BayDAG Contribution #14: Increasing Larch Loading for NMTF#780

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Merged
jpn-- merged 2 commits into from
Apr 1, 2024
Merged

BayDAG Contribution #14: Increasing Larch Loading for NMTF #780

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6a3a332
decrease larch load time for nmtf
dhensle Dec 16, 2023
74212e4
nmtf larch av arg
dhensle Mar 12, 2024
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99 changes: 98 additions & 1 deletion activitysim/estimation/larch/nonmand_tour_freq.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -7,6 +7,8 @@
from larch import DataFrames, Model
from larch.log import logger_name
from larch.util import Dict
import pickle
from datetime import datetime

from .general import (
apply_coefficients,
Expand All@@ -27,6 +29,7 @@ def interaction_simulate_data(
coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
segment_subset=[],
):
edb_directory = edb_directory.format(name=name)

Expand All@@ -46,21 +49,30 @@ def _read_csv(filename, **kwargs):
alt_values = {}

segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
if len(segment_subset) > 0:
assert set(segment_subset).issubset(
set(segment_names)
), f"{segment_subset} is not a subset of {segment_names}"
segment_names = segment_subset

for segment_name in segment_names:
print(f"Loading EDB for {segment_name} segment")
coefficients[segment_name] = _read_csv(
coefficients_files.format(name=name, segment_name=segment_name),
index_col="coefficient_name",
comment="#",
)
chooser_data[segment_name] = _read_csv(
chooser_data_files.format(name=name, segment_name=segment_name),
)
alt_values[segment_name] = _read_csv(
alt_values_files.format(name=name, segment_name=segment_name),
comment="#",
)

spec = _read_csv(
spec_file,
comment="#",
)
spec = remove_apostrophes(spec, ["Label"])
# alt_names = list(spec.columns[3:])
Expand DownExpand Up@@ -118,10 +130,80 @@ def unavail(model, x_ca):
return unav


# FIXME move all this to larch/general.py? see ActititySim issue #686
def _read_feather(filename, name, edb_directory, **kwargs):
filename = filename.format(name=name)
return pd.read_feather(os.path.join(edb_directory, filename), **kwargs)


def _to_feather(df, filename, name, edb_directory, **kwargs):
filename = filename.format(name=name)
return df.to_feather(os.path.join(edb_directory, filename), **kwargs)


def _read_pickle(filename, name, edb_directory, **kwargs):
filename = filename.format(name=name)
return pd.read_pickle(os.path.join(edb_directory, filename), **kwargs)


def _to_pickle(df, filename, name, edb_directory, **kwargs):
filename = filename.format(name=name)
return df.to_pickle(os.path.join(edb_directory, filename), **kwargs)


def _file_exists(filename, name, edb_directory):
filename = filename.format(name=name)
return os.path.exists(os.path.join(edb_directory, filename))


def get_x_ca_df(alt_values, name, edb_directory, num_chunks):
def split(a, n):
k, m = divmod(len(a), n)
return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

# process x_ca with cv_to_ca with or without chunking
x_ca_pickle_file = "{name}_x_ca.pkl"
if num_chunks == 1:
x_ca = cv_to_ca(alt_values)
elif _file_exists(x_ca_pickle_file, name, edb_directory):
# if pickle file from previous x_ca processing exist, load it to save time
time_start = datetime.now()
x_ca = _read_pickle(x_ca_pickle_file, name, edb_directory)
print(
f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}"
)
else:
time_start = datetime.now()
# calculate num_chunks based on chunking_size (or max number of rows per chunk)
chunking_size = round(len(alt_values) / num_chunks, 3)
print(
f"Using {num_chunks} chunks results in chunk size of {chunking_size} (of {len(alt_values)} total rows)"
)
all_chunk_ids = list(alt_values.index.get_level_values(0).unique())
split_ids = list(split(all_chunk_ids, num_chunks))
x_ca_list = []
for i, chunk_ids in enumerate(split_ids):
alt_values_i = alt_values.loc[chunk_ids]
x_ca_i = cv_to_ca(alt_values_i)
x_ca_list.append(x_ca_i)
print(
f"\rx_ca_i compute done for chunk {i+1}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}"
)
x_ca = pd.concat(x_ca_list, axis=0)
# save final x_ca result as pickle file to save time for future data loading
_to_pickle(x_ca, x_ca_pickle_file, name, edb_directory)
print(
f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}"
)
return x_ca


def nonmand_tour_freq_model(
edb_directory="output/estimation_data_bundle/{name}/",
return_data=False,
condense_parameters=False,
segment_subset=[],
num_chunks=1,
):
"""
Prepare nonmandatory tour frequency models for estimation.
Expand All@@ -141,10 +223,16 @@ def nonmand_tour_freq_model(
data = interaction_simulate_data(
name="non_mandatory_tour_frequency",
edb_directory=edb_directory,
segment_subset=segment_subset,
)

settings = data.settings
segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
if len(segment_subset) > 0:
assert set(segment_subset).issubset(
set(segment_names)
), f"{segment_subset} is not a subset of {segment_names}"
segment_names = segment_subset
if condense_parameters:
data.relabel_coef = link_same_value_coefficients(
segment_names, data.coefficients, data.spec
Expand All@@ -157,6 +245,7 @@ def nonmand_tour_freq_model(

m = {}
for segment_name in segment_names:
print(f"Creating larch model for {segment_name}")
segment_model = m[segment_name] = Model()
# One of the alternatives is coded as 0, so
# we need to explicitly initialize the MNL nesting graph
Expand All@@ -178,7 +267,15 @@ def nonmand_tour_freq_model(
.set_index("person_id")
.rename(columns={"TAZ": "HOMETAZ"})
)
x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
print("\t performing cv to ca step")
# x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
x_ca = get_x_ca_df(
alt_values=alt_values[segment_name].set_index(["person_id", "variable"]),
name=segment_name,
edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"),
num_chunks=num_chunks,
)

d = DataFrames(
co=x_co,
ca=x_ca,
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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BayDAG Contribution #14: Increasing Larch Loading for NMTF#780

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to your account

 Merged

jpn--
merged 2 commits into

from

Apr 1, 2024
 Merged


BayDAG Contribution #14: Increasing Larch Loading for NMTF
#780
 Changes from all commits
Commits

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2 commits


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Hold shift + click to select a range
6a3a332

decrease larch load time for nmtf


dhensle Dec 16, 2023
74212e4

nmtf larch av arg


dhensle Mar 12, 2024

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99 changes: 98 additions & 1 deletion
activitysim/estimation/larch/nonmand_tour_freq.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,6 +7,8 @@
from larch import DataFrames, Model
from larch.log import logger_name
from larch.util import Dict
import pickle
from datetime import datetime

from .general import (
 apply_coefficients,
Expand All@@ -27,6 +29,7 @@ def interaction_simulate_data(
 coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
 chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
 alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
 segment_subset=[],
):
 edb_directory = edb_directory.format(name=name)

Expand All@@ -46,21 +49,30 @@ def _read_csv(filename, **kwargs):
 alt_values = {}

 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset

 for segment_name in segment_names:
 print(f"Loading EDB for {segment_name} segment")
 coefficients[segment_name] = _read_csv(
 coefficients_files.format(name=name, segment_name=segment_name),
 index_col="coefficient_name",
 comment="#",
 )
 chooser_data[segment_name] = _read_csv(
 chooser_data_files.format(name=name, segment_name=segment_name),
 )
 alt_values[segment_name] = _read_csv(
 alt_values_files.format(name=name, segment_name=segment_name),
 comment="#",
 )

 spec = _read_csv(
 spec_file,
 comment="#",
 )
 spec = remove_apostrophes(spec, ["Label"])
 # alt_names = list(spec.columns[3:])
Expand DownExpand Up@@ -118,10 +130,80 @@ def unavail(model, x_ca):
 return unav


# FIXME move all this to larch/general.py? see ActititySim issue #686
def _read_feather(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_feather(os.path.join(edb_directory, filename), **kwargs)


def _to_feather(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_feather(os.path.join(edb_directory, filename), **kwargs)


def _read_pickle(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_pickle(os.path.join(edb_directory, filename), **kwargs)


def _to_pickle(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_pickle(os.path.join(edb_directory, filename), **kwargs)


def _file_exists(filename, name, edb_directory):
 filename = filename.format(name=name)
 return os.path.exists(os.path.join(edb_directory, filename))


def get_x_ca_df(alt_values, name, edb_directory, num_chunks):
 def split(a, n):
 k, m = divmod(len(a), n)
 return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

 # process x_ca with cv_to_ca with or without chunking
 x_ca_pickle_file = "{name}_x_ca.pkl"
 if num_chunks == 1:
 x_ca = cv_to_ca(alt_values)
 elif _file_exists(x_ca_pickle_file, name, edb_directory):
 # if pickle file from previous x_ca processing exist, load it to save time
 time_start = datetime.now()
 x_ca = _read_pickle(x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 else:
 time_start = datetime.now()
 # calculate num_chunks based on chunking_size (or max number of rows per chunk)
 chunking_size = round(len(alt_values) / num_chunks, 3)
 print(
 f"Using {num_chunks} chunks results in chunk size of {chunking_size} (of {len(alt_values)} total rows)"
 )
 all_chunk_ids = list(alt_values.index.get_level_values(0).unique())
 split_ids = list(split(all_chunk_ids, num_chunks))
 x_ca_list = []
 for i, chunk_ids in enumerate(split_ids):
 alt_values_i = alt_values.loc[chunk_ids]
 x_ca_i = cv_to_ca(alt_values_i)
 x_ca_list.append(x_ca_i)
 print(
 f"\rx_ca_i compute done for chunk {i+1}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 x_ca = pd.concat(x_ca_list, axis=0)
 # save final x_ca result as pickle file to save time for future data loading
 _to_pickle(x_ca, x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 return x_ca


def nonmand_tour_freq_model(
 edb_directory="output/estimation_data_bundle/{name}/",
 return_data=False,
 condense_parameters=False,
 segment_subset=[],
 num_chunks=1,
):
 """
 Prepare nonmandatory tour frequency models for estimation.
Expand All@@ -141,10 +223,16 @@ def nonmand_tour_freq_model(
 data = interaction_simulate_data(
 name="non_mandatory_tour_frequency",
 edb_directory=edb_directory,
 segment_subset=segment_subset,
 )

 settings = data.settings
 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset
 if condense_parameters:
 data.relabel_coef = link_same_value_coefficients(
 segment_names, data.coefficients, data.spec
Expand All@@ -157,6 +245,7 @@ def nonmand_tour_freq_model(

 m = {}
 for segment_name in segment_names:
 print(f"Creating larch model for {segment_name}")
 segment_model = m[segment_name] = Model()
 # One of the alternatives is coded as 0, so
 # we need to explicitly initialize the MNL nesting graph
Expand All@@ -178,7 +267,15 @@ def nonmand_tour_freq_model(
 .set_index("person_id")
 .rename(columns={"TAZ": "HOMETAZ"})
 )
 x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 print("\t performing cv to ca step")
 # x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 x_ca = get_x_ca_df(
 alt_values=alt_values[segment_name].set_index(["person_id", "variable"]),
 name=segment_name,
 edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"),
 num_chunks=num_chunks,
 )

 d = DataFrames(
 co=x_co,
 ca=x_ca,
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BayDAG Contribution #14: Increasing Larch Loading for NMTF#780

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privacy statement. We’ll occasionally send you account related emails.

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to your account

 Merged

jpn--
merged 2 commits into

from

Apr 1, 2024
 Merged


BayDAG Contribution #14: Increasing Larch Loading for NMTF
#780
 Changes from all commits
Commits

Show all changes


2 commits


Select commit
Hold shift + click to select a range
6a3a332

decrease larch load time for nmtf


dhensle Dec 16, 2023
74212e4

nmtf larch av arg


dhensle Mar 12, 2024

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99 changes: 98 additions & 1 deletion
activitysim/estimation/larch/nonmand_tour_freq.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,6 +7,8 @@
from larch import DataFrames, Model
from larch.log import logger_name
from larch.util import Dict
import pickle
from datetime import datetime

from .general import (
 apply_coefficients,
Expand All@@ -27,6 +29,7 @@ def interaction_simulate_data(
 coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
 chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
 alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
 segment_subset=[],
):
 edb_directory = edb_directory.format(name=name)

Expand All@@ -46,21 +49,30 @@ def _read_csv(filename, **kwargs):
 alt_values = {}

 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset

 for segment_name in segment_names:
 print(f"Loading EDB for {segment_name} segment")
 coefficients[segment_name] = _read_csv(
 coefficients_files.format(name=name, segment_name=segment_name),
 index_col="coefficient_name",
 comment="#",
 )
 chooser_data[segment_name] = _read_csv(
 chooser_data_files.format(name=name, segment_name=segment_name),
 )
 alt_values[segment_name] = _read_csv(
 alt_values_files.format(name=name, segment_name=segment_name),
 comment="#",
 )

 spec = _read_csv(
 spec_file,
 comment="#",
 )
 spec = remove_apostrophes(spec, ["Label"])
 # alt_names = list(spec.columns[3:])
Expand DownExpand Up@@ -118,10 +130,80 @@ def unavail(model, x_ca):
 return unav


# FIXME move all this to larch/general.py? see ActititySim issue #686
def _read_feather(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_feather(os.path.join(edb_directory, filename), **kwargs)


def _to_feather(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_feather(os.path.join(edb_directory, filename), **kwargs)


def _read_pickle(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_pickle(os.path.join(edb_directory, filename), **kwargs)


def _to_pickle(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_pickle(os.path.join(edb_directory, filename), **kwargs)


def _file_exists(filename, name, edb_directory):
 filename = filename.format(name=name)
 return os.path.exists(os.path.join(edb_directory, filename))


def get_x_ca_df(alt_values, name, edb_directory, num_chunks):
 def split(a, n):
 k, m = divmod(len(a), n)
 return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

 # process x_ca with cv_to_ca with or without chunking
 x_ca_pickle_file = "{name}_x_ca.pkl"
 if num_chunks == 1:
 x_ca = cv_to_ca(alt_values)
 elif _file_exists(x_ca_pickle_file, name, edb_directory):
 # if pickle file from previous x_ca processing exist, load it to save time
 time_start = datetime.now()
 x_ca = _read_pickle(x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 else:
 time_start = datetime.now()
 # calculate num_chunks based on chunking_size (or max number of rows per chunk)
 chunking_size = round(len(alt_values) / num_chunks, 3)
 print(
 f"Using {num_chunks} chunks results in chunk size of {chunking_size} (of {len(alt_values)} total rows)"
 )
 all_chunk_ids = list(alt_values.index.get_level_values(0).unique())
 split_ids = list(split(all_chunk_ids, num_chunks))
 x_ca_list = []
 for i, chunk_ids in enumerate(split_ids):
 alt_values_i = alt_values.loc[chunk_ids]
 x_ca_i = cv_to_ca(alt_values_i)
 x_ca_list.append(x_ca_i)
 print(
 f"\rx_ca_i compute done for chunk {i+1}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 x_ca = pd.concat(x_ca_list, axis=0)
 # save final x_ca result as pickle file to save time for future data loading
 _to_pickle(x_ca, x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 return x_ca


def nonmand_tour_freq_model(
 edb_directory="output/estimation_data_bundle/{name}/",
 return_data=False,
 condense_parameters=False,
 segment_subset=[],
 num_chunks=1,
):
 """
 Prepare nonmandatory tour frequency models for estimation.
Expand All@@ -141,10 +223,16 @@ def nonmand_tour_freq_model(
 data = interaction_simulate_data(
 name="non_mandatory_tour_frequency",
 edb_directory=edb_directory,
 segment_subset=segment_subset,
 )

 settings = data.settings
 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset
 if condense_parameters:
 data.relabel_coef = link_same_value_coefficients(
 segment_names, data.coefficients, data.spec
Expand All@@ -157,6 +245,7 @@ def nonmand_tour_freq_model(

 m = {}
 for segment_name in segment_names:
 print(f"Creating larch model for {segment_name}")
 segment_model = m[segment_name] = Model()
 # One of the alternatives is coded as 0, so
 # we need to explicitly initialize the MNL nesting graph
Expand All@@ -178,7 +267,15 @@ def nonmand_tour_freq_model(
 .set_index("person_id")
 .rename(columns={"TAZ": "HOMETAZ"})
 )
 x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 print("\t performing cv to ca step")
 # x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 x_ca = get_x_ca_df(
 alt_values=alt_values[segment_name].set_index(["person_id", "variable"]),
 name=segment_name,
 edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"),
 num_chunks=num_chunks,
 )

 d = DataFrames(
 co=x_co,
 ca=x_ca,
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Apr 1, 2024
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BayDAG Contribution #14: Increasing Larch Loading for NMTF
#780
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6a3a332

decrease larch load time for nmtf


dhensle Dec 16, 2023
74212e4

nmtf larch av arg


dhensle Mar 12, 2024

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99 changes: 98 additions & 1 deletion
activitysim/estimation/larch/nonmand_tour_freq.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,6 +7,8 @@
from larch import DataFrames, Model
from larch.log import logger_name
from larch.util import Dict
import pickle
from datetime import datetime

from .general import (
 apply_coefficients,
Expand All@@ -27,6 +29,7 @@ def interaction_simulate_data(
 coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
 chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
 alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
 segment_subset=[],
):
 edb_directory = edb_directory.format(name=name)

Expand All@@ -46,21 +49,30 @@ def _read_csv(filename, **kwargs):
 alt_values = {}

 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset

 for segment_name in segment_names:
 print(f"Loading EDB for {segment_name} segment")
 coefficients[segment_name] = _read_csv(
 coefficients_files.format(name=name, segment_name=segment_name),
 index_col="coefficient_name",
 comment="#",
 )
 chooser_data[segment_name] = _read_csv(
 chooser_data_files.format(name=name, segment_name=segment_name),
 )
 alt_values[segment_name] = _read_csv(
 alt_values_files.format(name=name, segment_name=segment_name),
 comment="#",
 )

 spec = _read_csv(
 spec_file,
 comment="#",
 )
 spec = remove_apostrophes(spec, ["Label"])
 # alt_names = list(spec.columns[3:])
Expand DownExpand Up@@ -118,10 +130,80 @@ def unavail(model, x_ca):
 return unav


# FIXME move all this to larch/general.py? see ActititySim issue #686
def _read_feather(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_feather(os.path.join(edb_directory, filename), **kwargs)


def _to_feather(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_feather(os.path.join(edb_directory, filename), **kwargs)


def _read_pickle(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_pickle(os.path.join(edb_directory, filename), **kwargs)


def _to_pickle(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_pickle(os.path.join(edb_directory, filename), **kwargs)


def _file_exists(filename, name, edb_directory):
 filename = filename.format(name=name)
 return os.path.exists(os.path.join(edb_directory, filename))


def get_x_ca_df(alt_values, name, edb_directory, num_chunks):
 def split(a, n):
 k, m = divmod(len(a), n)
 return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

 # process x_ca with cv_to_ca with or without chunking
 x_ca_pickle_file = "{name}_x_ca.pkl"
 if num_chunks == 1:
 x_ca = cv_to_ca(alt_values)
 elif _file_exists(x_ca_pickle_file, name, edb_directory):
 # if pickle file from previous x_ca processing exist, load it to save time
 time_start = datetime.now()
 x_ca = _read_pickle(x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 else:
 time_start = datetime.now()
 # calculate num_chunks based on chunking_size (or max number of rows per chunk)
 chunking_size = round(len(alt_values) / num_chunks, 3)
 print(
 f"Using {num_chunks} chunks results in chunk size of {chunking_size} (of {len(alt_values)} total rows)"
 )
 all_chunk_ids = list(alt_values.index.get_level_values(0).unique())
 split_ids = list(split(all_chunk_ids, num_chunks))
 x_ca_list = []
 for i, chunk_ids in enumerate(split_ids):
 alt_values_i = alt_values.loc[chunk_ids]
 x_ca_i = cv_to_ca(alt_values_i)
 x_ca_list.append(x_ca_i)
 print(
 f"\rx_ca_i compute done for chunk {i+1}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 x_ca = pd.concat(x_ca_list, axis=0)
 # save final x_ca result as pickle file to save time for future data loading
 _to_pickle(x_ca, x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 return x_ca


def nonmand_tour_freq_model(
 edb_directory="output/estimation_data_bundle/{name}/",
 return_data=False,
 condense_parameters=False,
 segment_subset=[],
 num_chunks=1,
):
 """
 Prepare nonmandatory tour frequency models for estimation.
Expand All@@ -141,10 +223,16 @@ def nonmand_tour_freq_model(
 data = interaction_simulate_data(
 name="non_mandatory_tour_frequency",
 edb_directory=edb_directory,
 segment_subset=segment_subset,
 )

 settings = data.settings
 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset
 if condense_parameters:
 data.relabel_coef = link_same_value_coefficients(
 segment_names, data.coefficients, data.spec
Expand All@@ -157,6 +245,7 @@ def nonmand_tour_freq_model(

 m = {}
 for segment_name in segment_names:
 print(f"Creating larch model for {segment_name}")
 segment_model = m[segment_name] = Model()
 # One of the alternatives is coded as 0, so
 # we need to explicitly initialize the MNL nesting graph
Expand All@@ -178,7 +267,15 @@ def nonmand_tour_freq_model(
 .set_index("person_id")
 .rename(columns={"TAZ": "HOMETAZ"})
 )
 x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 print("\t performing cv to ca step")
 # x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 x_ca = get_x_ca_df(
 alt_values=alt_values[segment_name].set_index(["person_id", "variable"]),
 name=segment_name,
 edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"),
 num_chunks=num_chunks,
 )

 d = DataFrames(
 co=x_co,
 ca=x_ca,
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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BayDAG Contribution #14: Increasing Larch Loading for NMTF#780

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 Merged

jpn--
merged 2 commits into

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Apr 1, 2024
 Merged


BayDAG Contribution #14: Increasing Larch Loading for NMTF
#780
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6a3a332

decrease larch load time for nmtf


dhensle Dec 16, 2023
74212e4

nmtf larch av arg


dhensle Mar 12, 2024

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99 changes: 98 additions & 1 deletion
activitysim/estimation/larch/nonmand_tour_freq.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,6 +7,8 @@
from larch import DataFrames, Model
from larch.log import logger_name
from larch.util import Dict
import pickle
from datetime import datetime

from .general import (
 apply_coefficients,
Expand All@@ -27,6 +29,7 @@ def interaction_simulate_data(
 coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
 chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
 alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
 segment_subset=[],
):
 edb_directory = edb_directory.format(name=name)

Expand All@@ -46,21 +49,30 @@ def _read_csv(filename, **kwargs):
 alt_values = {}

 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset

 for segment_name in segment_names:
 print(f"Loading EDB for {segment_name} segment")
 coefficients[segment_name] = _read_csv(
 coefficients_files.format(name=name, segment_name=segment_name),
 index_col="coefficient_name",
 comment="#",
 )
 chooser_data[segment_name] = _read_csv(
 chooser_data_files.format(name=name, segment_name=segment_name),
 )
 alt_values[segment_name] = _read_csv(
 alt_values_files.format(name=name, segment_name=segment_name),
 comment="#",
 )

 spec = _read_csv(
 spec_file,
 comment="#",
 )
 spec = remove_apostrophes(spec, ["Label"])
 # alt_names = list(spec.columns[3:])
Expand DownExpand Up@@ -118,10 +130,80 @@ def unavail(model, x_ca):
 return unav


# FIXME move all this to larch/general.py? see ActititySim issue #686
def _read_feather(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_feather(os.path.join(edb_directory, filename), **kwargs)


def _to_feather(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_feather(os.path.join(edb_directory, filename), **kwargs)


def _read_pickle(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_pickle(os.path.join(edb_directory, filename), **kwargs)


def _to_pickle(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_pickle(os.path.join(edb_directory, filename), **kwargs)


def _file_exists(filename, name, edb_directory):
 filename = filename.format(name=name)
 return os.path.exists(os.path.join(edb_directory, filename))


def get_x_ca_df(alt_values, name, edb_directory, num_chunks):
 def split(a, n):
 k, m = divmod(len(a), n)
 return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

 # process x_ca with cv_to_ca with or without chunking
 x_ca_pickle_file = "{name}_x_ca.pkl"
 if num_chunks == 1:
 x_ca = cv_to_ca(alt_values)
 elif _file_exists(x_ca_pickle_file, name, edb_directory):
 # if pickle file from previous x_ca processing exist, load it to save time
 time_start = datetime.now()
 x_ca = _read_pickle(x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 else:
 time_start = datetime.now()
 # calculate num_chunks based on chunking_size (or max number of rows per chunk)
 chunking_size = round(len(alt_values) / num_chunks, 3)
 print(
 f"Using {num_chunks} chunks results in chunk size of {chunking_size} (of {len(alt_values)} total rows)"
 )
 all_chunk_ids = list(alt_values.index.get_level_values(0).unique())
 split_ids = list(split(all_chunk_ids, num_chunks))
 x_ca_list = []
 for i, chunk_ids in enumerate(split_ids):
 alt_values_i = alt_values.loc[chunk_ids]
 x_ca_i = cv_to_ca(alt_values_i)
 x_ca_list.append(x_ca_i)
 print(
 f"\rx_ca_i compute done for chunk {i+1}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 x_ca = pd.concat(x_ca_list, axis=0)
 # save final x_ca result as pickle file to save time for future data loading
 _to_pickle(x_ca, x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 return x_ca


def nonmand_tour_freq_model(
 edb_directory="output/estimation_data_bundle/{name}/",
 return_data=False,
 condense_parameters=False,
 segment_subset=[],
 num_chunks=1,
):
 """
 Prepare nonmandatory tour frequency models for estimation.
Expand All@@ -141,10 +223,16 @@ def nonmand_tour_freq_model(
 data = interaction_simulate_data(
 name="non_mandatory_tour_frequency",
 edb_directory=edb_directory,
 segment_subset=segment_subset,
 )

 settings = data.settings
 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset
 if condense_parameters:
 data.relabel_coef = link_same_value_coefficients(
 segment_names, data.coefficients, data.spec
Expand All@@ -157,6 +245,7 @@ def nonmand_tour_freq_model(

 m = {}
 for segment_name in segment_names:
 print(f"Creating larch model for {segment_name}")
 segment_model = m[segment_name] = Model()
 # One of the alternatives is coded as 0, so
 # we need to explicitly initialize the MNL nesting graph
Expand All@@ -178,7 +267,15 @@ def nonmand_tour_freq_model(
 .set_index("person_id")
 .rename(columns={"TAZ": "HOMETAZ"})
 )
 x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 print("\t performing cv to ca step")
 # x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 x_ca = get_x_ca_df(
 alt_values=alt_values[segment_name].set_index(["person_id", "variable"]),
 name=segment_name,
 edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"),
 num_chunks=num_chunks,
 )

 d = DataFrames(
 co=x_co,
 ca=x_ca,
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BayDAG Contribution #14: Increasing Larch Loading for NMTF#780

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 Merged

jpn--
merged 2 commits into

from

Apr 1, 2024
 Merged


BayDAG Contribution #14: Increasing Larch Loading for NMTF
#780
 Changes from all commits
Commits

Show all changes


2 commits


Select commit
Hold shift + click to select a range
6a3a332

decrease larch load time for nmtf


dhensle Dec 16, 2023
74212e4

nmtf larch av arg


dhensle Mar 12, 2024

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99 changes: 98 additions & 1 deletion
activitysim/estimation/larch/nonmand_tour_freq.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,6 +7,8 @@
from larch import DataFrames, Model
from larch.log import logger_name
from larch.util import Dict
import pickle
from datetime import datetime

from .general import (
 apply_coefficients,
Expand All@@ -27,6 +29,7 @@ def interaction_simulate_data(
 coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
 chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
 alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
 segment_subset=[],
):
 edb_directory = edb_directory.format(name=name)

Expand All@@ -46,21 +49,30 @@ def _read_csv(filename, **kwargs):
 alt_values = {}

 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset

 for segment_name in segment_names:
 print(f"Loading EDB for {segment_name} segment")
 coefficients[segment_name] = _read_csv(
 coefficients_files.format(name=name, segment_name=segment_name),
 index_col="coefficient_name",
 comment="#",
 )
 chooser_data[segment_name] = _read_csv(
 chooser_data_files.format(name=name, segment_name=segment_name),
 )
 alt_values[segment_name] = _read_csv(
 alt_values_files.format(name=name, segment_name=segment_name),
 comment="#",
 )

 spec = _read_csv(
 spec_file,
 comment="#",
 )
 spec = remove_apostrophes(spec, ["Label"])
 # alt_names = list(spec.columns[3:])
Expand DownExpand Up@@ -118,10 +130,80 @@ def unavail(model, x_ca):
 return unav


# FIXME move all this to larch/general.py? see ActititySim issue #686
def _read_feather(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_feather(os.path.join(edb_directory, filename), **kwargs)


def _to_feather(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_feather(os.path.join(edb_directory, filename), **kwargs)


def _read_pickle(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_pickle(os.path.join(edb_directory, filename), **kwargs)


def _to_pickle(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_pickle(os.path.join(edb_directory, filename), **kwargs)


def _file_exists(filename, name, edb_directory):
 filename = filename.format(name=name)
 return os.path.exists(os.path.join(edb_directory, filename))


def get_x_ca_df(alt_values, name, edb_directory, num_chunks):
 def split(a, n):
 k, m = divmod(len(a), n)
 return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

 # process x_ca with cv_to_ca with or without chunking
 x_ca_pickle_file = "{name}_x_ca.pkl"
 if num_chunks == 1:
 x_ca = cv_to_ca(alt_values)
 elif _file_exists(x_ca_pickle_file, name, edb_directory):
 # if pickle file from previous x_ca processing exist, load it to save time
 time_start = datetime.now()
 x_ca = _read_pickle(x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 else:
 time_start = datetime.now()
 # calculate num_chunks based on chunking_size (or max number of rows per chunk)
 chunking_size = round(len(alt_values) / num_chunks, 3)
 print(
 f"Using {num_chunks} chunks results in chunk size of {chunking_size} (of {len(alt_values)} total rows)"
 )
 all_chunk_ids = list(alt_values.index.get_level_values(0).unique())
 split_ids = list(split(all_chunk_ids, num_chunks))
 x_ca_list = []
 for i, chunk_ids in enumerate(split_ids):
 alt_values_i = alt_values.loc[chunk_ids]
 x_ca_i = cv_to_ca(alt_values_i)
 x_ca_list.append(x_ca_i)
 print(
 f"\rx_ca_i compute done for chunk {i+1}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 x_ca = pd.concat(x_ca_list, axis=0)
 # save final x_ca result as pickle file to save time for future data loading
 _to_pickle(x_ca, x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 return x_ca


def nonmand_tour_freq_model(
 edb_directory="output/estimation_data_bundle/{name}/",
 return_data=False,
 condense_parameters=False,
 segment_subset=[],
 num_chunks=1,
):
 """
 Prepare nonmandatory tour frequency models for estimation.
Expand All@@ -141,10 +223,16 @@ def nonmand_tour_freq_model(
 data = interaction_simulate_data(
 name="non_mandatory_tour_frequency",
 edb_directory=edb_directory,
 segment_subset=segment_subset,
 )

 settings = data.settings
 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset
 if condense_parameters:
 data.relabel_coef = link_same_value_coefficients(
 segment_names, data.coefficients, data.spec
Expand All@@ -157,6 +245,7 @@ def nonmand_tour_freq_model(

 m = {}
 for segment_name in segment_names:
 print(f"Creating larch model for {segment_name}")
 segment_model = m[segment_name] = Model()
 # One of the alternatives is coded as 0, so
 # we need to explicitly initialize the MNL nesting graph
Expand All@@ -178,7 +267,15 @@ def nonmand_tour_freq_model(
 .set_index("person_id")
 .rename(columns={"TAZ": "HOMETAZ"})
 )
 x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 print("\t performing cv to ca step")
 # x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 x_ca = get_x_ca_df(
 alt_values=alt_values[segment_name].set_index(["person_id", "variable"]),
 name=segment_name,
 edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"),
 num_chunks=num_chunks,
 )

 d = DataFrames(
 co=x_co,
 ca=x_ca,
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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BayDAG Contribution #14: Increasing Larch Loading for NMTF#780

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 Merged

jpn--
merged 2 commits into

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Apr 1, 2024
 Merged


BayDAG Contribution #14: Increasing Larch Loading for NMTF
#780
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6a3a332

decrease larch load time for nmtf


dhensle Dec 16, 2023
74212e4

nmtf larch av arg


dhensle Mar 12, 2024

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99 changes: 98 additions & 1 deletion
activitysim/estimation/larch/nonmand_tour_freq.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,6 +7,8 @@
from larch import DataFrames, Model
from larch.log import logger_name
from larch.util import Dict
import pickle
from datetime import datetime

from .general import (
 apply_coefficients,
Expand All@@ -27,6 +29,7 @@ def interaction_simulate_data(
 coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
 chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
 alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
 segment_subset=[],
):
 edb_directory = edb_directory.format(name=name)

Expand All@@ -46,21 +49,30 @@ def _read_csv(filename, **kwargs):
 alt_values = {}

 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset

 for segment_name in segment_names:
 print(f"Loading EDB for {segment_name} segment")
 coefficients[segment_name] = _read_csv(
 coefficients_files.format(name=name, segment_name=segment_name),
 index_col="coefficient_name",
 comment="#",
 )
 chooser_data[segment_name] = _read_csv(
 chooser_data_files.format(name=name, segment_name=segment_name),
 )
 alt_values[segment_name] = _read_csv(
 alt_values_files.format(name=name, segment_name=segment_name),
 comment="#",
 )

 spec = _read_csv(
 spec_file,
 comment="#",
 )
 spec = remove_apostrophes(spec, ["Label"])
 # alt_names = list(spec.columns[3:])
Expand DownExpand Up@@ -118,10 +130,80 @@ def unavail(model, x_ca):
 return unav


# FIXME move all this to larch/general.py? see ActititySim issue #686
def _read_feather(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_feather(os.path.join(edb_directory, filename), **kwargs)


def _to_feather(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_feather(os.path.join(edb_directory, filename), **kwargs)


def _read_pickle(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_pickle(os.path.join(edb_directory, filename), **kwargs)


def _to_pickle(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_pickle(os.path.join(edb_directory, filename), **kwargs)


def _file_exists(filename, name, edb_directory):
 filename = filename.format(name=name)
 return os.path.exists(os.path.join(edb_directory, filename))


def get_x_ca_df(alt_values, name, edb_directory, num_chunks):
 def split(a, n):
 k, m = divmod(len(a), n)
 return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

 # process x_ca with cv_to_ca with or without chunking
 x_ca_pickle_file = "{name}_x_ca.pkl"
 if num_chunks == 1:
 x_ca = cv_to_ca(alt_values)
 elif _file_exists(x_ca_pickle_file, name, edb_directory):
 # if pickle file from previous x_ca processing exist, load it to save time
 time_start = datetime.now()
 x_ca = _read_pickle(x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 else:
 time_start = datetime.now()
 # calculate num_chunks based on chunking_size (or max number of rows per chunk)
 chunking_size = round(len(alt_values) / num_chunks, 3)
 print(
 f"Using {num_chunks} chunks results in chunk size of {chunking_size} (of {len(alt_values)} total rows)"
 )
 all_chunk_ids = list(alt_values.index.get_level_values(0).unique())
 split_ids = list(split(all_chunk_ids, num_chunks))
 x_ca_list = []
 for i, chunk_ids in enumerate(split_ids):
 alt_values_i = alt_values.loc[chunk_ids]
 x_ca_i = cv_to_ca(alt_values_i)
 x_ca_list.append(x_ca_i)
 print(
 f"\rx_ca_i compute done for chunk {i+1}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 x_ca = pd.concat(x_ca_list, axis=0)
 # save final x_ca result as pickle file to save time for future data loading
 _to_pickle(x_ca, x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 return x_ca


def nonmand_tour_freq_model(
 edb_directory="output/estimation_data_bundle/{name}/",
 return_data=False,
 condense_parameters=False,
 segment_subset=[],
 num_chunks=1,
):
 """
 Prepare nonmandatory tour frequency models for estimation.
Expand All@@ -141,10 +223,16 @@ def nonmand_tour_freq_model(
 data = interaction_simulate_data(
 name="non_mandatory_tour_frequency",
 edb_directory=edb_directory,
 segment_subset=segment_subset,
 )

 settings = data.settings
 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset
 if condense_parameters:
 data.relabel_coef = link_same_value_coefficients(
 segment_names, data.coefficients, data.spec
Expand All@@ -157,6 +245,7 @@ def nonmand_tour_freq_model(

 m = {}
 for segment_name in segment_names:
 print(f"Creating larch model for {segment_name}")
 segment_model = m[segment_name] = Model()
 # One of the alternatives is coded as 0, so
 # we need to explicitly initialize the MNL nesting graph
Expand All@@ -178,7 +267,15 @@ def nonmand_tour_freq_model(
 .set_index("person_id")
 .rename(columns={"TAZ": "HOMETAZ"})
 )
 x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 print("\t performing cv to ca step")
 # x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 x_ca = get_x_ca_df(
 alt_values=alt_values[segment_name].set_index(["person_id", "variable"]),
 name=segment_name,
 edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"),
 num_chunks=num_chunks,
 )

 d = DataFrames(
 co=x_co,
 ca=x_ca,
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BayDAG Contribution #14: Increasing Larch Loading for NMTF#780

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 Merged

jpn--
merged 2 commits into

from

Apr 1, 2024
 Merged


BayDAG Contribution #14: Increasing Larch Loading for NMTF
#780
 Changes from all commits
Commits

Show all changes


2 commits


Select commit
Hold shift + click to select a range
6a3a332

decrease larch load time for nmtf


dhensle Dec 16, 2023
74212e4

nmtf larch av arg


dhensle Mar 12, 2024

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99 changes: 98 additions & 1 deletion
activitysim/estimation/larch/nonmand_tour_freq.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,6 +7,8 @@
from larch import DataFrames, Model
from larch.log import logger_name
from larch.util import Dict
import pickle
from datetime import datetime

from .general import (
 apply_coefficients,
Expand All@@ -27,6 +29,7 @@ def interaction_simulate_data(
 coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
 chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
 alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
 segment_subset=[],
):
 edb_directory = edb_directory.format(name=name)

Expand All@@ -46,21 +49,30 @@ def _read_csv(filename, **kwargs):
 alt_values = {}

 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset

 for segment_name in segment_names:
 print(f"Loading EDB for {segment_name} segment")
 coefficients[segment_name] = _read_csv(
 coefficients_files.format(name=name, segment_name=segment_name),
 index_col="coefficient_name",
 comment="#",
 )
 chooser_data[segment_name] = _read_csv(
 chooser_data_files.format(name=name, segment_name=segment_name),
 )
 alt_values[segment_name] = _read_csv(
 alt_values_files.format(name=name, segment_name=segment_name),
 comment="#",
 )

 spec = _read_csv(
 spec_file,
 comment="#",
 )
 spec = remove_apostrophes(spec, ["Label"])
 # alt_names = list(spec.columns[3:])
Expand DownExpand Up@@ -118,10 +130,80 @@ def unavail(model, x_ca):
 return unav


# FIXME move all this to larch/general.py? see ActititySim issue #686
def _read_feather(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_feather(os.path.join(edb_directory, filename), **kwargs)


def _to_feather(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_feather(os.path.join(edb_directory, filename), **kwargs)


def _read_pickle(filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return pd.read_pickle(os.path.join(edb_directory, filename), **kwargs)


def _to_pickle(df, filename, name, edb_directory, **kwargs):
 filename = filename.format(name=name)
 return df.to_pickle(os.path.join(edb_directory, filename), **kwargs)


def _file_exists(filename, name, edb_directory):
 filename = filename.format(name=name)
 return os.path.exists(os.path.join(edb_directory, filename))


def get_x_ca_df(alt_values, name, edb_directory, num_chunks):
 def split(a, n):
 k, m = divmod(len(a), n)
 return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

 # process x_ca with cv_to_ca with or without chunking
 x_ca_pickle_file = "{name}_x_ca.pkl"
 if num_chunks == 1:
 x_ca = cv_to_ca(alt_values)
 elif _file_exists(x_ca_pickle_file, name, edb_directory):
 # if pickle file from previous x_ca processing exist, load it to save time
 time_start = datetime.now()
 x_ca = _read_pickle(x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 else:
 time_start = datetime.now()
 # calculate num_chunks based on chunking_size (or max number of rows per chunk)
 chunking_size = round(len(alt_values) / num_chunks, 3)
 print(
 f"Using {num_chunks} chunks results in chunk size of {chunking_size} (of {len(alt_values)} total rows)"
 )
 all_chunk_ids = list(alt_values.index.get_level_values(0).unique())
 split_ids = list(split(all_chunk_ids, num_chunks))
 x_ca_list = []
 for i, chunk_ids in enumerate(split_ids):
 alt_values_i = alt_values.loc[chunk_ids]
 x_ca_i = cv_to_ca(alt_values_i)
 x_ca_list.append(x_ca_i)
 print(
 f"\rx_ca_i compute done for chunk {i+1}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 x_ca = pd.concat(x_ca_list, axis=0)
 # save final x_ca result as pickle file to save time for future data loading
 _to_pickle(x_ca, x_ca_pickle_file, name, edb_directory)
 print(
 f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}"
 )
 return x_ca


def nonmand_tour_freq_model(
 edb_directory="output/estimation_data_bundle/{name}/",
 return_data=False,
 condense_parameters=False,
 segment_subset=[],
 num_chunks=1,
):
 """
 Prepare nonmandatory tour frequency models for estimation.
Expand All@@ -141,10 +223,16 @@ def nonmand_tour_freq_model(
 data = interaction_simulate_data(
 name="non_mandatory_tour_frequency",
 edb_directory=edb_directory,
 segment_subset=segment_subset,
 )

 settings = data.settings
 segment_names = [s["NAME"] for s in settings["SPEC_SEGMENTS"]]
 if len(segment_subset) > 0:
 assert set(segment_subset).issubset(
 set(segment_names)
 ), f"{segment_subset} is not a subset of {segment_names}"
 segment_names = segment_subset
 if condense_parameters:
 data.relabel_coef = link_same_value_coefficients(
 segment_names, data.coefficients, data.spec
Expand All@@ -157,6 +245,7 @@ def nonmand_tour_freq_model(

 m = {}
 for segment_name in segment_names:
 print(f"Creating larch model for {segment_name}")
 segment_model = m[segment_name] = Model()
 # One of the alternatives is coded as 0, so
 # we need to explicitly initialize the MNL nesting graph
Expand All@@ -178,7 +267,15 @@ def nonmand_tour_freq_model(
 .set_index("person_id")
 .rename(columns={"TAZ": "HOMETAZ"})
 )
 x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 print("\t performing cv to ca step")
 # x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"]))
 x_ca = get_x_ca_df(
 alt_values=alt_values[segment_name].set_index(["person_id", "variable"]),
 name=segment_name,
 edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"),
 num_chunks=num_chunks,
 )

 d = DataFrames(
 co=x_co,
 ca=x_ca,
Expand Down