case]
     else:
         use_cases = repo.get_use_cases()
 
     for use_case in use_cases:
-        print(f"Executing for use case {use_case}")
-        # store_clusters_as_files(use_case)
-        store_time_slices_as_files(use_case)
+        print(f"Executing cluster metrics calc for use case {use_case}")
         
         layers = [[l.layer_name, l.properties] for l in repo.get_layers_for_use_case(use_case)]
     
@@ -189,6 +166,6 @@ def start(use_case=None):
             store_metrics_for_clusters(use_case, layer[0], layer[1])
         ###################
         for name, _ in layers:
-            print(f"Creating training data for {name}")
+            print(f"Storing training data for {name}")
             store_training_data(use_case, layer_name=name)
             
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/processing/data_prep/layer_metrics_calc.py

+from processing.data_prep.metrics_base import calculate_center, get_cyclic_time_feature, get_evolution_label,convert_metrics_data_to_dataframe
+from pathlib import Path
+
+#################
+from typing import List, Tuple
+import statistics as stat
+import json
+import os
+from entities import TimeWindow, Layer
+from processing import ClusterMetricsCalculatorFactory
+
+def store_metrics_for_layers(use_case: str, layer_name: str, feature_names: List[str]):
+    print(f"Working on {layer_name} layer metrics")
+
+    # load global cluster centers
+    path_in = f'data/{use_case}/raw/clusters/{layer_name}.json'
+    with open(path_in, 'r') as file:
+        clusters = json.loads(file.read())
+        cluster_centers: Dict[str, Tuple[float]] = { 
+            str(cluster['cluster_label']): calculate_center(cluster, feature_names) 
+            for cluster in clusters 
+            if cluster['label'] != 'noise' 
+            }
+
+    # load time windows 
+    all_layers: List[Layer] = []
+    path_in = f'data/{use_case}/raw/timeslices/{layer_name}'
+    for root, _, files in os.walk(path_in):
+        for f in files:
+            with open(os.path.join(root, f), 'r') as file:
+                json_time_slice = json.loads(file.read())
+                time_window = TimeWindow.create_from_serializable_dict(json_time_slice)
+
+                layer = Layer.create_from_time_window(time_window, feature_names, cluster_centers)
+                all_layers.append(layer)
+        
+    # store the layer metrics
+    Path(f'data/{use_case}/layer_metrics/').mkdir(parents=True, exist_ok=True)
+    path_out = f'data/{use_case}/layer_metrics/{layer_name}.json'
+    with open(path_out, 'w') as file:
+        file.write(json.dumps([l.__dict__ for l in all_layers]))
+#########################
+from typing import List
+
+def get_columns(N) -> List[str]:
+    '''Returns columns for the data depending on sizes of N (number time windows) independent of M (number of clusters in L_R).'''
+
+    cols = ['n_nodes', 'n_clusters', 'entropy']
+
+    for v in ['sizes', 'relative_sizes', 'center_dist']:
+        cols += [f'{v}_min', f'{v}_max', f'{v}_avg', f'{v}_sum']
+
+    # cols.extend(['relative_cluster_sizes']*M)
+    # cols.extend(['cluster_centers']*M)
+    # cols.extend(['distance_from_global_centers']*M)
+
+    cols.extend(['time_f1', 'time_f2'])
+
+    cols = cols * N 
+    return cols + ['cluster_id'] + ['evolution_label']
+######################
+def get_cyclic_time_feature_from_time_window(time: str) -> Tuple[float, float]:
+    return get_cyclic_time_feature(int(time.replace('(', '').replace(')', '').split(',')[1]))
+#######################
+from typing import Iterable, List, Dict, Any
+import json
+from entities import Layer, Cluster
+
+def get_layer_metrics(layer: Layer) -> Iterable:
+    res = [layer.n_nodes, layer.n_clusters, layer.entropy]
+    res += [layer.cluster_size_agg_metrics[k] for k in ['min', 'max', 'avg', 'sum']]
+    res += [layer.cluster_relative_size_agg_metrics[k] for k in ['min', 'max', 'avg', 'sum']]
+    res += [layer.cluster_center_distance_agg_metrics[k] for k in ['min', 'max', 'avg', 'sum']]
+    res.append(get_cyclic_time_feature_from_time_window(layer.time_window_id))
+    return res
+
+def create_layer_metrics_training_data(use_case: str, layer_name: str, reference_layer: str, N: int = 2) -> Iterable:
+    """
+    Loads the metrics training data for an individual layer from disk.
+    
+    A single metrics training data point should look like this:
+
+    [(n_nodes, n_clusters, entropy,
+     (relative_cluster_size)^M, (cluster_centers)^M, (distance_from_global_centers)^M, 
+     (time1, time2))^N, 
+     cluster_number, evolution_label]
+     
+    The first tuple represents metrics from the reference layer in t_i-(N-1).
+    The Nth tuple represents metrics from the reference layer in t_i.
+    The reference_layer has M clusters in total, this might differ from the number of clusters in layer_name.
+    The cluster number identifies the cluster for which the evolution_label holds. 
+    The label is one of {continuing, shrinking, growing, dissolving, forming} \ {splitting, merging} and identifies the change for a cluster in the layer layer_name for t_i.
+    """
+    if N != 2:
+        raise NotImplementedError("N is not implemented and fixed to 2!")
+    
+    with open(f'data/{use_case}/cluster_metrics/{layer_name}.json') as file:
+        cluster_metrics: List[Cluster] = [Cluster.create_from_dict(e) for e in json.loads(file.read())]
+        cluster_ids = {c.cluster_id for c in cluster_metrics}
+        cluster_metrics: Dict[Any, Cluster] = {(c.time_window_id, c.cluster_id): c for c in cluster_metrics}
+        
+    with open(f'data/{use_case}/layer_metrics/{reference_layer}.json') as file:
+        layer_metrics: List[Layer] = [Layer.create_from_dict(e) for e in json.loads(file.read())]
+        layer_metrics: Dict[Any, Layer] = {l.time_window_id: l for l in layer_metrics}
+
+    # load the time keys chronologically
+    ordered_time_keys = list(layer_metrics.keys())
+    ordered_time_keys.sort(key=lambda x: [int(v) for v in x.replace('(', '').replace(')', '').split(',')])
+    
+    # go through all time windows once...
+    prev_time_key = ordered_time_keys[0]
+    for current_time_key in ordered_time_keys[1:]:
+        # ...and load the current and previous layer metrics in the reference_layer
+        current_layer_metric = layer_metrics[current_time_key]
+        prev_layer_metric = layer_metrics[prev_time_key]
+
+        current_layer_metric_tuple = get_layer_metrics(current_layer_metric)
+        prev_layer_metric_tuple = get_layer_metrics(prev_layer_metric)
+
+        # ...then load the current and previous cluster metrics for all clusters in the layer_name
+        for cluster_id in cluster_ids:
+            current_cluster_metric = cluster_metrics[(current_time_key, cluster_id)]
+            prev_cluster_metric = cluster_metrics[(prev_time_key, cluster_id)]
+            evolution_label = get_evolution_label(prev_cluster_metric.size, current_cluster_metric.size)
+
+            # yield each combination of reference layer metrics to clusters
+            yield [prev_layer_metric_tuple, current_layer_metric_tuple, int(cluster_id), evolution_label]
+
+        prev_time_key = current_time_key
+#####################
+import numpy as np
+
+def flatten_layer_metrics_datapoint(datapoint: list) -> Tuple['X', np.array]:
+    '''
+    Flattens a single layer metrics data point in the form:
+    [(n_nodes, n_clusters, entropy,
+     (relative_cluster_size)^M, (distance_from_global_centers)^M, 
+     (time1, time2))^N, 
+     cluster_number, evolution_label]
+    to:
+    (X, y: np.array)
+    '''
+    flat_list = []
+    for layer_metric_tuple in datapoint[:-2]: # for all x
+        flat_list.extend(layer_metric_tuple[0:-1]) # everything before time
+        flat_list.extend(layer_metric_tuple[-1]) # time1/2
+
+    flat_list.append(datapoint[-2]) # cluster num
+    flat_list.append(datapoint[-1]) # y
+
+    return np.asarray(flat_list)
+#########################
+import numpy as np
+import pandas as pd
+from pandas import DataFrame
+import collections
+import statistics as stat
+
+def balance_dataset(df: DataFrame) -> DataFrame:
+    # nothing happening here, balance only on real training, not during prep
+    return df
+
+def store_training_data(use_case: str, layer_name: str, reference_layer_name: str):
+    # load metrics data from disk
+    data: Iterable = create_layer_metrics_training_data(use_case=use_case, layer_name=layer_name, reference_layer=reference_layer_name)
+    
+    # convert to X and Y
+    df = convert_metrics_data_to_dataframe(data, columns=get_columns(N=2), flattening_method=flatten_layer_metrics_datapoint)
+    
+    # balance df
+    df = balance_dataset(df)
+    
+    # shuffle
+    df = df.sample(frac=1).reset_index(drop=True)
+
+    Path(f'data/{use_case}/ml_input/cross_context/').mkdir(parents=True, exist_ok=True)
+    df.to_csv(f'data/{use_case}/ml_input/cross_context/{layer_name}_{reference_layer_name}.csv')
+
+#########################
+
+from db.repository import Repository
+from db.dao import LayerDao
+from pathlib import Path
+
+repo = Repository()
+
+def run(use_case=None):
+    '''
+    Requires raw jsons and cluster metrics.
+    Working directory: data/
+    '''
+    if use_case is not None:
+        use_cases = [use_case]
+    else:
+        use_cases = repo.get_use_cases()
+
+    for use_case in use_cases:
+        print(f"Executing layer metrics calc for use case {use_case}")
+
+        layers = [[l.layer_name, l.properties] for l in repo.get_layers_for_use_case(use_case)]
+        layer_pairs = repo.get_layer_pairs(use_case)
+
+        ################
+        for layer in layers:
+            try:
+                store_metrics_for_layers(use_case, layer[0], layer[1])
+            except FileNotFoundError:
+                pass
+        ###############
+        for ld in layer_pairs:
+            print(f"Storing training data for {ld.layer} with L_R={ld.reference_layer}")
+            store_training_data(use_case, layer_name=ld.layer, reference_layer_name=ld.reference_layer)
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/processing/data_prep/main.py

+from processing.data_prep.cluster_metrics_calc import run as crun
+from processing.data_prep.layer_metrics_calc import run as lrun
+
+from pathlib import Path
+import json
+import os
+
+from db.repository import Repository
+
+
+repo = Repository()
+
+
+def store_clusters_as_files(use_case):
+    path_ = f'data/{use_case}/raw/clusters/'
+    Path(path_).mkdir(parents=True, exist_ok=True)
+
+    layers = repo.get_layers_for_use_case(use_case)
+    for l in layers:
+        clusters = repo.get_clusters_for_layer(use_case, l.use_case_table, l.layer_name)
+
+        with open(os.path.join(path_, f'{l.layer_name}.json'), 'w') as file_:
+            file_.write(json.dumps([c.to_serializable_dict() for c in clusters]))   
+            
+
+def store_time_slices_as_files(use_case):
+    path_ = f'data/{use_case}/raw/timeslices/'
+
+    layers = repo.get_layers_for_use_case(use_case)
+    for l in layers:
+        Path(os.path.join(path_, l.layer_name)).mkdir(parents=True, exist_ok=True)
+
+        time_slices = repo.get_time_slices_for_layer(use_case, l.use_case_table, l.layer_name)
+        for ts in time_slices:
+            with open(os.path.join(path_, l.layer_name, f'{ts.time}.json'), 'w') as file_:
+                file_.write(json.dumps(ts.to_serializable_dict()))   
+
+
+def run(use_case=None):
+    '''Prepares training data for single and cross-context using the file system (data/)'''
+    if use_case is not None:
+        use_cases = [use_case]
+    else:
+        use_cases = repo.get_use_cases()
+    
+
+    for use_case in use_cases:
+        store_clusters_as_files(use_case)
+        store_time_slices_as_files(use_case)
+
+        crun(use_case)
+        lrun(use_case)
+

src/data-hub/proactive-community-detection-microservice/app/processing/data_prep/metrics_base.py

+'''
+These functions are utilized for both single and cross-context data preparation, i.e.
+- cluster_metrics_main.py
+- layer_metrics_main.py
+'''
+
 #############################
 from typing import Tuple
 from processing import ClusterMetricsCalculatorFactory

src/data-hub/proactive-community-detection-microservice/app/run_dataprep.py

@@ -5,6 +5,6 @@ if os.path.exists(modules_path):
     sys.path.insert(1, modules_path)
 
 
-from processing.data_prep.cluster_metrics_main import start
+from processing.data_prep.main import run
 
-start(use_case='community-prediction-youtube-n')
+run(use_case='community-prediction-youtube-n')
\ No newline at end of file

src/data-hub/proactive-community-detection-microservice/app/run_layerpair_upload.py

+import sys
+import os
+modules_path = '../../../modules/'
+if os.path.exists(modules_path):
+    sys.path.insert(1, modules_path)
+
+from security.token_manager import TokenManager
+import network_constants
+from db.repository import Repository
+from db.dao import LayerPairDao
+
+from typing import List, Dict
+import requests
+import json
+
+
+def get_youtube_dependencies() -> List[LayerPairDao]:
+    uc = 'community-prediction-youtube-n'
+    layer_dependencies = [
+        ('CategoryLayer', 'CountryLayer'),
+        ('ViewsLayer', 'CountryLayer'),
+
+        ('ViewsLayer', 'CategoryLayer'),
+
+        ('LikesLayer', 'ViewsLayer'),
+        ('DislikesLayer', 'ViewsLayer'),
+        ('CommentCountLayer', 'ViewsLayer'),
+        ('TrendDelayLayer', 'ViewsLayer'),
+    ]
+    
+    return [LayerPairDao(uc, uc, ld[0], ld[1]) for ld in layer_dependencies]
+
+
+def get_taxi_dependencies() -> List[LayerPairDao]:
+    uc = 'community-prediction-taxi'
+    layer_dependencies = [
+        ('CallTypeLayer', 'DayTypeLayer'),
+
+        ('OriginCallLayer', 'CallTypeLayer'),
+        ('OriginStandLayer', 'CallTypeLayer'),
+
+        ('TaxiIdLayer', 'OriginStandLayer'),
+        ('StartLocationLayer', 'OriginStandLayer'),
+        ('EndLocationLayer', 'OriginStandLayer'),
+
+        ('StartLocationLayer', 'DayTypeLayer'),
+        ('EndLocationLayer', 'DayTypeLayer'),
+    ]
+    
+    return [LayerPairDao(uc, uc, ld[0], ld[1]) for ld in layer_dependencies]
+    
+
+def upload_layerpair(layerpair:LayerPairDao):
+    jwt = TokenManager.getInstance().getToken()
+
+    url = f'https://{network_constants.BUSINESS_LOGIC_HOSTNAME}:{network_constants.BUSINESS_LOGIC_REST_PORT}/api/use-cases/{layerpair.use_case}/tables/{layerpair.table}/layer-pairs'
+
+    response = requests.post(
+            url,
+            verify = False, 
+            proxies = { "http":None, "https":None },
+            headers = {"Authorization": f"Bearer {jwt}"},
+            json = layerpair.__dict__
+    )
+
+    if response.status_code != 200:
+        raise ConnectionError(f"Could not upload layer pair, statuscode: {response.status_code}!")
+
+
+if __name__ == '__main__':
+    assert False, 'replace with true to upload now'
+
+    for lp in get_youtube_dependencies():
+        upload_layerpair(lp)
+        
+    for lp in get_taxi_dependencies():
+        upload_layerpair(lp)
\ No newline at end of file