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Phil

PyPIPython versionsLicense

Representation-guided imputation for missing tabular data — PyPI package philler (import: phil).

Phil runs a grid of imputation strategies, scores each candidate with an Euler Characteristic Transform (ECT) descriptor via the trailed backend, and selects the most representative result.

Impute → Describe → Select → Transform

Installation

pip install philler # core library
pip install "philler[mcp]"# + FastMCP server for agents

Quick start

importpandasaspdfromphilimportPhildf=pd.read_csv("data_with_missing.csv")
phil=Phil(samples=30, random_state=42)
imputed_df=phil.fit(df)
new_df=phil.transform(new_data) # reuse fitted pipeline
MCP server — run sweeps from Claude, Cursor, Gemini CLI, etc.

Install the mcp extra and start the server:

pip install "philler[mcp]"
phil-mcp
# or ephemeral: uv tool run --from "philler[mcp]" phil-mcp

Example Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
"mcpServers": {
"phil": {
"command": "uv tool run",
"args": ["--from", "philler[mcp]", "phil-mcp"]
}
}
}

Key tools: ingest_dataset, characterize_dataset, recommend_grid, list_grids, create_config, validate_config, run_imputation_sweep, diagnose_sweep, export_imputed_data.

Agents can read phil://docs/imputation-matrix for grid comparison metadata. Polars users write to Parquet and ingest the file path.

See docs/source/userGuides/mcp.rst for the full tool table and example dialog. Local end-to-end testing: demos/medical.

Configuration — grids and ECT settings

Imputation grids

Named grids via GridGallery:

NameMethods
defaultBayesianRidge, DecisionTree, RandomForest, GradientBoosting
samplingDistributionImputer (empirical sampling)
financeIterativeImputer, KNNImputer, SimpleImputer
healthcareKNNImputer, SimpleImputer, IterativeImputer
marketingSimpleImputer, KNNImputer, IterativeImputer
engineeringSimpleImputer, KNNImputer, IterativeImputer

Custom grid:

fromphilimportPhil, ImputationConfigfromsklearn.model_selectionimportParameterGridconfig=ImputationConfig(
methods=["KNNImputer"],
modules=["sklearn.impute"],
grids=[ParameterGrid({"n_neighbors": [3, 5, 7]})],
)
phil=Phil(param_grid=config)

ECT descriptor

fromphilimportPhil, ECTConfigphil=Phil(config=ECTConfig(num_thetas=64, radius=1.0, resolution=100, scale=500, normalize=True, seed=42))
scikit-learn pipelines
fromsklearn.pipelineimportPipelinefromsklearn.ensembleimportRandomForestClassifierfromphilimportPhilTransformerpipe=Pipeline([
("imputer", PhilTransformer(samples=20, random_state=0)),
("model", RandomForestClassifier()),
])
pipe.fit(X_train, y_train)
What's new in v1.1.0
  • FastMCP server — phil-mcp exposes the imputation sweep pipeline as MCP tools for agents.
  • Grid recommender — recommend_grid, declarative GridMetadata, and the phil://docs/imputation-matrix resource.
  • Medical demo — demos/medical with covariate sampling, masked iterative imputation, and MDS visualization of descriptor space.

See CHANGELOG.md for full release notes.

Development
uv sync --all-extras
uv run pytest -v
uvx ruff format phil/ tests/
uvx ruff check phil/ tests/

Contributors: see AGENTS.md for package layout and design notes.

Documentation

Sphinx docs live under docs/source. Build locally:

uv run sphinx-build -M html docs/source docs/build

Made by Krv Labs

About

Fill your data. A Multiverse Imputation Method powered by Topology 🍩

docs.krv.ai/phil/

Topics

data-imputationrepresentation-learningtopology

Resources

Readme
BSD-3-Clause license
Activity
Custom properties

Stars

3 stars

Watchers

0 watching

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0 forks
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})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Phil
PyPIPython versionsLicense
Representation-guided imputation for missing tabular data — PyPI package philler (import: phil).
Phil runs a grid of imputation strategies, scores each candidate with an Euler Characteristic Transform (ECT) descriptor via the trailed backend, and selects the most representative result.
Impute → Describe → Select → Transform
Installation
pip install philler # core library
pip install "philler[mcp]"# + FastMCP server for agents
Quick start
importpandasaspdfromphilimportPhildf=pd.read_csv("data_with_missing.csv")
phil=Phil(samples=30, random_state=42)
imputed_df=phil.fit(df)
new_df=phil.transform(new_data) # reuse fitted pipeline
MCP server — run sweeps from Claude, Cursor, Gemini CLI, etc.
Install the mcp extra and start the server:
pip install "philler[mcp]"
phil-mcp
# or ephemeral: uv tool run --from "philler[mcp]" phil-mcp
Example Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"phil": {
"command": "uv tool run",
"args": ["--from", "philler[mcp]", "phil-mcp"]
}
}
}
Key tools: ingest_dataset, characterize_dataset, recommend_grid, list_grids, create_config, validate_config, run_imputation_sweep, diagnose_sweep, export_imputed_data.
Agents can read phil://docs/imputation-matrix for grid comparison metadata. Polars users write to Parquet and ingest the file path.
See docs/source/userGuides/mcp.rst for the full tool table and example dialog. Local end-to-end testing: demos/medical.
Configuration — grids and ECT settings
Imputation grids
Named grids via GridGallery:
NameMethods
defaultBayesianRidge, DecisionTree, RandomForest, GradientBoosting
samplingDistributionImputer (empirical sampling)
financeIterativeImputer, KNNImputer, SimpleImputer
healthcareKNNImputer, SimpleImputer, IterativeImputer
marketingSimpleImputer, KNNImputer, IterativeImputer
engineeringSimpleImputer, KNNImputer, IterativeImputer
Custom grid:
fromphilimportPhil, ImputationConfigfromsklearn.model_selectionimportParameterGridconfig=ImputationConfig(
methods=["KNNImputer"],
modules=["sklearn.impute"],
grids=[ParameterGrid({"n_neighbors": [3, 5, 7]})],
)
phil=Phil(param_grid=config)
ECT descriptor
fromphilimportPhil, ECTConfigphil=Phil(config=ECTConfig(num_thetas=64, radius=1.0, resolution=100, scale=500, normalize=True, seed=42))
scikit-learn pipelines
fromsklearn.pipelineimportPipelinefromsklearn.ensembleimportRandomForestClassifierfromphilimportPhilTransformerpipe=Pipeline([
("imputer", PhilTransformer(samples=20, random_state=0)),
("model", RandomForestClassifier()),
])
pipe.fit(X_train, y_train)
What's new in v1.1.0
  • FastMCP server — phil-mcp exposes the imputation sweep pipeline as MCP tools for agents.
  • Grid recommender — recommend_grid, declarative GridMetadata, and the phil://docs/imputation-matrix resource.
  • Medical demo — demos/medical with covariate sampling, masked iterative imputation, and MDS visualization of descriptor space.
See CHANGELOG.md for full release notes.
Development
uv sync --all-extras
uv run pytest -v
uvx ruff format phil/ tests/
uvx ruff check phil/ tests/
Contributors: see AGENTS.md for package layout and design notes.
Documentation
Sphinx docs live under docs/source. Build locally:
uv run sphinx-build -M html docs/source docs/build
Made by Krv Labs
About
Fill your data. A Multiverse Imputation Method powered by Topology 🍩
docs.krv.ai/phil/
Topics
data-imputationrepresentation-learningtopology
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
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Phil
PyPIPython versionsLicense
Representation-guided imputation for missing tabular data — PyPI package philler (import: phil).
Phil runs a grid of imputation strategies, scores each candidate with an Euler Characteristic Transform (ECT) descriptor via the trailed backend, and selects the most representative result.
Impute → Describe → Select → Transform
Installation
pip install philler # core library
pip install "philler[mcp]"# + FastMCP server for agents
Quick start
importpandasaspdfromphilimportPhildf=pd.read_csv("data_with_missing.csv")
phil=Phil(samples=30, random_state=42)
imputed_df=phil.fit(df)
new_df=phil.transform(new_data) # reuse fitted pipeline
MCP server — run sweeps from Claude, Cursor, Gemini CLI, etc.
Install the mcp extra and start the server:
pip install "philler[mcp]"
phil-mcp
# or ephemeral: uv tool run --from "philler[mcp]" phil-mcp
Example Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"phil": {
"command": "uv tool run",
"args": ["--from", "philler[mcp]", "phil-mcp"]
}
}
}
Key tools: ingest_dataset, characterize_dataset, recommend_grid, list_grids, create_config, validate_config, run_imputation_sweep, diagnose_sweep, export_imputed_data.
Agents can read phil://docs/imputation-matrix for grid comparison metadata. Polars users write to Parquet and ingest the file path.
See docs/source/userGuides/mcp.rst for the full tool table and example dialog. Local end-to-end testing: demos/medical.
Configuration — grids and ECT settings
Imputation grids
Named grids via GridGallery:
NameMethods
defaultBayesianRidge, DecisionTree, RandomForest, GradientBoosting
samplingDistributionImputer (empirical sampling)
financeIterativeImputer, KNNImputer, SimpleImputer
healthcareKNNImputer, SimpleImputer, IterativeImputer
marketingSimpleImputer, KNNImputer, IterativeImputer
engineeringSimpleImputer, KNNImputer, IterativeImputer
Custom grid:
fromphilimportPhil, ImputationConfigfromsklearn.model_selectionimportParameterGridconfig=ImputationConfig(
methods=["KNNImputer"],
modules=["sklearn.impute"],
grids=[ParameterGrid({"n_neighbors": [3, 5, 7]})],
)
phil=Phil(param_grid=config)
ECT descriptor
fromphilimportPhil, ECTConfigphil=Phil(config=ECTConfig(num_thetas=64, radius=1.0, resolution=100, scale=500, normalize=True, seed=42))
scikit-learn pipelines
fromsklearn.pipelineimportPipelinefromsklearn.ensembleimportRandomForestClassifierfromphilimportPhilTransformerpipe=Pipeline([
("imputer", PhilTransformer(samples=20, random_state=0)),
("model", RandomForestClassifier()),
])
pipe.fit(X_train, y_train)
What's new in v1.1.0
  • FastMCP server — phil-mcp exposes the imputation sweep pipeline as MCP tools for agents.
  • Grid recommender — recommend_grid, declarative GridMetadata, and the phil://docs/imputation-matrix resource.
  • Medical demo — demos/medical with covariate sampling, masked iterative imputation, and MDS visualization of descriptor space.
See CHANGELOG.md for full release notes.
Development
uv sync --all-extras
uv run pytest -v
uvx ruff format phil/ tests/
uvx ruff check phil/ tests/
Contributors: see AGENTS.md for package layout and design notes.
Documentation
Sphinx docs live under docs/source. Build locally:
uv run sphinx-build -M html docs/source docs/build
Made by Krv Labs
About
Fill your data. A Multiverse Imputation Method powered by Topology 🍩
docs.krv.ai/phil/
Topics
data-imputationrepresentation-learningtopology
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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// Highlight search terms from Google/DuckDuckGo/Bing referrer
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var ref = document.referrer;
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observer.observe(document.body, { childList: true, subtree: true });
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}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
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Phil
PyPIPython versionsLicense
Representation-guided imputation for missing tabular data — PyPI package philler (import: phil).
Phil runs a grid of imputation strategies, scores each candidate with an Euler Characteristic Transform (ECT) descriptor via the trailed backend, and selects the most representative result.
Impute → Describe → Select → Transform
Installation
pip install philler # core library
pip install "philler[mcp]"# + FastMCP server for agents
Quick start
importpandasaspdfromphilimportPhildf=pd.read_csv("data_with_missing.csv")
phil=Phil(samples=30, random_state=42)
imputed_df=phil.fit(df)
new_df=phil.transform(new_data) # reuse fitted pipeline
MCP server — run sweeps from Claude, Cursor, Gemini CLI, etc.
Install the mcp extra and start the server:
pip install "philler[mcp]"
phil-mcp
# or ephemeral: uv tool run --from "philler[mcp]" phil-mcp
Example Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"phil": {
"command": "uv tool run",
"args": ["--from", "philler[mcp]", "phil-mcp"]
}
}
}
Key tools: ingest_dataset, characterize_dataset, recommend_grid, list_grids, create_config, validate_config, run_imputation_sweep, diagnose_sweep, export_imputed_data.
Agents can read phil://docs/imputation-matrix for grid comparison metadata. Polars users write to Parquet and ingest the file path.
See docs/source/userGuides/mcp.rst for the full tool table and example dialog. Local end-to-end testing: demos/medical.
Configuration — grids and ECT settings
Imputation grids
Named grids via GridGallery:
NameMethods
defaultBayesianRidge, DecisionTree, RandomForest, GradientBoosting
samplingDistributionImputer (empirical sampling)
financeIterativeImputer, KNNImputer, SimpleImputer
healthcareKNNImputer, SimpleImputer, IterativeImputer
marketingSimpleImputer, KNNImputer, IterativeImputer
engineeringSimpleImputer, KNNImputer, IterativeImputer
Custom grid:
fromphilimportPhil, ImputationConfigfromsklearn.model_selectionimportParameterGridconfig=ImputationConfig(
methods=["KNNImputer"],
modules=["sklearn.impute"],
grids=[ParameterGrid({"n_neighbors": [3, 5, 7]})],
)
phil=Phil(param_grid=config)
ECT descriptor
fromphilimportPhil, ECTConfigphil=Phil(config=ECTConfig(num_thetas=64, radius=1.0, resolution=100, scale=500, normalize=True, seed=42))
scikit-learn pipelines
fromsklearn.pipelineimportPipelinefromsklearn.ensembleimportRandomForestClassifierfromphilimportPhilTransformerpipe=Pipeline([
("imputer", PhilTransformer(samples=20, random_state=0)),
("model", RandomForestClassifier()),
])
pipe.fit(X_train, y_train)
What's new in v1.1.0
  • FastMCP server — phil-mcp exposes the imputation sweep pipeline as MCP tools for agents.
  • Grid recommender — recommend_grid, declarative GridMetadata, and the phil://docs/imputation-matrix resource.
  • Medical demo — demos/medical with covariate sampling, masked iterative imputation, and MDS visualization of descriptor space.
See CHANGELOG.md for full release notes.
Development
uv sync --all-extras
uv run pytest -v
uvx ruff format phil/ tests/
uvx ruff check phil/ tests/
Contributors: see AGENTS.md for package layout and design notes.
Documentation
Sphinx docs live under docs/source. Build locally:
uv run sphinx-build -M html docs/source docs/build
Made by Krv Labs
About
Fill your data. A Multiverse Imputation Method powered by Topology 🍩
docs.krv.ai/phil/
Topics
data-imputationrepresentation-learningtopology
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
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0 forks
Report repository
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var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
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Phil
PyPIPython versionsLicense
Representation-guided imputation for missing tabular data — PyPI package philler (import: phil).
Phil runs a grid of imputation strategies, scores each candidate with an Euler Characteristic Transform (ECT) descriptor via the trailed backend, and selects the most representative result.
Impute → Describe → Select → Transform
Installation
pip install philler # core library
pip install "philler[mcp]"# + FastMCP server for agents
Quick start
importpandasaspdfromphilimportPhildf=pd.read_csv("data_with_missing.csv")
phil=Phil(samples=30, random_state=42)
imputed_df=phil.fit(df)
new_df=phil.transform(new_data) # reuse fitted pipeline
MCP server — run sweeps from Claude, Cursor, Gemini CLI, etc.
Install the mcp extra and start the server:
pip install "philler[mcp]"
phil-mcp
# or ephemeral: uv tool run --from "philler[mcp]" phil-mcp
Example Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"phil": {
"command": "uv tool run",
"args": ["--from", "philler[mcp]", "phil-mcp"]
}
}
}
Key tools: ingest_dataset, characterize_dataset, recommend_grid, list_grids, create_config, validate_config, run_imputation_sweep, diagnose_sweep, export_imputed_data.
Agents can read phil://docs/imputation-matrix for grid comparison metadata. Polars users write to Parquet and ingest the file path.
See docs/source/userGuides/mcp.rst for the full tool table and example dialog. Local end-to-end testing: demos/medical.
Configuration — grids and ECT settings
Imputation grids
Named grids via GridGallery:
NameMethods
defaultBayesianRidge, DecisionTree, RandomForest, GradientBoosting
samplingDistributionImputer (empirical sampling)
financeIterativeImputer, KNNImputer, SimpleImputer
healthcareKNNImputer, SimpleImputer, IterativeImputer
marketingSimpleImputer, KNNImputer, IterativeImputer
engineeringSimpleImputer, KNNImputer, IterativeImputer
Custom grid:
fromphilimportPhil, ImputationConfigfromsklearn.model_selectionimportParameterGridconfig=ImputationConfig(
methods=["KNNImputer"],
modules=["sklearn.impute"],
grids=[ParameterGrid({"n_neighbors": [3, 5, 7]})],
)
phil=Phil(param_grid=config)
ECT descriptor
fromphilimportPhil, ECTConfigphil=Phil(config=ECTConfig(num_thetas=64, radius=1.0, resolution=100, scale=500, normalize=True, seed=42))
scikit-learn pipelines
fromsklearn.pipelineimportPipelinefromsklearn.ensembleimportRandomForestClassifierfromphilimportPhilTransformerpipe=Pipeline([
("imputer", PhilTransformer(samples=20, random_state=0)),
("model", RandomForestClassifier()),
])
pipe.fit(X_train, y_train)
What's new in v1.1.0
  • FastMCP server — phil-mcp exposes the imputation sweep pipeline as MCP tools for agents.
  • Grid recommender — recommend_grid, declarative GridMetadata, and the phil://docs/imputation-matrix resource.
  • Medical demo — demos/medical with covariate sampling, masked iterative imputation, and MDS visualization of descriptor space.
See CHANGELOG.md for full release notes.
Development
uv sync --all-extras
uv run pytest -v
uvx ruff format phil/ tests/
uvx ruff check phil/ tests/
Contributors: see AGENTS.md for package layout and design notes.
Documentation
Sphinx docs live under docs/source. Build locally:
uv run sphinx-build -M html docs/source docs/build
Made by Krv Labs
About
Fill your data. A Multiverse Imputation Method powered by Topology 🍩
docs.krv.ai/phil/
Topics
data-imputationrepresentation-learningtopology
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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Phil
PyPIPython versionsLicense
Representation-guided imputation for missing tabular data — PyPI package philler (import: phil).
Phil runs a grid of imputation strategies, scores each candidate with an Euler Characteristic Transform (ECT) descriptor via the trailed backend, and selects the most representative result.
Impute → Describe → Select → Transform
Installation
pip install philler # core library
pip install "philler[mcp]"# + FastMCP server for agents
Quick start
importpandasaspdfromphilimportPhildf=pd.read_csv("data_with_missing.csv")
phil=Phil(samples=30, random_state=42)
imputed_df=phil.fit(df)
new_df=phil.transform(new_data) # reuse fitted pipeline
MCP server — run sweeps from Claude, Cursor, Gemini CLI, etc.
Install the mcp extra and start the server:
pip install "philler[mcp]"
phil-mcp
# or ephemeral: uv tool run --from "philler[mcp]" phil-mcp
Example Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"phil": {
"command": "uv tool run",
"args": ["--from", "philler[mcp]", "phil-mcp"]
}
}
}
Key tools: ingest_dataset, characterize_dataset, recommend_grid, list_grids, create_config, validate_config, run_imputation_sweep, diagnose_sweep, export_imputed_data.
Agents can read phil://docs/imputation-matrix for grid comparison metadata. Polars users write to Parquet and ingest the file path.
See docs/source/userGuides/mcp.rst for the full tool table and example dialog. Local end-to-end testing: demos/medical.
Configuration — grids and ECT settings
Imputation grids
Named grids via GridGallery:
NameMethods
defaultBayesianRidge, DecisionTree, RandomForest, GradientBoosting
samplingDistributionImputer (empirical sampling)
financeIterativeImputer, KNNImputer, SimpleImputer
healthcareKNNImputer, SimpleImputer, IterativeImputer
marketingSimpleImputer, KNNImputer, IterativeImputer
engineeringSimpleImputer, KNNImputer, IterativeImputer
Custom grid:
fromphilimportPhil, ImputationConfigfromsklearn.model_selectionimportParameterGridconfig=ImputationConfig(
methods=["KNNImputer"],
modules=["sklearn.impute"],
grids=[ParameterGrid({"n_neighbors": [3, 5, 7]})],
)
phil=Phil(param_grid=config)
ECT descriptor
fromphilimportPhil, ECTConfigphil=Phil(config=ECTConfig(num_thetas=64, radius=1.0, resolution=100, scale=500, normalize=True, seed=42))
scikit-learn pipelines
fromsklearn.pipelineimportPipelinefromsklearn.ensembleimportRandomForestClassifierfromphilimportPhilTransformerpipe=Pipeline([
("imputer", PhilTransformer(samples=20, random_state=0)),
("model", RandomForestClassifier()),
])
pipe.fit(X_train, y_train)
What's new in v1.1.0
  • FastMCP server — phil-mcp exposes the imputation sweep pipeline as MCP tools for agents.
  • Grid recommender — recommend_grid, declarative GridMetadata, and the phil://docs/imputation-matrix resource.
  • Medical demo — demos/medical with covariate sampling, masked iterative imputation, and MDS visualization of descriptor space.
See CHANGELOG.md for full release notes.
Development
uv sync --all-extras
uv run pytest -v
uvx ruff format phil/ tests/
uvx ruff check phil/ tests/
Contributors: see AGENTS.md for package layout and design notes.
Documentation
Sphinx docs live under docs/source. Build locally:
uv run sphinx-build -M html docs/source docs/build
Made by Krv Labs
About
Fill your data. A Multiverse Imputation Method powered by Topology 🍩
docs.krv.ai/phil/
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data-imputationrepresentation-learningtopology
Resources
Readme
BSD-3-Clause license
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Phil
PyPIPython versionsLicense
Representation-guided imputation for missing tabular data — PyPI package philler (import: phil).
Phil runs a grid of imputation strategies, scores each candidate with an Euler Characteristic Transform (ECT) descriptor via the trailed backend, and selects the most representative result.
Impute → Describe → Select → Transform
Installation
pip install philler # core library
pip install "philler[mcp]"# + FastMCP server for agents
Quick start
importpandasaspdfromphilimportPhildf=pd.read_csv("data_with_missing.csv")
phil=Phil(samples=30, random_state=42)
imputed_df=phil.fit(df)
new_df=phil.transform(new_data) # reuse fitted pipeline
MCP server — run sweeps from Claude, Cursor, Gemini CLI, etc.
Install the mcp extra and start the server:
pip install "philler[mcp]"
phil-mcp
# or ephemeral: uv tool run --from "philler[mcp]" phil-mcp
Example Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"phil": {
"command": "uv tool run",
"args": ["--from", "philler[mcp]", "phil-mcp"]
}
}
}
Key tools: ingest_dataset, characterize_dataset, recommend_grid, list_grids, create_config, validate_config, run_imputation_sweep, diagnose_sweep, export_imputed_data.
Agents can read phil://docs/imputation-matrix for grid comparison metadata. Polars users write to Parquet and ingest the file path.
See docs/source/userGuides/mcp.rst for the full tool table and example dialog. Local end-to-end testing: demos/medical.
Configuration — grids and ECT settings
Imputation grids
Named grids via GridGallery:
NameMethods
defaultBayesianRidge, DecisionTree, RandomForest, GradientBoosting
samplingDistributionImputer (empirical sampling)
financeIterativeImputer, KNNImputer, SimpleImputer
healthcareKNNImputer, SimpleImputer, IterativeImputer
marketingSimpleImputer, KNNImputer, IterativeImputer
engineeringSimpleImputer, KNNImputer, IterativeImputer
Custom grid:
fromphilimportPhil, ImputationConfigfromsklearn.model_selectionimportParameterGridconfig=ImputationConfig(
methods=["KNNImputer"],
modules=["sklearn.impute"],
grids=[ParameterGrid({"n_neighbors": [3, 5, 7]})],
)
phil=Phil(param_grid=config)
ECT descriptor
fromphilimportPhil, ECTConfigphil=Phil(config=ECTConfig(num_thetas=64, radius=1.0, resolution=100, scale=500, normalize=True, seed=42))
scikit-learn pipelines
fromsklearn.pipelineimportPipelinefromsklearn.ensembleimportRandomForestClassifierfromphilimportPhilTransformerpipe=Pipeline([
("imputer", PhilTransformer(samples=20, random_state=0)),
("model", RandomForestClassifier()),
])
pipe.fit(X_train, y_train)
What's new in v1.1.0
  • FastMCP server — phil-mcp exposes the imputation sweep pipeline as MCP tools for agents.
  • Grid recommender — recommend_grid, declarative GridMetadata, and the phil://docs/imputation-matrix resource.
  • Medical demo — demos/medical with covariate sampling, masked iterative imputation, and MDS visualization of descriptor space.
See CHANGELOG.md for full release notes.
Development
uv sync --all-extras
uv run pytest -v
uvx ruff format phil/ tests/
uvx ruff check phil/ tests/
Contributors: see AGENTS.md for package layout and design notes.
Documentation
Sphinx docs live under docs/source. Build locally:
uv run sphinx-build -M html docs/source docs/build
Made by Krv Labs
About
Fill your data. A Multiverse Imputation Method powered by Topology 🍩
docs.krv.ai/phil/
Topics
data-imputationrepresentation-learningtopology
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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Used by
Contributors
Languages

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function applyDarkMode() {
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style = document.createElement('style');
style.id = 'universal-dark-mode-style';
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}
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style.textContent = '
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filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
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filter: invert(1) hue-rotate(180deg) !important;
}
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.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
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.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
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filter: none !important;
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filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
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::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
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e.preventDefault();
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if (enabled) {
applyDarkMode();
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removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
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var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
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observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
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Phil
PyPIPython versionsLicense
Representation-guided imputation for missing tabular data — PyPI package philler (import: phil).
Phil runs a grid of imputation strategies, scores each candidate with an Euler Characteristic Transform (ECT) descriptor via the trailed backend, and selects the most representative result.
Impute → Describe → Select → Transform
Installation
pip install philler # core library
pip install "philler[mcp]"# + FastMCP server for agents
Quick start
importpandasaspdfromphilimportPhildf=pd.read_csv("data_with_missing.csv")
phil=Phil(samples=30, random_state=42)
imputed_df=phil.fit(df)
new_df=phil.transform(new_data) # reuse fitted pipeline
MCP server — run sweeps from Claude, Cursor, Gemini CLI, etc.
Install the mcp extra and start the server:
pip install "philler[mcp]"
phil-mcp
# or ephemeral: uv tool run --from "philler[mcp]" phil-mcp
Example Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"phil": {
"command": "uv tool run",
"args": ["--from", "philler[mcp]", "phil-mcp"]
}
}
}
Key tools: ingest_dataset, characterize_dataset, recommend_grid, list_grids, create_config, validate_config, run_imputation_sweep, diagnose_sweep, export_imputed_data.
Agents can read phil://docs/imputation-matrix for grid comparison metadata. Polars users write to Parquet and ingest the file path.
See docs/source/userGuides/mcp.rst for the full tool table and example dialog. Local end-to-end testing: demos/medical.
Configuration — grids and ECT settings
Imputation grids
Named grids via GridGallery:
NameMethods
defaultBayesianRidge, DecisionTree, RandomForest, GradientBoosting
samplingDistributionImputer (empirical sampling)
financeIterativeImputer, KNNImputer, SimpleImputer
healthcareKNNImputer, SimpleImputer, IterativeImputer
marketingSimpleImputer, KNNImputer, IterativeImputer
engineeringSimpleImputer, KNNImputer, IterativeImputer
Custom grid:
fromphilimportPhil, ImputationConfigfromsklearn.model_selectionimportParameterGridconfig=ImputationConfig(
methods=["KNNImputer"],
modules=["sklearn.impute"],
grids=[ParameterGrid({"n_neighbors": [3, 5, 7]})],
)
phil=Phil(param_grid=config)
ECT descriptor
fromphilimportPhil, ECTConfigphil=Phil(config=ECTConfig(num_thetas=64, radius=1.0, resolution=100, scale=500, normalize=True, seed=42))
scikit-learn pipelines
fromsklearn.pipelineimportPipelinefromsklearn.ensembleimportRandomForestClassifierfromphilimportPhilTransformerpipe=Pipeline([
("imputer", PhilTransformer(samples=20, random_state=0)),
("model", RandomForestClassifier()),
])
pipe.fit(X_train, y_train)
What's new in v1.1.0
  • FastMCP server — phil-mcp exposes the imputation sweep pipeline as MCP tools for agents.
  • Grid recommender — recommend_grid, declarative GridMetadata, and the phil://docs/imputation-matrix resource.
  • Medical demo — demos/medical with covariate sampling, masked iterative imputation, and MDS visualization of descriptor space.
See CHANGELOG.md for full release notes.
Development
uv sync --all-extras
uv run pytest -v
uvx ruff format phil/ tests/
uvx ruff check phil/ tests/
Contributors: see AGENTS.md for package layout and design notes.
Documentation
Sphinx docs live under docs/source. Build locally:
uv run sphinx-build -M html docs/source docs/build
Made by Krv Labs
About
Fill your data. A Multiverse Imputation Method powered by Topology 🍩
docs.krv.ai/phil/
Topics
data-imputationrepresentation-learningtopology
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages