trajectory_evaluation.ipynb breaks

[singhish]

When I reach the cell:

spatial_errors_df = pd.DataFrame()
spatial_errors_df = pd.concat([spatial_errors_df, get_spatial_errors(pv_la)], axis="index")
spatial_errors_df = pd.concat([spatial_errors_df, get_spatial_errors(pv_sj)], axis="index")
spatial_errors_df = pd.concat([spatial_errors_df, get_spatial_errors(pv_ucb)], axis="index")

I get the following output:

No ground truth route for suburb_city_driving_weekend walk_start, must be polygon, skipping...
Processing travel leg android, ucb-sdb-android-1, accuracy_control, suburb_city_driving_weekend, suburb_city_driving_weekend
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-21-8958a15239da> in <module>
      1 spatial_errors_df = pd.DataFrame()
----> 2 spatial_errors_df = pd.concat([spatial_errors_df, get_spatial_errors(pv_la)], axis="index")
      3 spatial_errors_df = pd.concat([spatial_errors_df, get_spatial_errors(pv_sj)], axis="index")
      4 spatial_errors_df = pd.concat([spatial_errors_df, get_spatial_errors(pv_ucb)], axis="index")

<ipython-input-20-26623ce778ff> in get_spatial_errors(pv)
     34                         meter_dist = filtered_us_gpdf.geometry.distance(filtered_gt_linestring)
     35                         ne = len(meter_dist)
---> 36                         curr_spatial_error_df = gpd.GeoDataFrame({"error": meter_dist,
     37                                                                   "ts": section_geo_df.ts,
     38                                                                   "geometry": section_geo_df.geometry,

~/miniconda3/envs/emissioneval/lib/python3.8/site-packages/geopandas/geodataframe.py in __init__(self, *args, **kwargs)
     59         crs = kwargs.pop("crs", None)
     60         geometry = kwargs.pop("geometry", None)
---> 61         super(GeoDataFrame, self).__init__(*args, **kwargs)
     62 
     63         # need to set this before calling self['geometry'], because

~/miniconda3/envs/emissioneval/lib/python3.8/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
    433             )
    434         elif isinstance(data, dict):
--> 435             mgr = init_dict(data, index, columns, dtype=dtype)
    436         elif isinstance(data, ma.MaskedArray):
    437             import numpy.ma.mrecords as mrecords

~/miniconda3/envs/emissioneval/lib/python3.8/site-packages/pandas/core/internals/construction.py in init_dict(data, index, columns, dtype)
    252             arr if not is_datetime64tz_dtype(arr) else arr.copy() for arr in arrays
    253         ]
--> 254     return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
    255 
    256 

~/miniconda3/envs/emissioneval/lib/python3.8/site-packages/pandas/core/internals/construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype)
     62     # figure out the index, if necessary
     63     if index is None:
---> 64         index = extract_index(arrays)
     65     else:
     66         index = ensure_index(index)

~/miniconda3/envs/emissioneval/lib/python3.8/site-packages/pandas/core/internals/construction.py in extract_index(data)
    376                         f"length {len(index)}"
    377                     )
--> 378                     raise ValueError(msg)
    379             else:
    380                 index = ibase.default_index(lengths[0])

ValueError: array length 180 does not match index length 259

Any ideas?

[shankari]

It is pretty clear that the error is because of the different lengths that we are trying to combine.

---> 36                         curr_spatial_error_df = gpd.GeoDataFrame({"error": meter_dist,
     37                                                                   "ts": section_geo_df.ts,
     38                                                                   "geometry": section_geo_df.geometry,

One of those vectors has length 180 and the other has length 259

[shankari]

I'm guessing that this is also due to the new MAMFDC entry, although I am not 100% sure why

[singhish]

@shankari The new MAMFDC doesn't get picked up until a few cells after. I'll try and deep dive a little further and see whether I can resolve it on my own. Will keep you posted.

[singhish]

@shankari After digging deeper, it looks like the data frames meter_dist and section_geo_df in the function get_spatial_errors are of different lengths. Will try to address this deeper in the morning, but will also check if the other trajectory notebooks work as well so I can grab box plots.

Any idea as to why that might be the case?

[shankari]

it looks like the data frames meter_dist and section_geo_df in the function get_spatial_errors are of different lengths.

Right, that's what I said in #19 (comment)

Any idea as to why that might be the case?

I thought this may be because of MAMFDC, but it wasn't.

[shankari]

@singhish The actual error is because we were using the filtered values (outside the start/end polygons) for all the other columns but not for the geometry and ts columns. I am not sure how this ever worked - maybe this is part of the lost code in the reset.

At any rate, the fix is:

@@ -364,9 +364,11 @@
     "                        filtered_gt_linestring = emd.filter_ground_truth_linestring(utm_section_gt_shapes)\n",
     "                        meter_dist = filtered_us_gpdf.geometry.distance(filtered_gt_linestring)\n",
     "                        ne = len(meter_dist)\n",
+    "                        \n",
+    "                        filtered_section_geo_df = section_geo_df.loc[filtered_us_gpdf.index]\n",
     "                        curr_spatial_error_df = gpd.GeoDataFrame({\"error\": meter_dist,\n",
-    "                                                                  \"ts\": section_geo_df.ts,\n",
-    "                                                                  \"geometry\": section_geo_df.geometry,\n",
+    "                                                                  \"ts\": filtered_section_geo_df.ts,\n",
+    "                                                                  \"geometry\": filtered_section_geo_df.geometry,\n",
     "                                                                  \"phone_os\": np.repeat(phone_os, ne),\n",
     "                                                                  \"phone_label\": np.repeat(phone_label, ne),\n",
     "                                                                  \"role\": np.repeat(r[\"eval_role_base\"], ne),\n",

Re-ran it for one section and it worked, rerunning for the entire set of timelines before submitting a PR.

[shankari]

visual representation of the unfiltered v/s filtered points, for the record. This is most visible on the left bottom of the map, near the Los Altos Library

[singhish]

This is helpful. Thank you!

Also, the trajectory_evaluation_spatio_temporal notebook ended up working properly, would you prefer we use the box plots from this notebook or the one relevant to this issue?

[shankari]

you mean, should we use trajectory_evaluation.ipynb or trajectory_evaluation_spatio_temporal.ipynb
the spatial is easier to understand
the spatio-temporal is more complicated

Which do you think?

[shankari]

There are some additional errors trajectory_evaluation.ipynb in the error checks at the end.
I am taking a look at those now so the entire notebook works.

[shankari]

I double-checked all the versions of this notebook and the code is identical. The other notebooks (e.g. trajectory_evaluation_analysis_master.ipynb) even have the outputs saved which proved that it did work.

But the error is straightforward. So how did this ever work?

[singhish]

the spatial is easier to understand

We can go with this one then!

[shankari]

there are some great examples of the spatial error at the end of the notebook that you can use.

[singhish]

Added these box plots to the presentation for now, as they give a good understanding of outliers vs what's expected in my opinion

I also added the map of the express_bus route over a single run

to give a real-world visual of what's going on. will pick this up in the morning

[shankari]

you may want to include only the car_scooter_brex part of that since the others don't have all the sensing regimes.