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fix: stop using deprecated tsam ClusterConfig(weights=) API #689

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Merged
FBumann merged 1 commit into from
May 29, 2026
Merged

fix: stop using deprecated tsam ClusterConfig(weights=) API #689

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1 change: 1 addition & 0 deletions .github/workflows/docs.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@ on:
paths:
- 'docs/**'
- 'mkdocs.yml'
- 'flixopt/**' # notebooks import flixopt; catch library changes that break docs
workflow_dispatch:
inputs:
deploy:
Expand Down
8 changes: 6 additions & 2 deletions docs/notebooks/data/generate_realistic_profiles.py
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Original file line number Diff line number Diff line change
Expand Up @@ -166,8 +166,12 @@ def generate(
Electricity demand profile in kW
"""
slp_type = self.CONSUMER_TYPES[consumer_type]
e_slp = bdew.ElecSlp(self.year, holidays=self.holidays)
profile = e_slp.get_scaled_power_profiles({slp_type: annual_demand_kwh})
# demandlib calls warnings.simplefilter("error") internally, which would otherwise
# leak into the global state and turn every later warning (e.g. third-party
# DeprecationWarnings) into a hard error. Contain that side effect here.
with warnings.catch_warnings():
e_slp = bdew.ElecSlp(self.year, holidays=self.holidays)
profile = e_slp.get_scaled_power_profiles({slp_type: annual_demand_kwh})
# Resample to hourly and align with requested timesteps
profile_hourly = profile[slp_type].resample('h').mean()
return profile_hourly.reindex(timesteps, method='ffill').values
Expand Down
39 changes: 23 additions & 16 deletions flixopt/transform_accessor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -900,8 +900,12 @@ def _build_cluster_config_with_weights(
cluster: ClusterConfig | None,
auto_weights: dict[str, float],
available_columns: set[str] | None = None,
) -> ClusterConfig:
"""Merge auto-calculated weights into ClusterConfig.
) -> tuple[ClusterConfig, dict[str, float]]:
"""Resolve clustering weights and build a ClusterConfig without weights.

Weights are returned separately so they can be passed as a top-level
argument to ``tsam.aggregate``. Passing weights via ``ClusterConfig`` is
deprecated in tsam (>= 3.4.0) and raises a ``DeprecationWarning``.

Args:
cluster: Optional user-provided ClusterConfig.
Expand All @@ -912,7 +916,7 @@ def _build_cluster_config_with_weights(
(e.g., constant arrays removed before clustering).

Returns:
ClusterConfig with weights set (either user-provided or auto-calculated).
Tuple of (ClusterConfig without weights, resolved weights dict).
"""
from tsam import ClusterConfig

Expand All @@ -926,19 +930,21 @@ def _build_cluster_config_with_weights(
if available_columns is not None:
weights = {name: w for name, w in weights.items() if name in available_columns}

# No ClusterConfig provided - use defaults with weights
# No ClusterConfig provided - use defaults (weights passed to aggregate())
if cluster is None:
return ClusterConfig(weights=weights)

# ClusterConfig provided - use its settings with (possibly filtered) weights
return ClusterConfig(
method=cluster.method,
representation=cluster.representation,
weights=weights,
normalize_column_means=cluster.normalize_column_means,
use_duration_curves=cluster.use_duration_curves,
include_period_sums=cluster.include_period_sums,
solver=cluster.solver,
return ClusterConfig(), weights

# ClusterConfig provided - preserve its settings; weights passed to aggregate()
return (
ClusterConfig(
method=cluster.method,
representation=cluster.representation,
normalize_column_means=cluster.normalize_column_means,
use_duration_curves=cluster.use_duration_curves,
include_period_sums=cluster.include_period_sums,
solver=cluster.solver,
),
weights,
)
Comment on lines +933 to 948
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@coderabbitai coderabbitai Bot May 29, 2026

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⚠️ Potential issue | 🟠 Major | ⚡ Quick win

🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
# Confirm ClusterConfig is a dataclass and weights=None does not warn
rg -nP --no-heading 'tsam' pyproject.toml setup.cfg setup.py requirements*.txt 2>/dev/null
pip install 'tsam==3.4.0' >/dev/null 2>&1 && python - <<'PY'
import dataclasses, warnings
from tsam import ClusterConfig
print("is_dataclass:", dataclasses.is_dataclass(ClusterConfig))
print("fields:", [f.name for f in dataclasses.fields(ClusterConfig)])
base = ClusterConfig(weights={'a': 1.0})
with warnings.catch_warnings(record=True) as w:
    warnings.simplefilter('always')
    dataclasses.replace(base, weights=None)
    print("warnings on replace(weights=None):", [str(x.category.__name__) for x in w])
PY

Repository: flixOpt/flixopt

Length of output: 849

🏁 Script executed:

#!/bin/bash
set -euo pipefail

# 1) Inspect tsam==3.4.0 aggregate signature for weights=
python - <<'PY'
import inspect
import tsam
from tsam import aggregate
print("tsam version:", getattr(tsam, "__version__", "unknown"))
print("aggregate:", aggregate)
print("aggregate signature:", inspect.signature(aggregate))
PY

# 2) Try calling aggregate with weights= kwarg to see if it raises TypeError
python - <<'PY'
import numpy as np
import traceback
from tsam import aggregate

data = np.arange(12, dtype=float).reshape(6,2)  # minimal 2-column time series-like data
kwargs = dict(
    data=data,
    n_clusters=2,
    weights={"x": 1.0},   # if accepted, should not TypeError; if not, should complain
    # keep other params default
)

try:
    aggregate(**kwargs)
    print("aggregate accepted weights= (call did not TypeError)")
except TypeError as e:
    print("TypeError for weights=:", e)
except Exception as e:
    # if it validates weights and fails later, still means weights kwarg is accepted
    print("Non-TypeError raised (weights kwarg likely accepted):", type(e).__name__, e)
    # print a short traceback line
    tb = traceback.format_exc().splitlines()
    print("traceback tail:", "\n".join(tb[-3:]))
PY

# 3) Repo checks around reserved_tsam_keys and cluster()/aggregate() call paths
rg -n "reserved_tsam_keys" -S flixopt/transform_accessor.py
rg -n "aggregate\\(" -S flixopt/transform_accessor.py
rg -n "weights" -S flixopt/transform_accessor.py | head -n 80

# Show the relevant sections (bounded)
python - <<'PY'
from pathlib import Path
p = Path("flixopt/transform_accessor.py")
lines = p.read_text().splitlines()
def show(start, end):
    for i in range(start, end+1):
        print(f"{i}:{lines[i-1]}")
print("\n--- reserved_tsam_keys region ---")
show(1700, 1760)
print("\n--- _build_cluster_config_with_weights region ---")
show(880, 980)
print("\n--- cluster() / aggregate() call region ---")
show(1760, 1860)
PY

Repository: flixOpt/flixopt

Length of output: 18014

🏁 Script executed:

#!/bin/bash
set -euo pipefail

# Find cluster() definition header and where tsam_kwargs is handled
rg -n "def cluster\\(" -S flixopt/transform_accessor.py
rg -n "tsam_kwargs" -S flixopt/transform_accessor.py

python - <<'PY'
from pathlib import Path
p=Path("flixopt/transform_accessor.py")
lines=p.read_text().splitlines()
# print a window around first occurrence of 'def cluster(' and around where tsam_kwargs is described/used
def show(center, span=80):
    start=max(1, center-span)
    end=min(len(lines), center+span)
    for i in range(start, end+1):
        print(f"{i}:{lines[i-1]}")
for idx,line in enumerate(lines, start=1):
    if "def cluster(" in line:
        print("\n--- def cluster() context ---")
        show(idx, 120)
        break
# find line that contains 'Additional keyword arguments passed'
for idx,line in enumerate(lines, start=1):
    if "Additional keyword arguments passed to" in line:
        print("\n--- tsam_kwargs doc context ---")
        show(idx, 120)
        break
PY

Repository: flixOpt/flixopt

Length of output: 25359

🏁 Script executed:

#!/bin/bash
set -euo pipefail

# Find cluster() definition header and where tsam_kwargs is handled
rg -n "def cluster\\(" -S flixopt/transform_accessor.py
rg -n "tsam_kwargs" -S flixopt/transform_accessor.py

python - <<'PY'
from pathlib import Path
p=Path("flixopt/transform_accessor.py")
lines=p.read_text().splitlines()

def show(center, span=80):
    start=max(1, center-span)
    end=min(len(lines), center+span)
    for i in range(start, end+1):
        print(f"{i}:{lines[i-1]}")

# cluster() context
for idx,line in enumerate(lines, start=1):
    if "def cluster(" in line:
        print("\n--- def cluster() context ---")
        show(idx, 130)
        break

# tsam_kwargs doc context
for idx,line in enumerate(lines, start=1):
    if "Additional keyword arguments passed to ``tsam.aggregate()``" in line:
        print("\n--- tsam_kwargs doc context ---")
        show(idx, 130)
        break

# where tsam_kwargs is likely passed in / constructed (look for **tsam_kwargs usage sites)
for idx,line in enumerate(lines, start=1):
    if "**tsam_kwargs" in line:
        print(f"\n--- **tsam_kwargs usage around line {idx} ---")
        show(idx, 60)
PY

Repository: flixOpt/flixopt

Length of output: 47349

🏁 Script executed:

#!/bin/bash
set -euo pipefail

python - <<'PY'
import warnings
import numpy as np
import pandas as pd
from tsam import aggregate, ClusterConfig

# minimal time series-like DataFrame for tsam
df = pd.DataFrame(np.arange(12, dtype=float).reshape(6,2), columns=["x","y"])

base = ClusterConfig(
    method="hierarchical",
    representation="medoid",
    weights={"x": 1.0},  # non-None to show deprecation behavior
)

# 1) Passing weights via ClusterConfig may warn (depending on tsam)
with warnings.catch_warnings(record=True) as w:
    warnings.simplefilter("always")
    try:
        aggregate(
            df,
            n_clusters=2,
            period_duration=24,
            temporal_resolution=None,
            cluster=base,
            weights=None,
        )
    except Exception:
        # ignore validation errors from tsam internals; we only care about DeprecationWarning emission
        pass
    dep_names = sorted({x.category.__name__ for x in w})
    print("warnings categories (cluster_config has weights):", dep_names)

# 2) weights=None in ClusterConfig + top-level weights should not warn
cfg_no_weights = ClusterConfig(
    method=base.method,
    representation=base.representation,
    normalize_column_means=base.normalize_column_means,
    use_duration_curves=base.use_duration_curves,
    include_period_sums=base.include_period_sums,
    solver=base.solver,
    weights=None,
)

with warnings.catch_warnings(record=True) as w:
    warnings.simplefilter("always")
    try:
        aggregate(
            df,
            n_clusters=2,
            period_duration=24,
            temporal_resolution=None,
            cluster=cfg_no_weights,
            weights={"x": 1.0},
        )
    except Exception:
        pass
    dep_names = sorted({x.category.__name__ for x in w})
    print("warnings categories (cluster_config weights=None):", dep_names)
PY

Repository: flixOpt/flixopt

Length of output: 426

🏁 Script executed:

#!/bin/bash
set -euo pipefail

python - <<'PY'
import numpy as np
import pandas as pd
from tsam import aggregate, ClusterConfig

# Create a small hourly time series with a datetime index
idx = pd.date_range("2020-01-01", periods=48, freq="h")  # 2 days
df = pd.DataFrame(
    {
        "x": np.sin(np.linspace(0, 10, len(idx))) + 2.0,
        "y": np.cos(np.linspace(0, 10, len(idx))) + 5.0,
    },
    index=idx,
)

# Case A: top-level weights= accepted
cfg = ClusterConfig(method="hierarchical", representation="medoid", weights=None)
res = aggregate(
    df,
    n_clusters=2,
    period_duration=24,
    temporal_resolution="1h",
    cluster=cfg,
    weights={"x": 1.0, "y": 2.0},
)
print("aggregate(weights=...) succeeded:", type(res).__name__)

# Case B: ensure python doesn't crash due to duplicate weights at call site
try:
    aggregate(
        df,
        n_clusters=2,
        period_duration=24,
        temporal_resolution="1h",
        cluster=cfg,
        weights={"x": 1.0},
        **{"weights": {"x": 2.0}},   # would normally cause "multiple values" at call site
    )
except TypeError as e:
    print("duplicate weights via **tsam_kwargs would TypeError:", e)
PY

Repository: flixOpt/flixopt

Length of output: 496

🏁 Script executed:

rg -n "^import dataclasses\\b|\\bfrom dataclasses import\\b" flixopt/transform_accessor.py

Repository: flixOpt/flixopt

Length of output: 41

🏁 Script executed:

#!/bin/bash
set -euo pipefail

rg -n "\\bdataclasses\\b" -S flixopt | head -n 50

Repository: flixOpt/flixopt

Length of output: 369

Refactor ClusterConfig reconstruction + guard tsam_kwargs from passing weights

  • For flixopt/transform_accessor.py _build_cluster_config_with_weights (933-948): tsam.ClusterConfig is a dataclass and dataclasses.replace(cluster, weights=None) does not emit a DeprecationWarning on tsam==3.4.0; prefer this over manually re-listing every field for future-proofing.
♻️ Proposed refactor 
+        import dataclasses
+
         # No ClusterConfig provided - use defaults (weights passed to aggregate())
         if cluster is None:
             return ClusterConfig(), weights

-        # ClusterConfig provided - preserve its settings; weights passed to aggregate()
-        return (
-            ClusterConfig(
-                method=cluster.method,
-                representation=cluster.representation,
-                normalize_column_means=cluster.normalize_column_means,
-                use_duration_curves=cluster.use_duration_curves,
-                include_period_sums=cluster.include_period_sums,
-                solver=cluster.solver,
-            ),
-            weights,
-        )
+        # ClusterConfig provided - drop only weights, preserve every other field
+        # (incl. fields added in future tsam versions). weights=None should not
+        # trigger the deprecation warning.
+        return dataclasses.replace(cluster, weights=None), weights
- `tsam.api.aggregate` in `tsam==3.4.0` does accept top-level `weights=...` (so `weights=resolved_weights` at the `tsam.aggregate(...)` call is fine), but `cluster()`’s `reserved_tsam_keys` should also include `'weights'`—otherwise passing `weights` via `**tsam_kwargs` causes `TypeError: got multiple values for keyword argument 'weights'`.
🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@flixopt/transform_accessor.py` around lines 933 - 948, Replace the manual
re-construction of ClusterConfig in _build_cluster_config_with_weights with
dataclasses.replace(cluster, weights=None) to avoid re-listing fields (use
ClusterConfig via dataclasses.replace). Also ensure the code that builds
tsam_kwargs/reserved_tsam_keys excludes top-level 'weights' by adding 'weights'
to reserved_tsam_keys (so weights is passed only via the explicit weights=...
arg to tsam.aggregate and not via **tsam_kwargs), preventing "multiple values
for keyword argument 'weights'".


def sel(
Expand Down Expand Up @@ -1786,7 +1792,7 @@ def to_clean_key(period_label, scenario_label) -> tuple:

# Build ClusterConfig with auto-calculated weights, filtered to available columns
clustering_weights = self._calculate_clustering_weights(ds_slice)
cluster_config = self._build_cluster_config_with_weights(
cluster_config, resolved_weights = self._build_cluster_config_with_weights(
cluster, clustering_weights, available_columns=set(df_for_clustering.columns)
)

Expand All @@ -1797,6 +1803,7 @@ def to_clean_key(period_label, scenario_label) -> tuple:
period_duration=hours_per_cluster,
temporal_resolution=dt,
cluster=cluster_config,
weights=resolved_weights,
extremes=extremes,
segments=segments,
preserve_column_means=preserve_column_means,
Expand Down
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