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Update clustering.py #37

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Nov 25, 2023
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Update clustering.py #37

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431b33d
Update clustering.py
humbleOldSage Aug 11, 2023
36065b4
moving clustering_examples.ipynb to trip_model
humbleOldSage Aug 16, 2023
97406c4
Removing changes in builtimeseries.py
humbleOldSage Aug 16, 2023
88988d3
Changes to support TRB_Label_Assist
humbleOldSage Aug 20, 2023
3e19b32
suggestions
humbleOldSage Aug 20, 2023
0899ee4
Revert "suggestions"
humbleOldSage Aug 20, 2023
667ab24
Improving readability
humbleOldSage Aug 20, 2023
e2448c5
making `cluster_performance.ipynb`, `generate_figs_for_poster` and `…
humbleOldSage Aug 22, 2023
59c7c64
Unified Interface for fit function
humbleOldSage Aug 26, 2023
a34836f
Fixing `models.py` to support `regenerate_classification_performance_…
humbleOldSage Aug 30, 2023
7eefdb0
[PARTIALLY TESTED] Single database read and Code Cleanuo
humbleOldSage Sep 14, 2023
6a641db
Delete TRB_label_assist/first_trial_results/cv results DBSCAN+SVM (de…
humbleOldSage Sep 18, 2023
3a8bdc0
Reverting Notebook
humbleOldSage Sep 18, 2023
7606d3d
[Partially Tested]Handled Whitespaces
humbleOldSage Sep 19, 2023
bb404e9
[Partially Tested] Suggested changes implemented
humbleOldSage Nov 7, 2023
97475ef
Revert "[Partially Tested] Suggested changes implemented"
humbleOldSage Nov 7, 2023
452e454
[Partially Tested] Suggested changes implemented
humbleOldSage Nov 7, 2023
2a39b12
Minor variable fixes
humbleOldSage Nov 10, 2023
e0beb0e
[TESTED] All the notebooks and files are tested
humbleOldSage Nov 16, 2023
c8c3883
Minor Fixes
humbleOldSage Nov 22, 2023
9225572
Minor Fixes in models.py
humbleOldSage Nov 24, 2023
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11 changes: 8 additions & 3 deletions TRB_label_assist/SVM_decision_boundaries.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@
"import emission.storage.timeseries.abstract_timeseries as esta\n",
"import emission.storage.decorations.trip_queries as esdtq\n",
"import emission.core.get_database as edb\n",
"import emission.analysis.modelling.trip_model.run_model as eamtr\n",
"\n",
"import data_wrangling\n",
"from clustering import add_loc_clusters"
Expand Down Expand Up @@ -60,10 +61,12 @@
"uuids = [suburban_uuid, college_campus_uuid]\n",
"confirmed_trip_df_map = {}\n",
"labeled_trip_df_map = {}\n",
"ct_entry={}\n",
"expanded_trip_df_map = {}\n",
"for u in uuids:\n",
" ts = esta.TimeSeries.get_time_series(u)\n",
" ct_df = ts.get_data_df(\"analysis/confirmed_trip\")\n",
" ct_entry[u]=eamtr._get_training_data(u,None)\n",
" ct_df = ts.to_data_df(\"analysis/confirmed_trip\",ct_entry[u])\n",
" confirmed_trip_df_map[u] = ct_df\n",
" labeled_trip_df_map[u] = esdtq.filter_labeled_trips(ct_df)\n",
" expanded_trip_df_map[u] = esdtq.expand_userinputs(labeled_trip_df_map[u])"
Expand Down Expand Up @@ -110,6 +113,8 @@
" df_for_cluster = all_trips_df if cluster_unlabeled else labeled_trips_df\n",
"\n",
" df_for_cluster = add_loc_clusters(df_for_cluster,\n",
" ct_entry,\n",
" clustering_way='destination',\n",
" radii=radii,\n",
" alg=alg,\n",
" loc_type=loc_type,\n",
Expand Down Expand Up @@ -264,9 +269,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python3",
"language": "python",
"name": "python3"
"name": "Python3"
},
"language_info": {
"codemirror_mode": {
Expand Down
9 changes: 5 additions & 4 deletions TRB_label_assist/classification_performance.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -19,15 +19,14 @@
"import pandas as pd\n",
"import numpy as np\n",
"from uuid import UUID\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# import logging\n",
"# logging.basicConfig(level=logging.DEBUG)\n",
"\n",
"import emission.storage.timeseries.abstract_timeseries as esta\n",
"import emission.storage.decorations.trip_queries as esdtq\n",
"\n",
"import emission.analysis.modelling.trip_model.run_model as eamtr\n",
"from performance_eval import get_clf_metrics, cv_for_all_algs, PREDICTORS"
]
},
Expand All @@ -49,10 +48,11 @@
"labeled_trip_df_map = {}\n",
"expanded_labeled_trip_df_map = {}\n",
"expanded_all_trip_df_map = {}\n",
"ct_entry={}\n",
"for u in all_users:\n",
" ts = esta.TimeSeries.get_time_series(u)\n",
" ct_df = ts.get_data_df(\"analysis/confirmed_trip\")\n",
"\n",
" ct_entry[u]=eamtr._get_training_data(u,None)\n",
" ct_df = ts.to_data_df(\"analysis/confirmed_trip\",ct_entry[u])\n",
" confirmed_trip_df_map[u] = ct_df\n",
" labeled_trip_df_map[u] = esdtq.filter_labeled_trips(ct_df)\n",
" expanded_labeled_trip_df_map[u] = esdtq.expand_userinputs(\n",
Expand Down Expand Up @@ -132,6 +132,7 @@
"# load in all runs\n",
"model_names = list(PREDICTORS.keys())\n",
"cv_results = cv_for_all_algs(\n",
" ct_entry,\n",
" uuid_list=all_users,\n",
" expanded_trip_df_map=expanded_labeled_trip_df_map,\n",
" model_names=model_names,\n",
Expand Down
12 changes: 8 additions & 4 deletions TRB_label_assist/cluster_performance.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -15,11 +15,10 @@
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.gridspec import GridSpec\n",
"\n",
"import emission.analysis.modelling.trip_model.run_model as eamtr\n",
"import emission.storage.timeseries.abstract_timeseries as esta\n",
"import emission.storage.decorations.trip_queries as esdtq\n",
"import performance_eval\n",
Expand All @@ -45,10 +44,11 @@
"labeled_trip_df_map = {}\n",
"expanded_labeled_trip_df_map = {}\n",
"expanded_all_trip_df_map = {}\n",
"ct_entry={}\n",
"for u in all_users:\n",
" ts = esta.TimeSeries.get_time_series(u)\n",
" ct_df = ts.get_data_df(\"analysis/confirmed_trip\")\n",
"\n",
" ct_entry[u]=eamtr._get_training_data(u,None) \n",
" ct_df = ts.to_data_df(\"analysis/confirmed_trip\",ct_entry[u]) \n",
" confirmed_trip_df_map[u] = ct_df\n",
" labeled_trip_df_map[u] = esdtq.filter_labeled_trips(ct_df)\n",
" expanded_labeled_trip_df_map[u] = esdtq.expand_userinputs(\n",
Expand Down Expand Up @@ -87,6 +87,8 @@
"\n",
" all_results_df = performance_eval.run_eval_cluster_metrics(\n",
" expanded_labeled_trip_df_map,\n",
" ct_entry,\n",
" clustering_way='destination',\n",
" user_list=all_users,\n",
" radii=radii,\n",
" loc_type='end',\n",
Expand Down Expand Up @@ -265,6 +267,8 @@
"\n",
"SVM_results_df = performance_eval.run_eval_cluster_metrics(\n",
" expanded_labeled_trip_df_map,\n",
" ct_entry,\n",
" clustering_way=\"destination\",\n",
" user_list=all_users,\n",
" radii=radii,\n",
" loc_type='end',\n",
Expand Down
47 changes: 37 additions & 10 deletions TRB_label_assist/clustering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,8 +16,8 @@
# our imports
# NOTE: this requires changing the branch of e-mission-server to
# eval-private-data-compatibility
import emission.analysis.modelling.tour_model_extended.similarity as eamts
import emission.storage.decorations.trip_queries as esdtq
import emission.analysis.modelling.trip_model.greedy_similarity_binning as eamtg

EARTH_RADIUS = 6371000
ALG_OPTIONS = [
Expand All @@ -28,9 +28,27 @@
'mean_shift'
]

def cleanEntryTypeData(loc_df,trip_entry):

"""
Helps weed out entries from the list of entries which were removed from the df using
esdtq.filter_labeled_trips() and esdtq.expand_userinputs()

loc_df : dataframe amde from entry type data
trip_entry : the entry type equivalent of loc_df ,
which was passed alongside the dataframe while loading the data

"""

ids_in_df=loc_df['_id']
filtered_trip_entry = list(filter(lambda entry: entry['_id'] in ids_in_df.values, trip_entry))
return filtered_trip_entry


def add_loc_clusters(
loc_df,
trip_entry,
clustering_way,
radii,
loc_type,
alg,
Expand All @@ -53,6 +71,9 @@ def add_loc_clusters(
Args:
loc_df (dataframe): must have columns 'start_lat' and 'start_lon'
or 'end_lat' and 'end_lon'
trip_entry ( list of Entry/confirmedTrip): list consisting all entries from the
time data was loaded. loc_df was obtained from this by converting to df and
then filtering out labeled trips and expanding user_inputs
radii (int list): list of radii to run the clustering algs with
loc_type (str): 'start' or 'end'
alg (str): 'DBSCAN', 'naive', 'OPTICS', 'SVM', 'fuzzy', or
Expand Down Expand Up @@ -98,19 +119,25 @@ def add_loc_clusters(
loc_df.loc[:, f"{loc_type}_DBSCAN_clusters_{r}_m"] = labels

elif alg == 'naive':

cleaned_trip_entry= cleanEntryTypeData(loc_df,trip_entry)

for r in radii:
# this is using a modified Similarity class that bins start/end
# points separately before creating trip-level bins
sim_model = eamts.Similarity(loc_df,
radius_start=r,
radius_end=r,
shouldFilter=False,
cutoff=False)
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# we only bin the loc_type points to speed up the alg. avoid
# unnecessary binning since this is really slow
sim_model.bin_helper(loc_type=loc_type)
labels = sim_model.data_df[loc_type + '_bin'].to_list()

model_config = {
"metric": "od_similarity",
"similarity_threshold_meters": r, # meters,
"apply_cutoff": False,
"clustering_way": clustering_way,
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"shouldFilter":False,
"incremental_evaluation": False
}

sim_model = eamtg.GreedySimilarityBinning(model_config)
sim_model.fit(cleaned_trip_entry)
labels = [int(l) for l in sim_model.tripLabels]
# # pd.Categorical converts the type from int to category (so
# # numerical operations aren't possible)
# loc_df.loc[:, f"{loc_type}_{alg}_clusters_{r}_m"] = pd.Categorical(
Expand Down
23 changes: 19 additions & 4 deletions TRB_label_assist/clustering_examples.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -26,12 +26,13 @@
"%autoreload 2\n",
"\n",
"from uuid import UUID\n",
"\n",
"import itertools\n",
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"import emission.storage.timeseries.abstract_timeseries as esta\n",
"import emission.storage.decorations.trip_queries as esdtq\n",
"import emission.core.get_database as edb\n",
"\n",
"import mapping"
"import emission.analysis.modelling.trip_model.run_model as eamtr\n",
"import mapping\n",
"import emission.analysis.modelling as eam"
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]
},
{
Expand Down Expand Up @@ -60,9 +61,11 @@
"confirmed_trip_df_map = {}\n",
"labeled_trip_df_map = {}\n",
"expanded_trip_df_map = {}\n",
"ct_entry={}\n",
"for u in uuids:\n",
" ts = esta.TimeSeries.get_time_series(u)\n",
" ct_df = ts.get_data_df(\"analysis/confirmed_trip\")\n",
" ct_entry[u]=eamtr._get_training_data(u,None) \n",
" ct_df = ts.to_data_df(\"analysis/confirmed_trip\",itertools.chain(ct_entry[u])) \n",
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" confirmed_trip_df_map[u] = ct_df\n",
" labeled_trip_df_map[u] = esdtq.filter_labeled_trips(ct_df)\n",
" expanded_trip_df_map[u] = esdtq.expand_userinputs(labeled_trip_df_map[u])"
Expand All @@ -83,8 +86,10 @@
"outputs": [],
"source": [
"fig = mapping.find_plot_clusters(expanded_trip_df_map[suburban_uuid],\n",
" ct_entry[suburban_uuid],\n",
" alg='naive',\n",
" loc_type='end',\n",
" clustering_way=\"destination\",\n",
" plot_unlabeled=False,\n",
" cluster_unlabeled=False,\n",
" radii=[50, 100, 150])\n",
Expand All @@ -98,8 +103,10 @@
"outputs": [],
"source": [
"fig = mapping.find_plot_clusters(expanded_trip_df_map[college_campus_uuid],\n",
" ct_entry[college_campus_uuid],\n",
" alg='naive',\n",
" loc_type='end',\n",
" clustering_way=\"destination\",\n",
" plot_unlabeled=False,\n",
" cluster_unlabeled=False,\n",
" radii=[50, 100, 150])\n",
Expand All @@ -121,9 +128,11 @@
"outputs": [],
"source": [
"fig = mapping.find_plot_clusters(expanded_trip_df_map[suburban_uuid],\n",
" ct_entry[suburban_uuid],\n",
" alg='DBSCAN',\n",
" SVM=False,\n",
" loc_type='end',\n",
" clustering_way=\"destination\",\n",
" plot_unlabeled=False,\n",
" cluster_unlabeled=False,\n",
" radii=[50, 100, 150, 200])\n",
Expand All @@ -137,9 +146,11 @@
"outputs": [],
"source": [
"fig = mapping.find_plot_clusters(expanded_trip_df_map[college_campus_uuid],\n",
" ct_entry[college_campus_uuid],\n",
" alg='DBSCAN',\n",
" SVM=False,\n",
" loc_type='end',\n",
" clustering_way=\"destination\",\n",
" plot_unlabeled=False,\n",
" cluster_unlabeled=False,\n",
" radii=[50, 100, 150, 200])\n",
Expand All @@ -161,9 +172,11 @@
"outputs": [],
"source": [
"fig = mapping.find_plot_clusters(expanded_trip_df_map[suburban_uuid],\n",
" ct_entry[suburban_uuid],\n",
" alg='DBSCAN',\n",
" SVM=True,\n",
" loc_type='end',\n",
" clustering_way=\"destination\",\n",
" plot_unlabeled=False,\n",
" cluster_unlabeled=False,\n",
" radii=[50, 100, 150, 200])\n",
Expand All @@ -177,9 +190,11 @@
"outputs": [],
"source": [
"fig = mapping.find_plot_clusters(expanded_trip_df_map[college_campus_uuid],\n",
" ct_entry[college_campus_uuid],\n",
" alg='DBSCAN',\n",
" SVM=True,\n",
" loc_type='end',\n",
" clustering_way=\"destination\",\n",
" plot_unlabeled=False,\n",
" cluster_unlabeled=False,\n",
" radii=[50, 100, 150, 200])\n",
Expand Down
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