---

Price Contour finds optimal price scenario values across a portfolio of insurance risks subject to business constraints. Give it a scored dataset with objective and constraint values at discrete price points, and it returns the scenario value per quote that maximises your objective while respecting every constraint.

The core algorithm is Lagrangian dual decomposition, implemented in Rust for speed and exposed to Python via zero-copy Polars DataFrames. A portfolio of 1M+ risks solves in seconds.

---

Quick start

uv add price-contour

import polars as pl
import price_contour as pc

# Long-format DataFrame: one row per (quote, price_scenario)
# with pre-computed objective and constraint values
df = pl.read_parquet("scored_quotes.parquet")

optimiser = pc.OnlineOptimiser(
    objective="income",
    constraints={"volume": {"min_pct": 0.90}},  # retain at least 90% of baseline volume
    quote_id="quote_id",
    scenario_index="scenario_index",
    scenario_value="scenario_value",
)

result = optimiser.solve(df)

print(result.converged)        # True
print(result.iterations)       # 23
print(result.lambdas)          # {'volume': 0.147}
print(result.total_objective)  # 1_284_302.5

# Per-quote optimal scenario values as a Polars DataFrame
out = result.dataframe
print(out.head())
# ┌──────────┬──────────────┬────────────────────┬─────────────────────┬──────────────────┐
# │ quote_id │ optimal_step │ optimal_scenario_value │ optimal_income      │ optimal_volume   │
# ╞══════════╪══════════════╪════════════════════╪═════════════════════╪══════════════════╡
# │ Q001     │ 14           │ 1.07               │ 42.30               │ 0.82             │
# │ Q002     │ 11           │ 0.98               │ 18.55               │ 0.91             │
# └──────────┴──────────────┴────────────────────┴─────────────────────┴──────────────────┘

---

What it does

Price Contour operates on pre-computed scenario data. It does not fit models or generate demand curves. Upstream, your pricing pipeline scores every quote at a grid of price scenario values (e.g. 0.8, 0.85, 0.9, ..., 1.2) and computes what the expected income, volume, loss ratio, etc. would be at each point. Price Contour then selects the optimal scenario value per quote across the portfolio.

The input is a long-format Polars DataFrame:

quote_id	scenario_index	scenario_value	income	volume	loss_ratio
Q001	0	0.80	85.2	0.95	0.62
Q001	1	0.90	92.1	0.88	0.59
Q001	2	1.00	100.0	0.80	0.60
Q002	0	0.80	42.0	0.97	0.58
...	...	...	...	...	...

The output is one optimal scenario value per quote, chosen to maximise portfolio-level income while keeping portfolio-level volume above 90% of baseline (or whatever constraints you set).

---

Three optimisation modes

Online optimisation

Find the optimal scenario value per individual quote. Each quote independently picks its best price point, coordinated by shared Lagrange multipliers that enforce portfolio-level constraints.

optimiser = pc.OnlineOptimiser(
    objective="income",
    constraints={
        "volume": {"min_pct": 0.90},                # sum constraint
        "loss_ratio": {                              # ratio constraint
            "numerator":   "incurred",
            "denominator": "premium",
            "max":         0.65,
        },
    },
)
result = optimiser.solve(df)
print(result.lambdas)            # {'volume': 0.147, 'loss_ratio': 1.21}
print(result.total_constraints)  # {'volume': 5400.0, 'loss_ratio': 0.6498}

Both sum and ratio constraints work in all three optimisation modes (online, ratebook, apply) and in the efficient-frontier sweep.

Ratebook optimisation

Find optimal rating factors across rating dimensions. Instead of individual scenario values, find the best factor value for each level of each rating factor (e.g. age band, region, vehicle power), applied uniformly to all quotes sharing that level.

optimiser = pc.RatebookOptimiser(
    objective="income",
    constraints={"volume": {"min_pct": 0.90}},
    factor_columns=[["age_band"], ["region"], ["vehicle_power"]],
)

result = optimiser.solve(df, factors=factor_df)

print(result.factor_tables)
# {'age_band': {'18-25': 1.15, '26-35': 1.02, '36-50': 0.95, '51+': 0.98},
#  'region': {'London': 1.08, 'South East': 1.01, 'North': 0.93},
#  'vehicle_power': {'Low': 0.97, 'Medium': 1.0, 'High': 1.06}}

# Per-quote evaluation of the solution: step, scenario value, objective,
# constraints, factor product and clamp flags for every quote.
result.quote_results

# Re-evaluate any factor tables exactly (e.g. a loaded result or a frontier point)
evaluation = optimiser.evaluate(df, factor_df, result.factor_tables)

# Save to disk
result.save("parameters/")

# Convert to rating-step DataFrames
tables = result.to_rating_entries()

Every reported ratebook total is the canonical evaluation of the final factor tables: each quote is priced at the grid step nearest the f32 product of its factor values (products outside the grid clamp to the end steps; an exact midpoint goes to the lower step). evaluate() runs the same kernel, so it reproduces a result's totals and quote_results exactly.

Live scoring with stored lambdas

Apply pre-computed Lagrange multipliers to new quotes in a single forward pass, with no iteration. Use this in production to score individual quotes using lambdas learned from a batch solve.

# Batch solve (offline)
result = optimiser.solve(df_portfolio)
lambdas = result.lambdas

# Live scoring (per-quote, no iteration)
applier = pc.ApplyOptimiser(
    lambdas=lambdas,
    objective="income",
    constraints={"volume": {"min_pct": 0.90}},
)
applier.save("config/applier.json")

# Later, in production:
applier = pc.ApplyOptimiser.load("config/applier.json")
live_result = applier.apply(df_single_quote)
optimal_scenario_value = live_result.dataframe["optimal_scenario_value"][0]

---

Efficient frontier

Sweep constraint thresholds to generate the Pareto frontier - the trade-off curve between your objective and constraints. Each point on the frontier is a full portfolio solve at a different constraint target.

frontier = optimiser.frontier(
    df,
    threshold_ranges={"volume": (0.85, 1.0)},
    n_points_per_dim=20,
)

# DataFrame with one row per frontier point (columns abridged; every
# constraint also has an absolute `bound_<name>` column)
print(frontier.points)
# ┌──────────────────┬─────────────────┬──────────────┬───────────────┬────────────┬───────────┬─────────┬─────────────────┐
# │ threshold_volume │ total_objective │ total_volume │ lambda_volume │ iterations │ converged │ sv_mean │ sv_pct_increase │
# ╞══════════════════╪═════════════════╪══════════════╪═══════════════╪════════════╪═══════════╪═════════╪═════════════════╡
# │ 0.85             │ 1_350_102       │ 0.851        │ 0.089         │ 18         │ true      │ 1.04    │ 0.62            │
# │ 0.86             │ 1_342_891       │ 0.861        │ 0.102         │ 21         │ true      │ 1.03    │ 0.58            │
# │ ...              │ ...             │ ...          │ ...           │ ...        │ ...       │ ...     │ ...             │
# └──────────────────┴─────────────────┴──────────────┴───────────────┴────────────┴───────────┴─────────┴─────────────────┘

Adjacent points are warm-started from each other (nearest-neighbour traversal of the threshold grid), so the full frontier solves much faster than running each point independently. Each point also includes scenario value distribution statistics (sv_mean, sv_std, percentiles, sv_pct_increase/sv_pct_decrease).

threshold_<name> is in the units of threshold_ranges (a fraction of baseline for min_pct/max_pct); bound_<name> is the absolute bound the point was solved against, for every constraint, swept or not. frontier_points_schema(mode, constraint_names) returns the exact column schema.

A ratebook frontier keeps each point's factor tables (frontier.point_factor_tables(i)); optimiser.evaluate(grid, factors, frontier.point_factor_tables(i)) reproduces row i exactly. Re-solving with a row's λ is not guaranteed to land on the same tables.

Sweeping a ratio target — declare the constraint with None so the constructor doesn't fix it, then supply the range to frontier():

optimiser = pc.OnlineOptimiser(
    objective="income",
    constraints={
        "loss_ratio": {
            "numerator":   "incurred",
            "denominator": "premium",
            "max":         None,       # frontier supplies the target
        },
    },
)
frontier = optimiser.frontier(
    df,
    threshold_ranges={"loss_ratio": (0.55, 0.75)},
    n_points_per_dim=10,
)
# points["threshold_loss_ratio"] = [0.55, 0.572, ..., 0.75]  (user units, verbatim)
# points["total_loss_ratio"]     = actual Σ incurred / Σ premium at each optimum

Mixed sweep — sweep multiple constraints at once via the cartesian product:

frontier = optimiser.frontier(
    df,
    threshold_ranges={
        "volume":     (8000, 12000),    # absolute units
        "loss_ratio": (0.55, 0.75),     # absolute ratio targets
    },
    n_points_per_dim=10,
)
# 10 × 10 = 100 frontier points

Constraints with numeric thresholds may be omitted from threshold_ranges — they are held fixed at the constructor value across the sweep. None thresholds must have a range entry.

---

Constraint format

Constraints are specified as a dictionary. There are two shapes:

Sum constraints apply to a single column. The dict key is the column name in your DataFrame, the value specifies direction and threshold. Use min / max for absolute thresholds and min_pct / max_pct for thresholds expressed as a fraction of baseline (the portfolio totals at the baseline step: the scenario value nearest 1.0, compared in f32, lowest on a tie):

constraints = {
    "volume":  {"min_pct": 0.90},     # portfolio volume >= 90% of baseline
    "premium": {"min": 1_000_000},    # absolute: portfolio premium >= 1M
    "claims":  {"max_pct": 1.05},     # portfolio claims <= 105% of baseline
}

Ratio constraints apply to a ratio of two summed columns (e.g. loss ratio = Σ incurred / Σ premium). The dict key is a display label (does NOT need to be a column); numerator and denominator name the columns:

constraints = {
    "loss_ratio": {
        "numerator":   "incurred",
        "denominator": "premium",
        "max":         0.65,           # portfolio loss ratio <= 0.65
    },
    "combined_ratio": {
        "numerator":   "claims_plus_expenses",
        "denominator": "premium",
        "max_pct":     1.10,           # <= 110% of baseline combined ratio
    },
}

Internally, ratio constraints are linearised as Σ (num − L·denom) ≤ 0 and handed to the same Lagrangian solver. Setting Σ_baseline denom == 0 raises ValueError for _pct modes (baseline ratio undefined). If Σ_optimum denom == 0 at the chosen step set, the ratio reported in total_constraints[label] and summary() is nan (sentinel; the divide is undefined, not silently zero).

None thresholds mark frontier-only constraints — the threshold is supplied by the sweep range:

constraints = {
    "loss_ratio": {
        "numerator":   "incurred",
        "denominator": "premium",
        "max":         None,           # frontier supplies the target
    },
}

frontier = optimiser.frontier(
    df,
    threshold_ranges={"loss_ratio": (0.55, 0.75)},
    n_points_per_dim=10,
)

solve() rejects None thresholds; frontier() requires a threshold_ranges entry for every None constraint. Numeric-threshold constraints are optional in threshold_ranges — omitted ones are held fixed at their constructor value across the sweep.

points["threshold_<name>"] reports the user-supplied range value verbatim (absolute units for min/max, fractions of baseline for min_pct/max_pct); points["total_<name>"] reports the actual aggregate at the optimum (the actual ratio for ratio constraints).

---

Direct Parquet loading

For large datasets, build the internal grid directly from a Parquet file without materialising a DataFrame in Python memory:

grid = pc.build_grid_from_parquet(
    "scored_quotes.parquet",
    constraint_columns=["volume", "loss_ratio"],
    objective="income",
)
result = optimiser.solve(grid)

For parquets that exceed available memory in their raw form, use the streaming variant. The IO buffer is bounded by chunk_size; the file is read in row slices via Polars' with_slice pushdown so only the row groups overlapping each slice are deserialised, and column projection means only the four schema columns plus the requested constraint columns are decoded:

grid = pc.build_grid_from_parquet_chunked(
    "huge_scored_quotes.parquet",
    constraint_columns=["volume", "loss_ratio"],
    chunk_size=500_000,         # rows per IO slice; rounded down to a multiple of n_steps
    objective="income",
    # n_steps=20,               # optional: lock upfront if your first slice could be partial
)
result = optimiser.solve(grid)

The final QuoteGrid is still O(n_quotes × n_steps × n_columns × 4 bytes) — that's inherent to the solver's flat data layout — but the parquet decode buffer never exceeds chunk_size rows. Use this when the parquet itself doesn't fit in RAM, not as a way to avoid loading the grid.

---

Incremental grid building

For datasets streamed from upstream pipelines (e.g. when chunks arrive out-of-order or before the full dataset is materialised anywhere), build the grid incrementally:

builder = pc.QuoteGridBuilder(
    ["volume", "loss_ratio"],
    quote_id="quote_id",
    scenario_index="scenario_index",
    scenario_value="scenario_value",
    objective="income",
    # n_steps=20,               # optional: lock upfront for streaming sources
)

for chunk in upstream:          # any iterable of pl.DataFrame
    builder.append(chunk)

grid = builder.build()
result = optimiser.solve(grid)

Per-chunk contract: each chunk's rows must already be grouped by quote_id (each quote occupies n_steps contiguous rows in scenario_index order). Within a chunk this is validated row-by-row (including a scenario_value consistency check against the canonical grid). Across chunks the order is arbitrary — the builder performs an in-place sort by quote_id at build() time using cycle-following permutation, so peak memory does not double during the sort. Duplicate quote_ids across all appended chunks are detected and reported with both append-order indices.

The optional n_steps kwarg lets streaming pipelines that may receive a partial first chunk lock the contract upfront, skipping the auto-detection probe.

---

Streaming apply to disk

For live scoring on inputs too large to hold in RAM, stream a parquet through apply and write per-quote results to a parquet output one row group per chunk:

result = pc.apply_lambdas_to_parquet_chunked(
    parquet_in="huge_scored_quotes.parquet",
    parquet_out="scored_results.parquet",
    lambdas={"volume": 0.147, "loss_ratio": 1.21},
    constraints={
        "volume": {"min_pct": 0.90},
        "loss_ratio": {"max_pct": 1.05},
    },
    chunk_size=500_000,
)

# Aggregate totals on the result; per-quote rows are in the output parquet.
print(result.total_objective)         # 1_284_302.5
print(result.total_constraints)       # {'volume': 5400.0, 'loss_ratio': 0.6498}
print(result.output_path)             # 'scored_results.parquet'

# Read back per-quote results lazily.
opt = pl.scan_parquet(result.output_path)

The whole-portfolio optimal_steps array is never materialised — only one chunk's optimal_steps is alive at a time (chunk_size / n_steps entries), and gets dropped along with the chunk's mini-grid after the row group has been written. Aggregate totals accumulate in f64 across chunks. On any error the partial output is best-effort deleted so callers never observe a corrupt artefact, and the input/output paths are checked for equality so the input parquet can't be silently overwritten. Lambda keys not matching any constraint are rejected up front (matching ApplyOptimiser). Ratio constraints are rejected on this path — use ApplyOptimiser.apply(df) on a DataFrame instead, since the per-chunk mini-grid can't carry the raw numerator/denominator columns.

---

MLflow integration

Both OnlineOptimiser and RatebookOptimiser produce MLflow-ready summaries:

result = optimiser.solve(df)
summary = optimiser.summary(result)

import mlflow
mlflow.log_params(summary["params"])
mlflow.log_metrics(summary["metrics"])
mlflow.log_dict(summary["artifacts"]["lambdas"], "lambdas.json")
mlflow.log_dict(summary["artifacts"]["config"], "config.json")

---

How it works

The algorithm

Price Contour solves the constrained optimisation problem:

Maximise    sum_i  objective(quote_i, scenario_value_i)
Subject to  sum_i  constraint_k(quote_i, scenario_value_i) >= threshold_k   for all k
            scenario_value_i in {discrete grid}

This is a combinatorial problem (each quote picks from M discrete scenario values). Lagrangian dual decomposition relaxes the coupling constraints into the objective using dual variables (lambdas), decomposing it into N independent per-quote subproblems:

For fixed lambdas:
    Each quote picks:  argmax_m [ objective(i, m) + sum_k lambda_k * constraint_k(i, m) ]

These are independent and embarrassingly parallel.

The outer loop updates lambdas via the subgradient method with adaptive step sizes, iterating until all constraints are satisfied and lambdas converge.

Performance

The Rust core uses:

• Quote-major memory layout - each quote's M scenario values are contiguous, optimising the per-quote argmax inner loop for cache locality
• Rayon parallelism - the argmax across quotes is parallelised in grain sizes of 4096 quotes
• Adaptive step scaling - per-constraint scale factors normalise for differing magnitudes, so the algorithm works equally well for constraints ranging from 0.1 to 1,000,000
• Lambda averaging - smooths the oscillations inherent in discrete Lagrangian relaxation where all quotes can flip simultaneously

Ratebook mode

For ratebook optimisation, coordinate descent iterates over rating factors. For each factor, a grouped Lagrangian solve finds the best discrete factor value per group (e.g. per age band), with the individual quote scenario value computed as the product of all factor values times a per-quote residual. The inner grouped solve uses the same Lagrangian machinery with remapping to the nearest grid point. After the last pass, the final factor tables are evaluated once by the canonical kernel, which supplies every reported number.

converged on a ratebook result means only that the factor values stopped moving (max change below cd_tolerance); it does not check that the constraints are met. Compare total_constraints with constraint_bounds for that.

---

Architecture

price-contour/
├── crates/
│   ├── price-contour-core/        # Pure Rust: algorithms, data structures, solver
│   │   └── src/
│   │       ├── data.rs            # QuoteGrid, SolverConfig, SolveResult, GroupMapping
│   │       ├── solver/
│   │       │   ├── online.rs      # Lagrangian dual decomposition
│   │       │   ├── grouped.rs     # Grouped solve (ratebook inner loop)
│   │       │   ├── argmax.rs      # Per-quote Lagrangian argmax (parallel)
│   │       │   ├── lambda.rs      # Subgradient lambda updates
│   │       │   └── apply.rs       # Fixed-lambda forward pass
│   │       ├── frontier.rs        # Efficient frontier sweeping
│   │       ├── constants.rs       # Solver defaults
│   │       └── error.rs           # Error types
│   └── price-contour/             # PyO3 bindings (thin wrappers)
│       └── src/
│           ├── solver_py.rs       # DataFrame ingestion + solve
│           ├── grouped_py.rs      # Grouped solve bindings
│           ├── apply_py.rs        # Apply bindings
│           ├── frontier_py.rs     # Frontier bindings
│           ├── builder_py.rs      # QuoteGridBuilder bindings
│           ├── grid_py.rs         # QuoteGrid bindings
│           └── parquet_grid_py.rs # Parquet → QuoteGrid loader
├── python/
│   └── price_contour/
│       ├── solver.py              # OnlineOptimiser, ratio linearisation, validation
│       ├── ratebook.py            # RatebookOptimiser + RatebookResult
│       ├── apply.py               # ApplyOptimiser + apply_from_grid
│       ├── frontier.py            # FrontierResult helpers + frontier_summary
│       ├── builder.py             # QuoteGridBuilder wrapper
│       ├── _ratio_results.py      # Shared ratio reporting (actual ratios + column stitching)
│       └── _frontier_helpers.py   # Shared frontier orchestrator (used by online + ratebook)
├── tests/
│   └── python/                    # Integration tests
├── notebooks/                     # Demo notebooks
├── docs/                          # Design documentation
└── scripts/                       # Utility scripts

The pure-Rust core (price-contour-core) has no Python dependencies and can be tested independently with cargo test. The PyO3 crate (price-contour) is a thin binding layer that converts between Polars DataFrames and the internal QuoteGrid representation with zero-copy where possible.

---

Development

# Clone
git clone https://github.com/PricingFrontier/price-contour.git
cd price-contour

# Install in development mode (compiles Rust, links Python)
uv sync --all-groups
maturin develop

# Run Rust tests
cargo test

# Run Python tests
pytest

# Rebuild after Rust changes
maturin develop

Requirements: Rust toolchain (stable), Python 3.10+, maturin.

---

API reference

OnlineOptimiser

Method	Description
solve(df_or_grid, *, lambdas=None)	Run full optimisation. Returns SolveResult. Ratio constraints require a DataFrame (the linearisation needs raw numerator/denominator columns); a pre-built QuoteGrid with ratio constraints raises ValueError.
frontier(df_or_grid, *, threshold_ranges, n_points_per_dim=10, initial_lambdas=None)	Sweep the efficient frontier. Returns FrontierResult. Numeric thresholds are optional in threshold_ranges (held fixed if omitted); None thresholds require a range.
summary(result)	Package result into MLflow-ready params, metrics, artifacts dicts.
config_dict()	Serialisable solver configuration.

RatebookOptimiser

Method	Description
solve(df_or_grid, factors, *, factor_columns=None, lambdas=None)	Run ratebook optimisation via coordinate descent. Returns RatebookResult.
evaluate(df_or_grid, factors, factor_tables)	Evaluate factor tables per quote with the canonical kernel. Returns RatebookEvaluation. Tables must cover exactly the factors and levels in factors; rates must be finite and > 0. Ratio constraints raise.
frontier(df_or_grid, factors, *, threshold_ranges, n_points_per_dim=5, factor_columns=None, initial_lambdas=None)	Sweep the efficient frontier via coordinate descent at each threshold. Returns RatebookFrontierResult (points plus each point's factor tables). parallel=True raises.
summary(result)	Package result into MLflow-ready dicts.

ApplyOptimiser

Method	Description
apply(df)	Single-pass scoring with fixed lambdas. Returns ApplyResult. For ratio constraints, min_pct/max_pct resolve L = pct × baseline_LR from the apply-time DataFrame (live-scoring contract), not the solve-time baseline.
with_explainer_columns(df)	Return the input scored candidate DataFrame with optimiser-consistent decision_score, selected, is_baseline, and per-constraint linearised_* / lambda_term_* columns appended. Ratio constraints use the same linearisation as apply(df).
save(path)	Save config + lambdas to JSON. Ratio specs round-trip verbatim.
ApplyOptimiser.load(path)	Load from saved JSON. Rejects unknown keys.

QuoteGridBuilder

Method	Description
QuoteGridBuilder(constraint_columns, *, quote_id, scenario_index, scenario_value, objective, n_steps=None)	Construct a builder. n_steps may be passed upfront to skip auto-detection from the first chunk — useful for streaming sources where the first chunk may be partial.
append(df)	Add a chunk of quotes. Rows must be grouped by quote_id with scenario_index running 0..n_steps in order. Per-row validation rejects layout violations and scenario_value drift across chunks.
build()	Finalise and return a QuoteGrid. Sorts by quote_id in-place via cycle-following permutation (no 2× memory peak). Rejects duplicate quote_ids with both append-order indices in the error.

SolveResult

Property	Type	Description
converged	bool	Whether the solver converged.
iterations	int	Number of iterations taken.
lambdas	dict[str, float]	Final Lagrange multipliers per constraint, in constraint order.
total_objective	float	Portfolio-level objective at optimal solution.
total_constraints	dict[str, float]	Portfolio-level constraint totals.
baseline_objective	float	Objective at the baseline step (the scenario value nearest 1.0).
baseline_constraints	dict[str, float]	Constraints at the baseline step.
constraint_bounds	dict[str, float]	Absolute bound of each constraint (baseline × fraction for min_pct/max_pct; the ratio bound for ratio constraints).
baseline_scenario_value	float	Scenario value of the baseline step.
dataframe	pl.DataFrame	Per-quote results with optimal scenario values.
history	list[dict] | None	Per-iteration convergence records (if record_history=True).
n_quotes	int	Number of quotes in the grid.
n_steps	int	Number of scenario value steps.
scenario_values	list[float]	The scenario value grid.
grid	QuoteGrid	The internal grid (reusable for subsequent solves or apply).

ApplyResult

Property	Type	Description
total_objective	float	Portfolio-level objective.
total_constraints	dict[str, float]	Portfolio-level constraint totals.
baseline_objective	float	Objective at the baseline step (the scenario value nearest 1.0).
baseline_constraints	dict[str, float]	Constraints at the baseline step.
lambdas	dict[str, float]	Applied Lagrange multipliers.
dataframe	pl.DataFrame	Per-quote results with optimal scenario values.

ChunkedApplyResult

Returned by apply_lambdas_to_parquet_chunked. Carries the same aggregate totals as ApplyResult but the per-quote rows live only in the output parquet — only one chunk's optimal_steps (chunk_size / n_steps entries) is alive at any time, then dropped after the row group is written.

Property	Type	Description
total_objective	float	Portfolio-level objective at the optimum (summed across chunks in f64).
total_constraints	dict[str, float]	Portfolio-level constraint totals.
baseline_objective	float	Objective at the baseline step (the scenario value nearest 1.0).
baseline_constraints	dict[str, float]	Constraints at the baseline step.
lambdas	dict[str, float]	Applied Lagrange multipliers.
output_path	str	Path to the streamed-output parquet. Read back via pl.read_parquet or pl.scan_parquet.

FrontierResult

Property	Type	Description
points	pl.DataFrame	One row per frontier point: threshold_*, bound_*, total_objective, total_*, lambda_*, iterations, converged, solver_path, non_convergence_reason, and scenario value statistics (sv_mean, sv_std, sv_min, sv_p5–sv_p95, sv_max, sv_pct_increase, sv_pct_decrease). Exact schema: frontier_points_schema("online", constraint_names).
n_points	int	Number of frontier points.

RatebookResult

Property	Type	Description
factor_tables	dict[str, dict[str, float]]	Factor name to level-rate mapping, in factor-spec order. Composite levels join their parts with FACTOR_SEPARATOR ("\x1f").
lambdas	dict[str, float]	Lagrange multipliers from the last inner solve, in constraint order.
total_objective	float	Objective of the canonical evaluation of factor_tables.
total_constraints	dict[str, float]	Constraint totals of the canonical evaluation.
constraint_bounds	dict[str, float]	Absolute bound of each constraint.
baseline_objective	float	Objective at the baseline step (the scenario value nearest 1.0, f32, lowest on a tie).
baseline_constraints	dict[str, float]	Constraints at the baseline step.
baseline_scenario_value	float	Scenario value of the baseline step.
scenario_values	tuple[float, ...]	The grid's scenario values, ascending.
converged	bool	Coordinate descent converged (factor values stopped moving). Not a feasibility check.
cd_iterations	int	Coordinate descent passes.
clamp_rate	float	Search-space diagnostic: the mean, over every grouped solve, of the fraction of (quote, candidate) targets that fell strictly outside the scenario range. It does not count quotes at a grid edge.
n_quotes	int	Quotes evaluated.
n_quotes_clamped_low / n_quotes_clamped_high	int	Quotes whose factor product lies strictly below / above the scenario range.
quote_results	pl.DataFrame	Per-quote evaluation: quote_id, optimal_step, optimal_scenario_value, optimal_objective, optimal_<c>, factor_product, clamped_low, clamped_high (quote_results_schema(constraint_names)). Not persisted: raises ResultUnavailableError on a loaded result; use evaluate().
per_factor_results	tuple[PerFactorRecord, ...]	One record per inner grouped solve, with explicit cd_iteration, factor, factor_index, totals, λ, clamp_rate, inner_iterations, inner_converged.
save(path)		Save to a directory (format 2: config.json plus one JSON per factor). Factor names that map to the same file name raise.
RatebookResult.load(path)		Load format 1 or 2. Rejects unknown keys and missing factor files; fields a format-1 save lacks raise ResultUnavailableError.
to_rating_entries()	dict[str, pl.DataFrame]	Convert to rating-step DataFrames.

Utility functions

Function	Description
build_grid_from_parquet(path, constraint_columns, *, ...)	Build a QuoteGrid directly from a Parquet file. Loads the projected columns whole; column projection prunes everything outside constraint_columns + the four schema columns. Sum constraints only — ratio constraints require a DataFrame.
build_grid_from_parquet_chunked(path, constraint_columns, chunk_size, *, n_steps=None, ...)	Stream a Parquet file in fixed-size row slices via Polars' with_slice pushdown. Memory peak for the parquet decode buffer is bounded by chunk_size; the final QuoteGrid is still O(total_rows). chunk_size is rounded down to a multiple of n_steps so every slice ends on a quote boundary. Use when the parquet itself doesn't fit in RAM.
apply_lambdas_to_parquet_chunked(parquet_in, parquet_out, lambdas, constraints, chunk_size, *, n_steps=None, ...)	Stream a parquet through apply and write per-quote results to parquet_out one row group per chunk. Returns ChunkedApplyResult with aggregate totals; per-quote rows live in the output parquet. The input/output paths are checked for equality (refuses to overwrite the input), and any error best-effort-deletes the partial output.
apply_from_grid(grid, lambdas, constraints)	Single-pass Lagrangian apply on an existing QuoteGrid. Returns ApplyResult. Sum constraints only; ratio constraints raise ValueError (use ApplyOptimiser.apply(df) on a DataFrame instead — the grid path can't carry numerator/denominator columns for linearisation).
frontier_summary(frontier_result, selected_index)	Package a frontier result into MLflow-ready params, metrics, artifacts dicts.

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License

Price Contour is licensed under the GNU Affero General Public License v3.0.