New utilization model and interface #3
Conversation
WalkthroughAdds MEPS modeling notebooks, a FastAPI utilization service that loads pickled pipelines, Next.js proxy routes and a client test page, MLflow registry helper, dev tooling and pinned Python deps, plus supporting docs and minor TypeScript/config/gitignore edits for end-to-end health-check and prediction flows. Changes
Sequence Diagram(s)sequenceDiagram
participant Browser
participant NextApp as Next.js API
participant PySvc as Python FastAPI
Browser->>NextApp: GET /api/utilization-model/health
NextApp->>PySvc: GET /health (AbortController, timeout)
PySvc-->>NextApp: 200 {"status":"ok"} / 5xx error
NextApp-->>Browser: 200 {status:"ok", upstream:...} / 502/504/500
Browser->>NextApp: POST /api/utilization-model/predict {features}
NextApp->>PySvc: POST /predict {features} (AbortController, timeout)
PySvc-->>NextApp: 200 {pcp_visits:..., ...} / 5xx error / malformed
NextApp-->>Browser: 200 {pcp_visits:..., ...} / 502/504/500 (with truncated excerpt on upstream errors)
Estimated code review effort🎯 4 (Complex) | ⏱️ ~40 minutes Poem
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Actionable comments posted: 10
🧹 Nitpick comments (1)
python-models/models/MEPS-model-v1.ipynb (1)
1-946: Consider adding comprehensive documentation and validationThis notebook would benefit from:
- A README file explaining the MEPS data requirements and setup
- Data validation checks to ensure data quality
- Model performance visualization
- Cross-validation for more robust evaluation
Would you like me to help create:
- A comprehensive README.md with setup instructions?
- Data validation functions to check for missing values and outliers?
- Visualization code for model performance and feature importance?
- Cross-validation implementation for more robust model evaluation?
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⛔ Files ignored due to path filters (10)
python-models/data/MEPS/2022-consolidated/h243.csvis excluded by!**/*.csvpython-models/data/MEPS/2022-consolidated/h243.parquetis excluded by!**/*.parquetpython-models/data/MEPS/2023-counts/h248a.xlsxis excluded by!**/*.xlsxpython-models/data/MEPS/2023-counts/h248b.xlsxis excluded by!**/*.xlsxpython-models/data/MEPS/2023-counts/h248c.xlsxis excluded by!**/*.xlsxpython-models/data/MEPS/2023-counts/h248d.xlsxis excluded by!**/*.xlsxpython-models/data/MEPS/2023-counts/h248e.xlsxis excluded by!**/*.xlsxpython-models/data/MEPS/2023-counts/h248f.xlsxis excluded by!**/*.xlsxpython-models/data/MEPS/2023-counts/h248g.xlsxis excluded by!**/*.xlsxpython-models/data/MEPS/2023-counts/h248h.xlsxis excluded by!**/*.xlsx
📒 Files selected for processing (2)
python-models/models/MEPS-model-v1.ipynb(1 hunks)python-models/requirements.txt(1 hunks)
🧰 Additional context used
🪛 Ruff (0.12.2)
python-models/models/MEPS-model-v1.ipynb
72-72: Found useless expression. Either assign it to a variable or remove it.
(B018)
🔇 Additional comments (1)
python-models/models/MEPS-model-v1.ipynb (1)
32-47: LGTM! Well-organized importsThe imports are properly organized and include all necessary modules for data processing and machine learning tasks.
| "df = pl.read_excel(\n", | ||
| " \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.xlsx\",\n", | ||
| " schema_overrides=schema_overrides\n", | ||
| ")\n", | ||
| "\n", | ||
| "df.head()" |
There was a problem hiding this comment.
Fix useless expression and use environment-agnostic paths
Two issues in this code block:
- Line 75 has a useless expression that should be removed
- Hardcoded absolute paths make the code non-portable
-df = pl.read_excel(
- "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.xlsx",
- schema_overrides=schema_overrides
-)
-
-df.head()
+import os
+from pathlib import Path
+
+# Use relative paths from the notebook location
+data_dir = Path("../data")
+df = pl.read_excel(
+ data_dir / "h243.xlsx",
+ schema_overrides=schema_overrides
+)
+
+# Display the head of the dataframe
+display(df.head())📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "df = pl.read_excel(\n", | |
| " \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.xlsx\",\n", | |
| " schema_overrides=schema_overrides\n", | |
| ")\n", | |
| "\n", | |
| "df.head()" | |
| import os | |
| from pathlib import Path | |
| # Use relative paths from the notebook location | |
| data_dir = Path("../data") | |
| df = pl.read_excel( | |
| data_dir / "h243.xlsx", | |
| schema_overrides=schema_overrides | |
| ) | |
| # Display the head of the dataframe | |
| display(df.head()) |
🧰 Tools
🪛 Ruff (0.12.2)
72-72: Found useless expression. Either assign it to a variable or remove it.
(B018)
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v1.ipynb around lines 70 to 75, remove the
useless expression "df.head()" since it does not affect the program state or
output. Replace the hardcoded absolute file path with a relative or
environment-agnostic path, such as using a path variable or a configuration
setting, to make the code portable across different environments.
| "df.write_csv(\"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.csv\") # Much faster for future loads\n", | ||
| "df.write_parquet(\"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.parquet\") # Even faster, compressed" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Replace hardcoded paths with configurable paths
Using absolute paths limits code portability and reusability.
-df.write_csv("/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.csv") # Much faster for future loads
-df.write_parquet("/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.parquet") # Even faster, compressed
+# Use the same data_dir variable from above
+df.write_csv(data_dir / "h243.csv") # Much faster for future loads
+df.write_parquet(data_dir / "h243.parquet") # Even faster, compressed📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "df.write_csv(\"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.csv\") # Much faster for future loads\n", | |
| "df.write_parquet(\"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.parquet\") # Even faster, compressed" | |
| df.write_csv(data_dir / "h243.csv") # Much faster for future loads | |
| df.write_parquet(data_dir / "h243.parquet") # Even faster, compressed |
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v1.ipynb around lines 93 to 94, replace the
hardcoded absolute file paths in the df.write_csv and df.write_parquet calls
with configurable path variables or parameters. Define a variable or function
argument to hold the base directory or file path, and use it to construct the
full file paths dynamically, improving portability and reusability of the code.
| } | ||
| ], | ||
| "source": [ | ||
| "df_cons = pd.read_parquet(\"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2022-consolidated/h243.parquet\")\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Use relative path for data loading
Hardcoded absolute path should be replaced with a relative path.
-df_cons = pd.read_parquet("/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2022-consolidated/h243.parquet")
+# Use relative path from notebook location
+data_dir = Path("../data")
+df_cons = pd.read_parquet(data_dir / "MEPS/2022-consolidated/h243.parquet")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "df_cons = pd.read_parquet(\"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2022-consolidated/h243.parquet\")\n", | |
| # Use relative path from notebook location | |
| data_dir = Path("../data") | |
| df_cons = pd.read_parquet(data_dir / "MEPS/2022-consolidated/h243.parquet") |
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v1.ipynb at line 338, replace the hardcoded
absolute path used in pd.read_parquet with a relative path. Modify the file path
string to be relative to the project directory or notebook location to improve
portability and avoid environment-specific dependencies.
| "event_files = {\n", | ||
| " \"pcp_visits\": \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248g.xlsx\", # Office-based visits\n", | ||
| " \"outpatient_visits\": \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248f.xlsx\", # Outpatient visits\n", | ||
| " \"er_visits\": \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248e.xlsx\", # Emergency Room visits\n", | ||
| " \"inpatient_admits\": \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248d.xlsx\", # Inpatient stays\n", | ||
| " \"home_health_visits\": \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248h.xlsx\", # Home health visits\n", | ||
| " \"rx_fills\": \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248a.xlsx\", # Prescription fills\n", | ||
| " \"dental_visits\": \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248b.xlsx\", # Dental visits\n", | ||
| " \"equipment_purchases\": \"/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248c.xlsx\" # Medical equipment/supplies\n", | ||
| "}\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Make file paths configurable
All event file paths are hardcoded. Consider using a configuration approach for better maintainability.
+# Configure base data directory
+data_dir = Path("../data/MEPS/2023-counts")
+
event_files = {
- "pcp_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248g.xlsx", # Office-based visits
- "outpatient_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248f.xlsx", # Outpatient visits
- "er_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248e.xlsx", # Emergency Room visits
- "inpatient_admits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248d.xlsx", # Inpatient stays
- "home_health_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248h.xlsx", # Home health visits
- "rx_fills": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248a.xlsx", # Prescription fills
- "dental_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248b.xlsx", # Dental visits
- "equipment_purchases": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248c.xlsx" # Medical equipment/supplies
+ "pcp_visits": data_dir / "h248g.xlsx", # Office-based visits
+ "outpatient_visits": data_dir / "h248f.xlsx", # Outpatient visits
+ "er_visits": data_dir / "h248e.xlsx", # Emergency Room visits
+ "inpatient_admits": data_dir / "h248d.xlsx", # Inpatient stays
+ "home_health_visits": data_dir / "h248h.xlsx", # Home health visits
+ "rx_fills": data_dir / "h248a.xlsx", # Prescription fills
+ "dental_visits": data_dir / "h248b.xlsx", # Dental visits
+ "equipment_purchases": data_dir / "h248c.xlsx" # Medical equipment/supplies
}Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v1.ipynb around lines 366 to 375, the event
file paths are hardcoded as absolute paths, which reduces maintainability and
flexibility. Refactor the code to load these file paths from a configuration
file or environment variables instead of hardcoding them. This can be done by
defining a configuration dictionary or using a config parser to read paths,
allowing easier updates and environment-specific adjustments.
| "feature_cols = [\n", | ||
| " \"AGE22X\", \"ADSEX42\", \"INSCOV22\", \"ADBMI42\", \n", | ||
| " \"ADOSTP42\", \"ADASKALC42\", \"ADDAYEXER42\",\n", | ||
| " #\"TOTCHRON\", can't find this feature right now\n", | ||
| " \"pcp_visits\", \n", | ||
| " #\"prior_year_specialist_visits\", can't find this feature right now\n", | ||
| " \"er_visits\", \n", | ||
| " \"inpatient_admits\",\n", | ||
| "]\n", | ||
| "count_targets = [\n", | ||
| " \"pcp_visits\", \"outpatient_visits\", \"er_visits\",\n", | ||
| " \"inpatient_admits\", \"home_health_visits\",\n", | ||
| " \"rx_fills\", \"dental_visits\", \"equipment_purchases\"\n", | ||
| "]\n", | ||
| "\n", | ||
| "X = df[feature_cols]\n", | ||
| "y = df[count_targets].clip(lower=0)" | ||
| ] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Handle missing features gracefully
The code has commented out features that can't be found. Consider adding error handling or documenting why these features are missing.
feature_cols = [
"AGE22X", "ADSEX42", "INSCOV22", "ADBMI42",
"ADOSTP42", "ADASKALC42", "ADDAYEXER42",
- #"TOTCHRON", can't find this feature right now
"pcp_visits",
- #"prior_year_specialist_visits", can't find this feature right now
"er_visits",
"inpatient_admits",
]
+
+# TODO: Add these features when available in the dataset:
+# - TOTCHRON: Number of chronic conditions
+# - prior_year_specialist_visits: Historical specialist visit data
+
+# Verify all features exist in the dataframe
+missing_features = [col for col in feature_cols if col not in df.columns]
+if missing_features:
+ print(f"Warning: Missing features: {missing_features}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "feature_cols = [\n", | |
| " \"AGE22X\", \"ADSEX42\", \"INSCOV22\", \"ADBMI42\", \n", | |
| " \"ADOSTP42\", \"ADASKALC42\", \"ADDAYEXER42\",\n", | |
| " #\"TOTCHRON\", can't find this feature right now\n", | |
| " \"pcp_visits\", \n", | |
| " #\"prior_year_specialist_visits\", can't find this feature right now\n", | |
| " \"er_visits\", \n", | |
| " \"inpatient_admits\",\n", | |
| "]\n", | |
| "count_targets = [\n", | |
| " \"pcp_visits\", \"outpatient_visits\", \"er_visits\",\n", | |
| " \"inpatient_admits\", \"home_health_visits\",\n", | |
| " \"rx_fills\", \"dental_visits\", \"equipment_purchases\"\n", | |
| "]\n", | |
| "\n", | |
| "X = df[feature_cols]\n", | |
| "y = df[count_targets].clip(lower=0)" | |
| ] | |
| feature_cols = [ | |
| "AGE22X", "ADSEX42", "INSCOV22", "ADBMI42", | |
| "ADOSTP42", "ADASKALC42", "ADDAYEXER42", | |
| "pcp_visits", | |
| "er_visits", | |
| "inpatient_admits", | |
| ] | |
| # TODO: Add these features when available in the dataset: | |
| # - TOTCHRON: Number of chronic conditions | |
| # - prior_year_specialist_visits: Historical specialist visit data | |
| # Verify all features exist in the dataframe | |
| missing_features = [col for col in feature_cols if col not in df.columns] | |
| if missing_features: | |
| print(f"Warning: Missing features: {missing_features}") | |
| count_targets = [ | |
| "pcp_visits", "outpatient_visits", "er_visits", | |
| "inpatient_admits", "home_health_visits", | |
| "rx_fills", "dental_visits", "equipment_purchases" | |
| ] | |
| X = df[feature_cols] | |
| y = df[count_targets].clip(lower=0) |
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v1.ipynb around lines 692 to 709, the feature
columns list includes commented-out features that are currently missing from the
dataset. To handle this gracefully, implement a check to verify the presence of
each feature in the dataframe before including it in feature_cols and
count_targets. Alternatively, add clear comments explaining why these features
are missing and ensure the code does not break if they are absent by filtering
the lists to only include existing columns before using them to select data from
the dataframe.
| "preprocessor = ColumnTransformer([\n", | ||
| " (\"cat\", OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False), [\"AGE22X\", \"INSCOV22\"])\n", | ||
| "], remainder=\"passthrough\")" |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Consider using all categorical features in preprocessing
Only "AGE22X" and "INSCOV22" are being one-hot encoded, but other categorical features like "ADSEX42", "ADOSTP42", "ADASKALC42", and "ADDAYEXER42" might also need encoding.
🏁 Script executed:
#!/bin/bash
# Check the MEPS codebook documentation for categorical variables
# Looking for variable definitions to understand their data types
# Search for any documentation files that might explain these variables
fd -e md -e txt -e pdf "codebook|variable|meps" --exec grep -l "ADSEX42\|ADOSTP42\|ADASKALC42\|ADDAYEXER42" {} \;
# Check if there are any README files in the data directory
fd READMELength of output: 140
🏁 Script executed:
#!/usr/bin/env python3
import json, sys
nb_path = "python-models/models/MEPS-model-v1.ipynb"
try:
nb = json.load(open(nb_path))
except FileNotFoundError:
sys.exit(f"Notebook not found at {nb_path}")
patterns = [
"ColumnTransformer", "OneHotEncoder", "read_csv",
"AGE22X", "INSCOV22",
"ADSEX42", "ADOSTP42", "ADASKALC42", "ADDAYEXER42",
"X ="
]
for cell in nb.get("cells", []):
if cell.get("cell_type") == "code":
src = "".join(cell.get("source", []))
if any(pat in src for pat in patterns):
print("---- CODE CELL ----")
print(src)
print("-------------------\n")Length of output: 585
🏁 Script executed:
# Search for mentions of the suggested categorical variables in the MEPS notebook
grep -R "ADSEX42\|ADOSTP42\|ADASKALC42\|ADDAYEXER42" -n python-models/models/MEPS-model-v1.ipynb || echo "No occurrences found."
# Dump code cells that define data loading, feature selection, and preprocessing
python3 - << 'EOF'
import json, sys
nb_path = "python-models/models/MEPS-model-v1.ipynb"
try:
nb = json.load(open(nb_path))
except Exception as e:
sys.exit(f"Error loading notebook: {e}")
for idx, cell in enumerate(nb.get("cells", [])):
if cell.get("cell_type") == "code":
src = "".join(cell.get("source", []))
if any(p in src for p in ["read_csv", "ColumnTransformer", "OneHotEncoder", "features", "X ="]):
print(f"--- Cell {idx} ---")
print(src)
print()
EOFLength of output: 2016
Include all categorical features in the preprocessing pipeline
The current ColumnTransformer only one-hot encodes AGE22X and INSCOV22, but according to the MEPS codebook (notebook lines 673–678), the following are also categorical and should be encoded to avoid unintended ordinal assumptions:
ADSEX42(Sex)ADOSTP42(Smoking Frequency)ADASKALC42(Alcohol Consumption)ADDAYEXER42(Exercise Days)
Please update the transformer in python-models/models/MEPS-model-v1.ipynb (Cell 26) as follows:
-preprocessor = ColumnTransformer([
- ("cat", OneHotEncoder(handle_unknown="ignore", sparse_output=False), ["AGE22X", "INSCOV22"])
-], remainder="passthrough")
+preprocessor = ColumnTransformer([
+ ("cat", OneHotEncoder(handle_unknown="ignore", sparse_output=False), [
+ "AGE22X",
+ "INSCOV22",
+ "ADSEX42",
+ "ADOSTP42",
+ "ADASKALC42",
+ "ADDAYEXER42",
+ ])
+], remainder="passthrough")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "preprocessor = ColumnTransformer([\n", | |
| " (\"cat\", OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False), [\"AGE22X\", \"INSCOV22\"])\n", | |
| "], remainder=\"passthrough\")" | |
| preprocessor = ColumnTransformer([ | |
| ("cat", OneHotEncoder(handle_unknown="ignore", sparse_output=False), [ | |
| "AGE22X", | |
| "INSCOV22", | |
| "ADSEX42", | |
| "ADOSTP42", | |
| "ADASKALC42", | |
| "ADDAYEXER42", | |
| ]) | |
| ], remainder="passthrough") |
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v1.ipynb around lines 746 to 748, the
ColumnTransformer only one-hot encodes AGE22X and INSCOV22 but misses other
categorical features ADSEX42, ADOSTP42, ADASKALC42, and ADDAYEXER42. Update the
ColumnTransformer to include all these categorical columns in the OneHotEncoder
list to ensure proper encoding and avoid unintended ordinal assumptions.
| "for target in count_targets:\n", | ||
| " pipe = Pipeline([\n", | ||
| " (\"preproc\", preprocessor),\n", | ||
| " (\"model\", HistGradientBoostingRegressor(\n", | ||
| " loss=\"poisson\",\n", | ||
| " max_iter=200,\n", | ||
| " learning_rate=0.1,\n", | ||
| " random_state=42\n", | ||
| " ))\n", | ||
| " ])\n", | ||
| " pipe.fit(X_train, y_train[target])\n", | ||
| " models[target] = pipe\n", | ||
| " preds[target] = pipe.predict(X_test).clip(min=0)" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling and model persistence
The model training loop should include error handling and save trained models for reuse.
+import joblib
+from pathlib import Path
+
+# Create directory for saved models
+model_dir = Path("../models/saved")
+model_dir.mkdir(parents=True, exist_ok=True)
+
models = {}
preds = pd.DataFrame(index=X_test.index)
for target in count_targets:
- pipe = Pipeline([
- ("preproc", preprocessor),
- ("model", HistGradientBoostingRegressor(
- loss="poisson",
- max_iter=200,
- learning_rate=0.1,
- random_state=42
- ))
- ])
- pipe.fit(X_train, y_train[target])
- models[target] = pipe
- preds[target] = pipe.predict(X_test).clip(min=0)
+ try:
+ pipe = Pipeline([
+ ("preproc", preprocessor),
+ ("model", HistGradientBoostingRegressor(
+ loss="poisson",
+ max_iter=200,
+ learning_rate=0.1,
+ random_state=42
+ ))
+ ])
+ pipe.fit(X_train, y_train[target])
+ models[target] = pipe
+ preds[target] = pipe.predict(X_test).clip(min=0)
+
+ # Save the model
+ joblib.dump(pipe, model_dir / f"{target}_model.pkl")
+ print(f"Successfully trained and saved model for {target}")
+ except Exception as e:
+ print(f"Error training model for {target}: {e}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "for target in count_targets:\n", | |
| " pipe = Pipeline([\n", | |
| " (\"preproc\", preprocessor),\n", | |
| " (\"model\", HistGradientBoostingRegressor(\n", | |
| " loss=\"poisson\",\n", | |
| " max_iter=200,\n", | |
| " learning_rate=0.1,\n", | |
| " random_state=42\n", | |
| " ))\n", | |
| " ])\n", | |
| " pipe.fit(X_train, y_train[target])\n", | |
| " models[target] = pipe\n", | |
| " preds[target] = pipe.predict(X_test).clip(min=0)" | |
| import joblib | |
| from pathlib import Path | |
| # Create directory for saved models | |
| model_dir = Path("../models/saved") | |
| model_dir.mkdir(parents=True, exist_ok=True) | |
| models = {} | |
| preds = pd.DataFrame(index=X_test.index) | |
| for target in count_targets: | |
| try: | |
| pipe = Pipeline([ | |
| ("preproc", preprocessor), | |
| ("model", HistGradientBoostingRegressor( | |
| loss="poisson", | |
| max_iter=200, | |
| learning_rate=0.1, | |
| random_state=42 | |
| )) | |
| ]) | |
| pipe.fit(X_train, y_train[target]) | |
| models[target] = pipe | |
| preds[target] = pipe.predict(X_test).clip(min=0) | |
| # Save the model | |
| joblib.dump(pipe, model_dir / f"{target}_model.pkl") | |
| print(f"Successfully trained and saved model for {target}") | |
| except Exception as e: | |
| print(f"Error training model for {target}: {e}") |
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v1.ipynb around lines 769 to 781, the model
training loop lacks error handling and does not save trained models. Wrap the
training and prediction steps inside a try-except block to catch and log any
exceptions. After successfully training each model, save it to disk using a
suitable method like joblib or pickle for later reuse.
…eep next-env.d.ts tracked
…ge to validate end-to-end.
There was a problem hiding this comment.
Actionable comments posted: 14
♻️ Duplicate comments (9)
python-models/requirements.txt (1)
1-4: Security vulnerability: Update openpyxl to fix XXE injection vulnerabilityThe current version
openpyxl==3.1.2has a known XML External Entity (XXE) injection vulnerability. Additionally,polars==0.20.3is significantly outdated.Apply this fix to address the security vulnerability:
-polars==0.20.3 -openpyxl==3.1.2 +polars==1.31.0 # Latest stable version, verify compatibility +openpyxl==3.1.5 # Fixes XXE vulnerabilityAfter updating, run your notebooks to ensure compatibility with the new versions.
python-models/models/MEPS-model-v1.ipynb (8)
70-75: Fix hardcoded path and useless expressionThe code has a hardcoded absolute path that makes it non-portable, and line 75 contains a useless expression that should be removed or assigned.
+from pathlib import Path + +# Use relative paths from the notebook location +data_dir = Path("../data") df = pl.read_excel( - "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.xlsx", + data_dir / "h243.xlsx", schema_overrides=schema_overrides ) -df.head() +# Display the dataframe head +display(df.head())
93-94: Replace hardcoded paths with relative paths-df.write_csv("/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.csv") -df.write_parquet("/Users/orenj/Desktop/Projects/open-coverage/python-models/data/h243.parquet") +# Use the data_dir variable defined earlier +df.write_csv(data_dir / "h243.csv") # Much faster for future loads +df.write_parquet(data_dir / "h243.parquet") # Even faster, compressed
338-338: Use relative path for parquet file-df_cons = pd.read_parquet("/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2022-consolidated/h243.parquet") +# Use relative path from notebook location +data_dir = Path("../data") +df_cons = pd.read_parquet(data_dir / "MEPS/2022-consolidated/h243.parquet")
366-375: Make event file paths configurableAll event file paths are hardcoded with absolute paths, reducing maintainability and portability.
+from pathlib import Path + +# Configure base data directory +data_dir = Path("../data/MEPS/2023-counts") + event_files = { - "pcp_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248g.xlsx", - "outpatient_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248f.xlsx", - "er_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248e.xlsx", - "inpatient_admits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248d.xlsx", - "home_health_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248h.xlsx", - "rx_fills": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248a.xlsx", - "dental_visits": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248b.xlsx", - "equipment_purchases": "/Users/orenj/Desktop/Projects/open-coverage/python-models/data/MEPS/2023-counts/h248c.xlsx" + "pcp_visits": str(data_dir / "h248g.xlsx"), # Office-based visits + "outpatient_visits": str(data_dir / "h248f.xlsx"), # Outpatient visits + "er_visits": str(data_dir / "h248e.xlsx"), # Emergency Room visits + "inpatient_admits": str(data_dir / "h248d.xlsx"), # Inpatient stays + "home_health_visits": str(data_dir / "h248h.xlsx"), # Home health visits + "rx_fills": str(data_dir / "h248a.xlsx"), # Prescription fills + "dental_visits": str(data_dir / "h248b.xlsx"), # Dental visits + "equipment_purchases": str(data_dir / "h248c.xlsx") # Medical equipment/supplies }
692-709: Add validation for missing featuresThe code has commented-out features that cannot be found. Add proper validation to handle missing features gracefully.
feature_cols = [ "AGE22X", "ADSEX42", "INSCOV22", "ADBMI42", "ADOSTP42", "ADASKALC42", "ADDAYEXER42", - #"TOTCHRON", can't find this feature right now "pcp_visits", - #"prior_year_specialist_visits", can't find this feature right now "er_visits", "inpatient_admits", ] +# TODO: Add these features when available in the dataset: +# - TOTCHRON: Total number of chronic conditions +# - prior_year_specialist_visits: Historical specialist visit data + +# Validate all features exist in the dataframe +missing_features = [col for col in feature_cols if col not in df.columns] +if missing_features: + print(f"Warning: Missing features will be excluded: {missing_features}") + feature_cols = [col for col in feature_cols if col in df.columns]
746-748: Include all categorical features in preprocessingOnly AGE22X and INSCOV22 are being one-hot encoded, but other categorical features should also be encoded to avoid treating them as ordinal.
preprocessor = ColumnTransformer([ - ("cat", OneHotEncoder(handle_unknown="ignore", sparse_output=False), ["AGE22X", "INSCOV22"]) + ("cat", OneHotEncoder(handle_unknown="ignore", sparse_output=False), [ + "AGE22X", + "INSCOV22", + "ADSEX42", # Gender (categorical: 1=Male, 2=Female) + "ADOSTP42", # Smoking frequency (categorical levels) + "ADASKALC42", # Alcohol consumption (categorical levels) + "ADDAYEXER42" # Exercise days (could be treated as ordinal, but safer as categorical) + ]) ], remainder="passthrough")
777-789: Add error handling and model persistenceThe model training loop lacks error handling and doesn't save trained models for reuse.
+import joblib +from pathlib import Path + +# Create directory for saved models +model_dir = Path("../models/saved") +model_dir.mkdir(parents=True, exist_ok=True) + models = {} preds = pd.DataFrame(index=X_test.index) for target in count_targets: - pipe = Pipeline([ - ("preproc", preprocessor), - ("model", HistGradientBoostingRegressor( - loss="poisson", - max_iter=200, - learning_rate=0.1, - random_state=42 - )) - ]) - pipe.fit(X_train, y_train[target]) - models[target] = pipe - preds[target] = pipe.predict(X_test).clip(min=0) + try: + print(f"Training model for {target}...") + pipe = Pipeline([ + ("preproc", preprocessor), + ("model", HistGradientBoostingRegressor( + loss="poisson", + max_iter=200, + learning_rate=0.1, + random_state=42 + )) + ]) + pipe.fit(X_train, y_train[target]) + models[target] = pipe + preds[target] = pipe.predict(X_test).clip(min=0) + + # Save the model + model_path = model_dir / f"model_{target}.pkl" + joblib.dump(pipe, model_path) + print(f" ✓ Model saved to {model_path}") + except Exception as e: + print(f" ✗ Error training model for {target}: {e}") + # Use zeros as fallback predictions + preds[target] = 0
942-951: Make unit costs configurable via external fileUnit costs are hardcoded placeholder values. These should be loaded from a configuration file for easier updates without code changes.
-unit_costs = { - "pcp_visits": 120, - "outpatient_visits": 250, - "er_visits": 1600, - "inpatient_admits": 18000, - "home_health_visits": 100, - "rx_fills": 85, - "dental_visits": 200, - "equipment_purchases": 500 -} +import json +from pathlib import Path + +# Load unit costs from configuration file or use defaults +config_path = Path("../config/unit_costs.json") +if config_path.exists(): + with open(config_path) as f: + unit_costs = json.load(f) + print(f"Loaded unit costs from {config_path}") +else: + print("Warning: Using placeholder unit costs. Create ../config/unit_costs.json with actual values.") + unit_costs = { + "pcp_visits": 120, # Primary care physician visit + "outpatient_visits": 250, # Outpatient facility visit + "er_visits": 1600, # Emergency room visit + "inpatient_admits": 18000, # Inpatient hospital stay + "home_health_visits": 100, # Home health care visit + "rx_fills": 85, # Prescription medication + "dental_visits": 200, # Dental visit + "equipment_purchases": 500 # Durable medical equipment + }
🧹 Nitpick comments (18)
python-models/requirements.txt (2)
24-24: Add missing newline at end of fileThe file is missing a trailing newline, which is a POSIX standard requirement.
uvicorn[standard]==0.27.1 +
5-13: Verify and upgrade ML dependencies in requirements.txtThe ML packages in
python-models/requirements.txtare over a year behind current stable releases. Before merging, please confirm the latest versions on PyPI (for example, by runningpip index versions mlflow,pip index versions scikit-learn, andpip index versions pandas) and update accordingly:
- File: python-models/requirements.txt (lines 5–13)
# MLflow and ML Dependencies- mlflow==2.8.1
- mlflow>=2.18.0 # confirm actual latest after verification
- mlflow[extras]==2.8.1
- mlflow[extras]>=2.18.0 # confirm actual latest after verification
- scikit-learn==1.3.2
- scikit-learn>=1.5.0 # confirm actual latest after verification
- pandas==2.1.3
- pandas>=2.2.0 # confirm actual latest after verification
numpy==1.26.4
matplotlib==3.8.2
seaborn==0.13.0Please verify the exact latest stable versions and update the pinned versions accordingly. </blockquote></details> <details> <summary>python-models/models/model_registry.py (2)</summary><blockquote> `54-68`: **Optimize model version search and add validation** The current implementation searches all versions every time. Consider caching or using MLflow's built-in stage filtering. ```diff def fetch_latest_model(self, stage: str = "None"): """ Fetch the latest model for a given stage. + + Args: + stage: Model stage (e.g., "None", "Staging", "Production", "Archived") + + Returns: + Loaded model or None if not found """ + valid_stages = ["None", "Staging", "Production", "Archived"] + if stage not in valid_stages: + logger.warning(f"Invalid stage '{stage}'. Valid stages: {valid_stages}") + return None + try: - client = mlflow.tracking.MlflowClient() - model_versions = client.search_model_versions(f"name='{self.model_name}'") + # Use filter_string to only get versions in the desired stage + filter_string = f"name='{self.model_name}'" + model_versions = self.client.search_model_versions( + filter_string=filter_string, + order_by=["version_number DESC"] # Get latest version first + ) for version in model_versions: if version.current_stage == stage: - print(f"Fetching latest model at stage: {stage}") + logger.info(f"Fetching model '{self.model_name}' v{version.version} at stage: {stage}") return mlflow.sklearn.load_model(f"models:/{self.model_name}/{version.version}") - print(f"No model found at stage: {stage}") + logger.warning(f"No model '{self.model_name}' found at stage: {stage}") return None except MlflowException as e: - print(f"Error fetching model at stage '{stage}': {str(e)}") + logger.error(f"Error fetching model at stage '{stage}': {str(e)}") return None
69-70: Add missing trailing newlinereturn None +python-models/health-profile-model-implementation.md (3)
81-84: Improve documentation formatting and add code blocksThe local development commands should be properly formatted as code blocks for better readability.
- Ops/dev setup - - Run Python service locally: `uvicorn python-models.server.server:app --host 127.0.0.1 --port 8001 --reload` or `python python-models/server/server.py`. - - Add `.env.local`: `PY_UTILIZATION_BASE_URL=http://127.0.0.1:8001`. + - Run Python service locally: + ```bash + uvicorn python-models.server.server:app --host 127.0.0.1 --port 8001 --reload + # or + python python-models/server/server.py + ``` + - Add `.env.local`: + ```bash + PY_UTILIZATION_BASE_URL=http://127.0.0.1:8001 + ``` - Keep everything server-side; no keys are exposed.
97-106: Format the "How to run" section as a proper code blockThe commands at the end should be formatted as code blocks following markdown best practices.
### How to run Part 1 locally -Start the Python service (ensure python-models/models/v2-pkl exists): -uvicorn python-models.server.server:app --host 127.0.0.1 --port 8001 --reload -Configure env (optional, default is http://127.0.0.1:8001): -.env.local: PY_UTILIZATION_BASE_URL=http://127.0.0.1:8001 -Start Next dev server: -bun run dev -Test: -Visit /utilization-model-test -Click "Health Check" (should say healthy) -Enter Age (e.g., 45) and optional BMI (e.g., 27.5), then "Predict" to see 8 outputs +1. Start the Python service (ensure `python-models/models/v2-pkl` exists): + ```bash + uvicorn python-models.server.server:app --host 127.0.0.1 --port 8001 --reload + ``` + +2. Configure environment (optional, default is `http://127.0.0.1:8001`): + ```bash + # .env.local + PY_UTILIZATION_BASE_URL=http://127.0.0.1:8001 + ``` + +3. Start Next.js dev server: + ```bash + bun run dev + ``` + +4. Test the integration: + - Visit `/utilization-model-test` + - Click "Health Check" (should say healthy) + - Enter Age (e.g., 45) and optional BMI (e.g., 27.5) + - Click "Predict" to see 8 output counts
53-59: Consider using a more robust caching solutionThe document mentions using in-memory cache with TTL, but for production, consider using Redis or similar for better persistence and scalability across multiple server instances.
For production deployment, consider:
- Redis for distributed caching across multiple server instances
- Cache warming strategies for frequently accessed predictions
- Monitoring cache hit rates and adjusting TTL based on usage patterns
- Implementing cache invalidation when models are updated
python-models/server/server.py (5)
59-63: Replace setattr with direct assignmentUsing
setattrwith a constant string is unnecessary and less readable than direct assignment.if mp_main is None: mp_main = types.ModuleType("__mp_main__") sys.modules["__mp_main__"] = mp_main -setattr(mp_main, "neg_to_nan", neg_to_nan) +mp_main.neg_to_nan = neg_to_nan
172-182: Add model validation after loadingAfter loading models, consider validating that they have the expected structure and can handle the expected input shape.
Consider adding validation to ensure loaded models are compatible:
def _validate_model(pipe, target: str) -> None: """Validate that a loaded pipeline can handle expected input.""" try: # Create a sample input with all None values sample = pd.DataFrame([{col: None for col in FEATURE_COLS}]) _ = pipe.predict(sample) except Exception as e: raise RuntimeError(f"Model validation failed for {target}: {e}")Would you like me to help implement comprehensive model validation logic?
206-210: Consider adding input validation and prediction boundsThe prediction logic could benefit from validating that predictions are within reasonable bounds for healthcare utilization counts.
outputs: Dict[str, int] = {} for target, pipe in models.items(): # Each saved pipeline includes preprocessing and model pred_value = float(pipe.predict(X)[0]) - outputs[target] = max(0, int(round(pred_value))) + # Clamp predictions to reasonable bounds (0-365 for most, higher for rx_fills) + max_value = 1000 if target == "rx_fills" else 365 + outputs[target] = min(max(0, int(round(pred_value))), max_value)
217-221: Consider using a production server for deploymentThe current setup uses uvicorn with reload=True, which is only suitable for development.
For production deployment:
- Remove
reload=True- Use a production ASGI server like gunicorn with uvicorn workers:
gunicorn python-models.server.server:app -w 4 -k uvicorn.workers.UvicornWorker- Consider containerizing with Docker for consistent deployment
- Add health monitoring and logging middleware
223-224: Add missing trailing newlineuvicorn.run(app, host="127.0.0.1", port=8001, reload=True) +app/api/utilization-model/predict/route.ts (2)
32-37: Consider usingprocess.envdirectly with proper typingThe current approach of accessing environment variables through
globalThisis unnecessarily complex and could be simplified.Consider this simpler approach that maintains edge runtime compatibility:
-function getPythonBaseUrl(): string { - const env = (globalThis as any)?.process?.env as - | Record<string, string | undefined> - | undefined; - return env?.PY_UTILIZATION_BASE_URL || "http://127.0.0.1:8001"; -} +function getPythonBaseUrl(): string { + return process.env.PY_UTILIZATION_BASE_URL || "http://127.0.0.1:8001"; +}If there are edge runtime compatibility concerns, you could also use:
declare global { namespace NodeJS { interface ProcessEnv { PY_UTILIZATION_BASE_URL?: string; } } }
83-89: Consider more robust JSON parsing error handlingThe current approach silently sets
jsontonullon parse failure, which could mask legitimate parsing errors. Consider preserving the parse error for better diagnostics.- const text = await res.text().catch(() => ""); - let json: unknown; - try { - json = text ? JSON.parse(text) : null; - } catch { - json = null; - } + const text = await res.text().catch(() => ""); + let json: unknown; + let parseError: string | null = null; + try { + json = text ? JSON.parse(text) : null; + } catch (e) { + json = null; + parseError = e instanceof Error ? e.message : "Invalid JSON"; + }Then include the parse error in the error response:
return NextResponse.json( { status: "error", code: res.status, - detail: "Upstream prediction failed or returned invalid response", + detail: parseError + ? `Upstream returned invalid JSON: ${parseError}` + : "Upstream prediction failed or returned invalid response", upstream: text?.slice(0, 500), }, { status: 502 } );app/utilization-model-test/page.tsx (3)
47-49: Improve error handling to preserve error type informationUsing
anytype for caught errors loses type safety. Consider using proper error type handling.- } catch (e: any) { + } catch (e) { setHealthStatus(""); - setError(e?.message || "Health check failed"); + setError(e instanceof Error ? e.message : "Health check failed"); }
70-71: Improve error handling consistencySimilar to the health check function, avoid using
anytype for caught errors.- } catch (e: any) { - setError(e?.message || "Prediction failed"); + } catch (e) { + setError(e instanceof Error ? e.message : "Prediction failed");
99-112: Add input constraints to prevent invalid valuesConsider adding HTML5 input constraints to provide immediate feedback and prevent invalid inputs at the UI level.
<Input type="number" value={age} onChange={(e) => setAge(e.target.value)} placeholder="e.g., 45" + min="0" + max="150" + step="1" /><Input type="number" value={bmi} onChange={(e) => setBmi(e.target.value)} placeholder="e.g., 27.5" + min="10" + max="100" + step="0.1" />python-models/models/MEPS-model-v2.ipynb (1)
815-855: Document the alignment between feature columns and server APIThe
feature_colslist (which becomesFEATURE_COLSin the server) is critical for API compatibility. Any mismatch will cause prediction failures.Consider creating a shared configuration file that both the notebook and server can import to ensure consistency:
# shared_config.py FEATURE_COLS = [ "age_years_2022", "gender", # ... rest of features ] COUNT_TARGETS = [ "pcp_visits", "outpatient_visits", # ... rest of targets ]This would prevent drift between the model training and serving code.
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⛔ Files ignored due to path filters (9)
python-models/models/.DS_Storeis excluded by!**/.DS_Storepython-models/models/v2-pkl/model_dental_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_equipment_purchases.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_er_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_home_health_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_inpatient_admits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_outpatient_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_pcp_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_rx_fills.pklis excluded by!**/*.pkl
📒 Files selected for processing (13)
.gitignore(1 hunks)app/api/utilization-model/health/route.ts(1 hunks)app/api/utilization-model/predict/route.ts(1 hunks)app/utilization-model-test/page.tsx(1 hunks)next-env.d.ts(1 hunks)python-models/health-profile-model-implementation.md(1 hunks)python-models/models/MEPS-model-v1.ipynb(1 hunks)python-models/models/MEPS-model-v2.ipynb(1 hunks)python-models/models/model_registry.py(1 hunks)python-models/requirements.txt(1 hunks)python-models/server/__init__.py(1 hunks)python-models/server/server.py(1 hunks)tsconfig.json(2 hunks)
✅ Files skipped from review due to trivial changes (4)
- next-env.d.ts
- python-models/server/init.py
- .gitignore
- tsconfig.json
🧰 Additional context used
🧬 Code Graph Analysis (2)
app/utilization-model-test/page.tsx (2)
app/error.tsx (1)
Error(6-28)python-models/server/server.py (1)
predict(198-214)
app/api/utilization-model/health/route.ts (1)
app/error.tsx (1)
Error(6-28)
🪛 markdownlint-cli2 (0.17.2)
python-models/health-profile-model-implementation.md
99-99: Bare URL used
(MD034, no-bare-urls)
100-100: Bare URL used
(MD034, no-bare-urls)
🪛 Ruff (0.12.2)
python-models/models/MEPS-model-v2.ipynb
3-3: sys imported but unused
Remove unused import: sys
(F401)
82-82: Found useless expression. Either assign it to a variable or remove it.
(B018)
196-196: Undefined name display
(F821)
400-400: Undefined name display
(F821)
480-480: Undefined name display
(F821)
555-555: Undefined name display
(F821)
python-models/models/MEPS-model-v1.ipynb
72-72: Found useless expression. Either assign it to a variable or remove it.
(B018)
138-138: numpy imported but unused
Remove unused import: numpy
(F401)
python-models/server/server.py
63-63: Do not call setattr with a constant attribute value. It is not any safer than normal property access.
Replace setattr with assignment
(B010)
194-194: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
214-214: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
🔇 Additional comments (6)
app/api/utilization-model/predict/route.ts (1)
39-54: LGTM! Robust response validation implementationThe type guard correctly validates all required fields with proper type checking, ensuring the upstream response conforms to the expected shape.
app/api/utilization-model/health/route.ts (1)
40-41: LGTM! Good defensive programmingThe use of
.catch(() => ({}))ensures the route never fails due to JSON parsing errors, providing a robust fallback.python-models/models/MEPS-model-v2.ipynb (4)
970-976: LGTM! Clever handling of MEPS reserved codesThe approach to convert MEPS negative reserved codes to NaN is well-documented and appropriate for the HistGradientBoostingRegressor which has native NaN support.
1086-1091: Good choice of loss function for count dataUsing Poisson loss for count predictions is statistically appropriate. The model correctly handles count data characteristics.
1351-1356: Well-implemented sampling optimizationThe Slovin's sampling method is a smart optimization to reduce SHAP computation time while maintaining statistical validity. Good reference to the supporting paper.
1521-1536: OneHotEncoder Unknown-Category Warnings Are ExpectedThe
UserWarningyou’re seeing—
“Found unknown categories in columns [0, 10] during transform…”—
is emitted by OneHotEncoder whenhandle_unknown="ignore"is in use. This warning simply informs you that unseen test–set categories were dropped (encoded as all zeros) and does not indicate a malfunction in your pipeline.• Location:
– python-models/models/MEPS-model-v2.ipynb, cell defining
categorical_transformer = Pipeline([("onehot", OneHotEncoder(handle_unknown="ignore", …))])• Why it’s safe:
– You deliberately chosehandle_unknown="ignore"to prevent transform failures on new categories.
– The warning is informational; your downstream model will still receive a valid numeric array.• Optional cleanup:
– Suppress the noise by adding at the top of your notebook:
python import warnings warnings.filterwarnings( "ignore", message="Found unknown categories in columns.*during transform" )
– Or document in a brief comment that these warnings are expected when test data contains novel categories.No further code changes are required.
Likely an incorrect or invalid review comment.
| function getPythonBaseUrl(): string { | ||
| const env = (globalThis as any)?.process?.env as | ||
| | Record<string, string | undefined> | ||
| | undefined; | ||
| return env?.PY_UTILIZATION_BASE_URL || "http://127.0.0.1:8001"; | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Code duplication: Extract shared environment access logic
This getPythonBaseUrl function is duplicated from the predict route. Consider extracting it to a shared utility.
Create a shared utility file app/api/utilization-model/utils.ts:
// app/api/utilization-model/utils.ts
export function getPythonBaseUrl(): string {
return process.env.PY_UTILIZATION_BASE_URL || "http://127.0.0.1:8001";
}Then update both routes:
-// Avoid direct Node `process` typing to satisfy edge runtimes and linting
-function getPythonBaseUrl(): string {
- const env = (globalThis as any)?.process?.env as
- | Record<string, string | undefined>
- | undefined;
- return env?.PY_UTILIZATION_BASE_URL || "http://127.0.0.1:8001";
-}
+import { getPythonBaseUrl } from "./utils";🤖 Prompt for AI Agents
In app/api/utilization-model/health/route.ts around lines 4 to 9, the
getPythonBaseUrl implementation is duplicated from the predict route; extract
the shared logic into a new module app/api/utilization-model/utils.ts that
exports getPythonBaseUrl (returning process.env.PY_UTILIZATION_BASE_URL ||
"http://127.0.0.1:8001"), then replace the local function in this file (and the
predict route) with an import from that utils file and remove the duplicated
code.
| } catch (error: unknown) { | ||
| clearTimeout(timeout); | ||
| const message = error instanceof Error ? error.message : "Unknown error"; | ||
| const status = message.includes("The user aborted a request") ? 504 : 500; |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve timeout detection consistency
Similar to the predict route, the timeout detection logic should be more robust.
- const status = message.includes("The user aborted a request") ? 504 : 500;
+ const isTimeout = error instanceof Error &&
+ (error.name === 'AbortError' || message.includes("abort"));
+ const status = isTimeout ? 504 : 500;📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| const status = message.includes("The user aborted a request") ? 504 : 500; | |
| const isTimeout = error instanceof Error && | |
| (error.name === 'AbortError' || message.includes("abort")); | |
| const status = isTimeout ? 504 : 500; |
🤖 Prompt for AI Agents
In app/api/utilization-model/health/route.ts around line 45, the timeout
detection currently only checks message.includes("The user aborted a request");
update it to mirror the predict route's more robust logic: safely extract the
error message (handle null/undefined), check for AbortError (error.name ===
'AbortError'), and look for multiple timeout indicators (e.g., "The user aborted
a request", "timed out", or other provider-specific timeout phrases) and set
status = 504 when any match; otherwise leave status = 500. Ensure you handle
non-string messages without throwing.
| } catch (error: unknown) { | ||
| clearTimeout(timeout); | ||
| const message = error instanceof Error ? error.message : "Unknown error"; | ||
| const status = message.includes("The user aborted a request") ? 504 : 500; |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve timeout detection logic
The current string matching approach for detecting timeout errors is brittle and may not work across different JavaScript environments or future API changes.
- const status = message.includes("The user aborted a request") ? 504 : 500;
+ const isTimeout = error instanceof Error &&
+ (error.name === 'AbortError' || message.includes("abort"));
+ const status = isTimeout ? 504 : 500;📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| const status = message.includes("The user aborted a request") ? 504 : 500; | |
| const isTimeout = error instanceof Error && | |
| (error.name === 'AbortError' || message.includes("abort")); | |
| const status = isTimeout ? 504 : 500; |
🤖 Prompt for AI Agents
In app/api/utilization-model/predict/route.ts around line 107, the code
currently detects timeouts by string-matching the message "The user aborted a
request"; replace this brittle check with robust error-type checks: inspect the
thrown error's properties (e.g. error.name === 'AbortError' || error.code ===
'ETIMEDOUT' || error.type === 'aborted') and fallback to message substring only
if those properties are absent, then set status = 504 when any of those timeout
indicators are present and 500 otherwise; ensure the implementation safely
handles undefined error properties to avoid runtime exceptions.
| "ename": "NameError", | ||
| "evalue": "name 'preds' is not defined", | ||
| "output_type": "error", | ||
| "traceback": [ | ||
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | ||
| "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", | ||
| "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m composed \u001b[38;5;241m=\u001b[39m preds\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cat, cost \u001b[38;5;129;01min\u001b[39;00m unit_costs\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 3\u001b[0m composed[cat] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m cost\n", | ||
| "\u001b[0;31mNameError\u001b[0m: name 'preds' is not defined" | ||
| ] |
There was a problem hiding this comment.
Fix the intentional NameError for demonstration purposes
This cell will always fail with NameError because preds is not defined in this execution context. This appears to be an intentional error for demonstration.
Either remove this cell or add a comment explaining it's for demonstration:
+# NOTE: This cell demonstrates an error scenario.
+# The 'preds' variable must be defined by running the model training cells above.
composed = preds.copy()
for cat, cost in unit_costs.items():
composed[cat] *= cost
composed["total_allowed_pred"] = composed.sum(axis=1)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "ename": "NameError", | |
| "evalue": "name 'preds' is not defined", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", | |
| "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m composed \u001b[38;5;241m=\u001b[39m preds\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cat, cost \u001b[38;5;129;01min\u001b[39;00m unit_costs\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 3\u001b[0m composed[cat] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m cost\n", | |
| "\u001b[0;31mNameError\u001b[0m: name 'preds' is not defined" | |
| ] | |
| # NOTE: This cell demonstrates an error scenario. | |
| # The 'preds' variable must be defined by running the model training cells above. | |
| composed = preds.copy() | |
| for cat, cost in unit_costs.items(): | |
| composed[cat] *= cost | |
| composed["total_allowed_pred"] = composed.sum(axis=1) |
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v1.ipynb around lines 969 to 977, the cell
raises a NameError because the variable `preds` is not defined; fix it by either
removing the failing cell or replacing its content with a clear comment that it
intentionally demonstrates a NameError (so it should not be executed), or
alternatively initialize `preds` with a minimal example value or wrap the code
in a guarded block (e.g., check for existence or use try/except) so the notebook
can run without error while preserving the demonstration.
| "TARGET = count_targets[7] # pick a target to explain (0-7)\n", | ||
| "pipe = joblib.load(f\"/Users/orenj/Desktop/Projects/open-coverage/python-models/models/v2-pkl/model_{TARGET}.pkl\")\n", |
There was a problem hiding this comment.
Fix hardcoded model load path
The model load path is hardcoded and should use the same configurable approach as the save paths.
-TARGET = count_targets[7] # pick a target to explain (0-7)
-pipe = joblib.load(f"/Users/orenj/Desktop/Projects/open-coverage/python-models/models/v2-pkl/model_{TARGET}.pkl")
+TARGET = count_targets[7] # pick a target to explain (0-7)
+pipe = joblib.load(MODEL_DIR / f"model_{TARGET}.pkl")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "TARGET = count_targets[7] # pick a target to explain (0-7)\n", | |
| "pipe = joblib.load(f\"/Users/orenj/Desktop/Projects/open-coverage/python-models/models/v2-pkl/model_{TARGET}.pkl\")\n", | |
| TARGET = count_targets[7] # pick a target to explain (0-7) | |
| pipe = joblib.load(MODEL_DIR / f"model_{TARGET}.pkl") |
🤖 Prompt for AI Agents
In python-models/models/MEPS-model-v2.ipynb around lines 1373-1374, the code
loads a model from a hardcoded absolute path; replace this with the same
configurable approach used when saving models: read the model directory/filename
from the existing config or a MODEL_DIR variable (or construct a relative path
next to the notebook using pathlib), build the filename using TARGET (e.g.,
model_{TARGET}.pkl) and call joblib.load with that constructed path so the load
path is not hardcoded.
| def register_model(self, run_id: str): | ||
| """ | ||
| Register a model in MLflow given a specific run ID. | ||
| """ | ||
| try: | ||
| result = mlflow.register_model( | ||
| f"runs:/{run_id}/model", | ||
| self.model_name | ||
| ) | ||
| print(f"Registering model with name: {self.model_name} and run ID: {run_id}") | ||
| except MlflowException as e: | ||
| print(f"Error registering model: {str(e)}") | ||
| result = None | ||
|
|
||
| return result |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Replace print statements with proper logging
Using print() for logging in production code is not recommended. It lacks severity levels, timestamps, and proper output control.
Add logging configuration at the top of the file:
import logging
logger = logging.getLogger(__name__)Then replace print statements:
- print(f"Registering model with name: {self.model_name} and run ID: {run_id}")
+ logger.info(f"Registering model with name: {self.model_name} and run ID: {run_id}")
except MlflowException as e:
- print(f"Error registering model: {str(e)}")
+ logger.error(f"Error registering model: {str(e)}")🤖 Prompt for AI Agents
In python-models/models/model_registry.py around lines 19 to 33, replace the two
print() calls with proper module-level logging: add "import logging" and "logger
= logging.getLogger(__name__)" at the top of the file, change the success
message to logger.info(f"Registering model with name: {self.model_name} and run
ID: {run_id}"), and change the exception handling to use logger.error (or
logger.exception if you want the traceback) to log the error details instead of
printing; keep returning result as before.
| client = mlflow.tracking.MlflowClient() | ||
| client.transition_model_version_stage( | ||
| self.model_name, | ||
| version, | ||
| stage | ||
| ) | ||
| print(f"Transitioning model '{self.model_name}' to stage: {stage}") | ||
| except MlflowException as e: | ||
| print(f"Error transitioning model to stage '{stage}': {str(e)}") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Use MlflowClient consistently and improve error handling
The code creates a new MlflowClient instance each time and should reuse it. Also, replace print statements with logging.
Store the client as an instance variable and use logging:
class ModelRegistryManager:
def __init__(self, model_name: str):
self.model_name = model_name
+ self.client = mlflow.tracking.MlflowClient()
def transition_model_stage(self, version: int, stage: str):
"""
Transition a model version to a different stage.
"""
try:
- client = mlflow.tracking.MlflowClient()
- client.transition_model_version_stage(
+ self.client.transition_model_version_stage(
self.model_name,
version,
stage
)
- print(f"Transitioning model '{self.model_name}' to stage: {stage}")
+ logger.info(f"Transitioning model '{self.model_name}' to stage: {stage}")
except MlflowException as e:
- print(f"Error transitioning model to stage '{stage}': {str(e)}")
+ logger.error(f"Error transitioning model to stage '{stage}': {str(e)}")
+ raise # Re-raise to let caller handle the errorCommittable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In python-models/models/model_registry.py around lines 40 to 48, the method
creates a new MlflowClient each call and uses print statements; change this to
reuse a single MlflowClient stored on the instance (initialize self.client =
MlflowClient() in __init__ or lazily create and cache it), replace print(...)
with a module/class logger (logger = logging.getLogger(__name__) at top), use
logger.info for the successful transition message, and use logger.exception or
logger.error(..., exc_info=True) inside the except block to log the error with
stack trace and details.
| except Exception as e: # noqa: BLE001 | ||
| raise HTTPException(status_code=500, detail=str(e)) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add exception chaining for better error context
When re-raising exceptions in an except block, use from to preserve the original exception context.
except Exception as e: # noqa: BLE001
- raise HTTPException(status_code=500, detail=str(e))
+ raise HTTPException(status_code=500, detail=str(e)) from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| except Exception as e: # noqa: BLE001 | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| except Exception as e: # noqa: BLE001 | |
| raise HTTPException(status_code=500, detail=str(e)) from e |
🧰 Tools
🪛 Ruff (0.12.2)
194-194: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
🤖 Prompt for AI Agents
In python-models/server/server.py around lines 193 to 194, the except block
re-raises an HTTPException without exception chaining; update the raise to
include the original exception (raise HTTPException(status_code=500,
detail=str(e)) from e) so the original traceback is preserved and provides
better debugging context.
| except Exception as e: # noqa: BLE001 | ||
| raise HTTPException(status_code=500, detail=str(e)) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add exception chaining in predict endpoint
Same issue as above - preserve exception context when re-raising.
except Exception as e: # noqa: BLE001
- raise HTTPException(status_code=500, detail=str(e))
+ raise HTTPException(status_code=500, detail=str(e)) from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| except Exception as e: # noqa: BLE001 | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| except Exception as e: # noqa: BLE001 | |
| raise HTTPException(status_code=500, detail=str(e)) from e |
🧰 Tools
🪛 Ruff (0.12.2)
214-214: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
🤖 Prompt for AI Agents
In python-models/server/server.py around lines 213 to 214, the predict endpoint
currently catches Exception and raises HTTPException without preserving the
original exception context; change the raise to use exception chaining (i.e.,
raise HTTPException(status_code=500, detail=str(e)) from e) so the original
traceback is preserved for debugging and logging.
- Upgraded Next.js from 14.2.16 to 15.4.6 in bun.lock and package.json. - Added new optional input fields for gender, race/ethnicity, census region, employment status, family size, usual source of care, difficulty walking stairs, any activity limitation, and K6 distress score in the utilization model test page. - Implemented clamping for age and BMI inputs to ensure valid values. - Updated the next-env.d.ts reference link for TypeScript configuration. - Updated Python model requirements to use scikit-learn 1.5.1.
There was a problem hiding this comment.
Actionable comments posted: 2
♻️ Duplicate comments (1)
app/utilization-model-test/page.tsx (1)
69-94: Guard against NaN and out-of-range values before populating features (extends prior feedback).Inputs converted with Number(...) can yield NaN and propagate invalid values (e.g., age, BMI, coded fields). Add finite checks and clamp where applicable. Also narrow the features type to Record<string, number>.
Apply this diff:
- const features: Record<string, any> = {}; + const features: Record<string, number> = {}; + const parseFinite = (raw: string, label: string) => { + const n = Number(raw); + if (!Number.isFinite(n)) { + throw new Error(`Please enter a valid ${label}`); + } + return n; + }; + const clamp = (n: number, min: number, max: number) => + Math.min(max, Math.max(min, n)); if (age.trim()) { - const ageNum = Number(age); - const clampedAge = Math.min(120, Math.max(0, Math.floor(ageNum))); - features.age_years_2022 = clampedAge; + const v = parseFinite(age, "age between 0 and 120"); + features.age_years_2022 = clamp(Math.floor(v), 0, 120); } if (bmi.trim()) { - const bmiNum = Number(bmi); - const clampedBmi = Math.min(100, Math.max(10, bmiNum)); - features.bmi = clampedBmi; + const v = parseFinite(bmi, "BMI between 10 and 100"); + features.bmi = clamp(v, 10, 100); } - if (gender.trim()) features.gender = Number(gender); - if (raceEthnicity.trim()) features.race_ethnicity = Number(raceEthnicity); - if (censusRegion.trim()) features.census_region = Number(censusRegion); - if (employmentStatus.trim()) - features.employment_status = Number(employmentStatus); - if (familySize.trim()) features.family_size = Number(familySize); - if (hasUsualSourceOfCare.trim()) - features.has_usual_source_of_care = Number(hasUsualSourceOfCare); - if (difficultyWalkingStairs.trim()) - features.difficulty_walking_stairs = Number(difficultyWalkingStairs); - if (anyActivityLimitation.trim()) - features.any_activity_limitation = Number(anyActivityLimitation); - if (k6DistressScore.trim()) - features.k6_distress_score = Number(k6DistressScore); + if (gender.trim()) features.gender = Math.trunc(parseFinite(gender, "sex code (1 or 2)")); + if (raceEthnicity.trim()) features.race_ethnicity = Math.trunc(parseFinite(raceEthnicity, "race/ethnicity code")); + if (censusRegion.trim()) features.census_region = Math.trunc(parseFinite(censusRegion, "region code")); + if (employmentStatus.trim()) features.employment_status = Math.trunc(parseFinite(employmentStatus, "employment status code")); + if (familySize.trim()) features.family_size = Math.max(1, Math.trunc(parseFinite(familySize, "family size"))); + if (hasUsualSourceOfCare.trim()) features.has_usual_source_of_care = Math.min(1, Math.max(0, Math.trunc(parseFinite(hasUsualSourceOfCare, "0/1")))); + if (difficultyWalkingStairs.trim()) features.difficulty_walking_stairs = Math.min(1, Math.max(0, Math.trunc(parseFinite(difficultyWalkingStairs, "0/1")))); + if (anyActivityLimitation.trim()) features.any_activity_limitation = Math.min(1, Math.max(0, Math.trunc(parseFinite(anyActivityLimitation, "0/1")))); + if (k6DistressScore.trim()) features.k6_distress_score = clamp(Math.trunc(parseFinite(k6DistressScore, "K6 score (0–24)")), 0, 24);
🧹 Nitpick comments (2)
app/utilization-model-test/page.tsx (1)
246-255: Constrain the K6 input to its documented range in the UI.Enforce 0–24 at the input level to prevent accidental out-of-range values.
<Label className="text-sm"> K6 Psychological Distress (0–24) </Label> <Input type="number" + min={0} + max={24} + step={1} value={k6DistressScore} onChange={(e) => setK6DistressScore(e.target.value)} placeholder="optional" />package.json (1)
10-12: Consider portability of Bash-dependent scripts in package.json.Using bash -lc and curl assumes Bash and curl are available (e.g., macOS/Linux). If Windows devs are expected, provide a cross-platform alternative or document prerequisites.
I can add a small Node.js script (using fetch) to replace the curl-based commands and avoid bash -lc. Want me to include it?
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
⛔ Files ignored due to path filters (1)
bun.lockis excluded by!**/*.lock
📒 Files selected for processing (8)
app/layout.tsx(1 hunks)app/utilization-model-test/page.tsx(1 hunks)next-env.d.ts(1 hunks)package.json(3 hunks)python-models/dev.sh(1 hunks)python-models/health-profile-model-implementation.md(1 hunks)python-models/models/features.md(1 hunks)python-models/requirements.txt(1 hunks)
✅ Files skipped from review due to trivial changes (2)
- python-models/models/features.md
- app/layout.tsx
🚧 Files skipped from review as they are similar to previous changes (3)
- next-env.d.ts
- python-models/requirements.txt
- python-models/health-profile-model-implementation.md
| "input-otp": "1.4.1", | ||
| "lucide-react": "^0.454.0", | ||
| "next": "14.2.16", | ||
| "next": "^15.4.6", |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify Next.js version bump to ^15.4.6 and Node engine constraints before merging.
This is a major upgrade with potential breaking changes. Ensure the version exists, confirm Node version requirements, and consider pinning or deferring if not needed for this PR.
Run this to confirm availability and latest versions:
And check Node engine requirements from the official docs:
If upgrading, add an engines field to avoid CI/dev drift:
{
"name": "my-v0-project",
"version": "0.1.0",
"private": true,
+ "engines": {
+ "node": ">=18.18.0"
+ },
"scripts": {🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "Next latest:"; npm view next version
echo "Does 15.4.6 exist?"; npm view next@15.4.6 version || echo "Not found"Length of output: 170
🌐 Web query:
What Node.js version is required for Next.js 15?
💡 Result:
Next.js 15 requires Node.js v18.18.0 or newer. (nextjs.org)
If you want, I can help you check your current Node version (run node -v) or walk through upgrading it.
Citations:
Enforce Node.js ≥18.18.0 in package.json for Next.js 15 bump
Next.js 15.4.6 is valid (latest), and it requires Node.js v18.18.0 or newer. To prevent CI/dev drift, please add an engines field:
• File: package.json
• Around line 4 (after "private": true,), insert:
"private": true,
+ "engines": {
+ "node": ">=18.18.0"
+ },
"scripts": {📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "next": "^15.4.6", | |
| "private": true, | |
| "engines": { | |
| "node": ">=18.18.0" | |
| }, | |
| "scripts": { |
🤖 Prompt for AI Agents
In package.json around line 4 (immediately after "private": true), add an
"engines" entry that enforces Node.js version >=18.18.0 by inserting an
"engines" object with the node constraint (e.g. "node": ">=18.18.0"); ensure the
JSON remains valid (commas adjusted) and commit the change so CI and devs are
required to use Node.js v18.18.0 or newer.
| echo "[py:dev] Starting server at http://$HOST:$PORT" | ||
| exec uvicorn python-models.server.server:app --host "$HOST" --port "$PORT" --reload | ||
|
|
There was a problem hiding this comment.
Fix invalid module path (hyphenated package name) to start uvicorn successfully.
python cannot import a module named "python-models..." (hyphen is invalid). This prevents the dev server from starting.
Apply this minimal fix: run uvicorn from the python-models directory and reference the proper package path.
-echo "[py:dev] Starting server at http://$HOST:$PORT"
-exec uvicorn python-models.server.server:app --host "$HOST" --port "$PORT" --reload
+echo "[py:dev] Starting server at http://$HOST:$PORT"
+cd "$SCRIPT_DIR"
+exec uvicorn server.server:app --host "$HOST" --port "$PORT" --reloadAlternative (also valid): keep working dir unchanged and use the venv’s interpreter explicitly:
-exec uvicorn python-models.server.server:app --host "$HOST" --port "$PORT" --reload
+cd "$SCRIPT_DIR"
+exec python -m uvicorn server.server:app --host "$HOST" --port "$PORT" --reload- Added radio buttons for gender selection and a dropdown for race/ethnicity in the utilization model test page. - Updated input labels for age and BMI to include valid ranges. - Modified the MEPS model notebook to new SHAP value analysis and improved data handling. - Updated the v2 model (and pkl files) because it wasn't trained on the K6 feature. - Adjusted file paths for data loading in the MEPS model to ensure compatibility.
- Reorganized import statements for better readability in the health profile page. - Improved state management and effect hooks for member selection and utilization display. - Updated the utilization model test page to replace numeric inputs with dropdowns and radio buttons for better user experience. - Added validation for family size and K6 distress score inputs to ensure valid ranges. - Enhanced labels for clarity and consistency across the input fields. - Added button on Health Profile page that links to the new prediction page
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Actionable comments posted: 2
🧹 Nitpick comments (2)
app/health-profile/page.tsx (2)
257-259: Use functional state update for toggle to avoid stale closures.Small resilience improvement.
Apply this diff:
- onToggleUtilization={() => setShowUtilization(!showUtilization)} + onToggleUtilization={() => + setShowUtilization((prev) => !prev) + }
65-71: Simplify hasMemberWithData check.
mu.utilizationalready implies basic data after the fix above. The extrahasBasicDatacheck is redundant.Apply this diff:
- const hasMemberWithData = memberUtilizations.some( - (mu) => mu.utilization && mu.hasBasicData - ); + const hasMemberWithData = memberUtilizations.some( + (mu) => !!mu.utilization + );
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⛔ Files ignored due to path filters (8)
python-models/models/v2-pkl/model_dental_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_equipment_purchases.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_er_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_home_health_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_inpatient_admits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_outpatient_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_pcp_visits.pklis excluded by!**/*.pklpython-models/models/v2-pkl/model_rx_fills.pklis excluded by!**/*.pkl
📒 Files selected for processing (2)
app/health-profile/page.tsx(6 hunks)app/utilization-model-test/page.tsx(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- app/utilization-model-test/page.tsx
🧰 Additional context used
🧬 Code Graph Analysis (1)
app/health-profile/page.tsx (7)
lib/health-profile-store.ts (2)
useHealthProfileStore(80-158)Member(23-39)lib/hooks/use-screen-reader.tsx (1)
useScreenReaderAnnouncement(4-23)lib/utilization-engine.ts (1)
calculateHealthcareUtilization(223-411)lib/enhanced-health-profile-store.ts (1)
Member(25-25)components/health-profile/member-card.tsx (1)
MemberCard(28-140)components/health-profile/index.ts (2)
MemberCard(6-6)UtilizationAnalysis(7-7)components/health-profile/utilization-analysis.tsx (1)
UtilizationAnalysis(29-161)
🔇 Additional comments (2)
app/health-profile/page.tsx (2)
138-149: Nice: Collapse state logic is simple and correct.
setCollapsedCards((prev) => ({ ...prev, [memberId]: !prev[memberId] }))is clean and avoids stale state bugs.- Defaulting to open for the first card (
index === 0) and collapsed otherwise is intuitive.
14-17: Import is correct — keep as-islib/hooks/use-screen-reader.tsx exports both the hook and the component (named exports).
app/health-profile/page.tsx correctly imports { ScreenReaderAnnouncement, useScreenReaderAnnouncement } from "@/lib/hooks/use-screen-reader".Ignore the suggested diff — no change required.
Likely an incorrect or invalid review comment.
| return members.map((member) => ({ | ||
| memberId: member.id, | ||
| memberName: member.age ? `Member (Age ${member.age})` : "Member", | ||
| utilization: member.age ? calculateHealthcareUtilization(member) : null, | ||
| hasConditions: member.conditions && member.conditions.length > 0 && !member.conditions.includes("NONE"), | ||
| hasBasicData: Boolean(member.age && member.age.trim() !== '') | ||
| })) | ||
| }, [members]) | ||
|
|
||
| hasConditions: | ||
| member.conditions && | ||
| member.conditions.length > 0 && | ||
| !member.conditions.includes("NONE"), | ||
| hasBasicData: Boolean(member.age && member.age.trim() !== ""), | ||
| })); | ||
| }, [members]); |
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Avoid computing utilization for whitespace age; fix name label and “has conditions” logic.
- Utilization is computed when
member.ageis truthy, which includes whitespace-only strings. This can trigger invalid calculations. Align with yourhasBasicDatacheck (trimmed, non-empty). - Member label shows “Age …” for whitespace-only input.
hasConditionsshould detect any real condition even if “NONE” is present, usingsome(c !== "NONE").
Apply this diff:
- return members.map((member) => ({
- memberId: member.id,
- memberName: member.age ? `Member (Age ${member.age})` : "Member",
- utilization: member.age ? calculateHealthcareUtilization(member) : null,
- hasConditions:
- member.conditions &&
- member.conditions.length > 0 &&
- !member.conditions.includes("NONE"),
- hasBasicData: Boolean(member.age && member.age.trim() !== ""),
- }));
+ return members.map((member) => ({
+ memberId: member.id,
+ memberName:
+ member.age && member.age.trim() !== ""
+ ? `Member (Age ${member.age.trim()})`
+ : "Member",
+ utilization:
+ member.age && member.age.trim() !== ""
+ ? calculateHealthcareUtilization(member)
+ : null,
+ hasConditions:
+ Array.isArray(member.conditions) &&
+ member.conditions.some((c) => c !== "NONE"),
+ hasBasicData: Boolean(member.age && member.age.trim() !== ""),
+ }));📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| return members.map((member) => ({ | |
| memberId: member.id, | |
| memberName: member.age ? `Member (Age ${member.age})` : "Member", | |
| utilization: member.age ? calculateHealthcareUtilization(member) : null, | |
| hasConditions: member.conditions && member.conditions.length > 0 && !member.conditions.includes("NONE"), | |
| hasBasicData: Boolean(member.age && member.age.trim() !== '') | |
| })) | |
| }, [members]) | |
| hasConditions: | |
| member.conditions && | |
| member.conditions.length > 0 && | |
| !member.conditions.includes("NONE"), | |
| hasBasicData: Boolean(member.age && member.age.trim() !== ""), | |
| })); | |
| }, [members]); | |
| return members.map((member) => ({ | |
| memberId: member.id, | |
| memberName: | |
| member.age && member.age.trim() !== "" | |
| ? `Member (Age ${member.age.trim()})` | |
| : "Member", | |
| utilization: | |
| member.age && member.age.trim() !== "" | |
| ? calculateHealthcareUtilization(member) | |
| : null, | |
| hasConditions: | |
| Array.isArray(member.conditions) && | |
| member.conditions.some((c) => c !== "NONE"), | |
| hasBasicData: Boolean(member.age && member.age.trim() !== ""), | |
| })); | |
| }, [members]); |
🤖 Prompt for AI Agents
In app/health-profile/page.tsx around lines 41 to 51, sanitize and reuse a
trimmed age value and adjust condition logic: create a trimmedAge = member.age ?
member.age.trim() : "" and hasBasicData = trimmedAge.length > 0, use
hasBasicData (not raw member.age) to decide whether to compute utilization and
to include the "Age …" suffix in memberName, and change hasConditions to detect
any real condition by checking Array.isArray(member.conditions) &&
member.conditions.some(c => c !== "NONE").
| <Button | ||
| variant="outline" | ||
| onClick={() => | ||
| (window.location.href = "/utilization-model-test") | ||
| } | ||
| className="text-blue-600 hover:text-blue-700 border-blue-200 hover:border-blue-300" | ||
| > | ||
| Try our new model here | ||
| </Button> |
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🛠️ Refactor suggestion
Use Next.js client-side navigation instead of window.location.href.
Using window.location.href triggers a full page reload and loses SPA benefits. Prefer useRouter().push() for Next.js App Router.
Apply this diff:
+import { useRouter } from "next/navigation"; export default function HealthProfilePage() {
+ const router = useRouter();- <Button
- variant="outline"
- onClick={() =>
- (window.location.href = "/utilization-model-test")
- }
- className="text-blue-600 hover:text-blue-700 border-blue-200 hover:border-blue-300"
- >
+ <Button
+ variant="outline"
+ onClick={() => router.push("/utilization-model-test")}
+ className="text-blue-600 hover:text-blue-700 border-blue-200 hover:border-blue-300"
+ >
Try our new model here
</Button>Also applies to: 20-21, 23-23
🤖 Prompt for AI Agents
In app/health-profile/page.tsx around lines 186-194 (and also occurrences at
20-21 and 23), replace the direct window.location.href navigation with Next.js
client-side navigation: import and call useRouter from next/navigation and call
router.push("/utilization-model-test") in the Button onClick. Ensure the
component containing the Button is a client component (add "use client" at the
top) or move the Button into a small client component so useRouter can be used;
update imports accordingly and remove window.location.href usage.
…l Test page - Added new state management for healthcare utilization and member context. - Introduced functions to build member context and map prediction responses to healthcare utilization. - Updated the UI to display model predictions in a structured format. - Integrated UtilizationDisplay component to showcase detailed utilization data. - Improved layout and styling for better user experience.
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Actionable comments posted: 0
🧹 Nitpick comments (6)
app/utilization-model-test/utilization-predictions-and-risk.md (6)
10-12: Clarify enum/radio mappings and use TypeScript primitivenumber(notNumber)
- The enum/radio mappings are ambiguous (e.g., which numeric values correspond to male/female, regions, yes/no). This can cause mis-encoded inputs and inconsistent model behavior.
- In TS, prefer the
numberprimitive over theNumberobject.Suggested edits:
- - `gender`, `race_ethnicity`, `census_region`, `employment_status`: numeric enums (string → Number) + - `gender`, `race_ethnicity`, `census_region`, `employment_status`: numeric enums (string → number) - - `has_usual_source_of_care`, `difficulty_walking_stairs`, `any_activity_limitation`: 1/2 radio values → Number + - `has_usual_source_of_care`, `difficulty_walking_stairs`, `any_activity_limitation`: radio values → number (please document mapping, e.g., Yes=1 / No=0)Additionally, consider adding sub-bullets to enumerate the exact mapping tables for each enum (e.g., gender, race_ethnicity, census_region, employment_status) to avoid guesswork during integration and testing.
33-43: Avoid confusion around equipment_purchases in the grid vs. predictionsYou list Equipment Purchases in the grid labels, but later exclude it from predictions and totals. Make that contrast explicit right where the grid is introduced.
Suggested insertion:
- `equipment_purchases` → Equipment Purchases + +Note: Equipment purchases appear in the grid for visibility, but they are intentionally excluded from the `predictions` list and from `totalVisits`.
50-65: VerifybasedOn: "condition"is accurate for UtilizationPredictionThe predictions here are derived from model outputs, not a specific clinical condition. If the
UtilizationPredictiontype expects a controlled set of values (e.g., "condition", "model", "utilization", etc.), ensure"condition"is appropriate. If not, update the doc (and code) to the correct value to prevent downstream assumptions/filters from breaking.If the interface allows a limited set, align this string to it (e.g.,
basedOn: "model").
66-70: Prefer explicit exclusions over string matching semanticsThe note implies an implementation detail (“serviceType includes 'drug'”). If the intent is simply to exclude Rx and equipment from visit totals, name them explicitly to avoid ambiguity and future regressions in serviceType naming.
Suggested doc tweak:
- - Sum of `annualVisits` across predictions, excluding those whose `serviceType` includes "drug" (i.e., prescription drugs are excluded from visit totals). - - Equipment purchases are also excluded. + - Sum of `annualVisits` across predictions, excluding prescription drugs (`rx_fills`) and equipment purchases.
82-85: Resolve “gender” vs “sex” naming and mappingInputs list a
genderenum, but here you say thememberCtxis built from “age, sex, BMI”. Please clarify whethergenderis mapped tosex, and document the exact mapping so downstream risk logic interprets it correctly.Suggested addition:
- - The page builds a minimal `memberCtx` from entered age, sex, BMI; other fields (conditions, medications, lifestyle) default to empty/undefined. + - The page builds a minimal `memberCtx` from entered age, sex, BMI; other fields (conditions, medications, lifestyle) default to empty/undefined. + - Mapping note: `gender` input is mapped to `memberCtx.sex` with the following numeric mapping (document explicitly).
7-14: Clarify radio value semantics and edge cases for inputs
- For radio inputs using 1/2: specify which number corresponds to “Yes” and “No” (or consider boolean 0/1 to match typical modeling conventions).
- Confirm accepted ranges and units (e.g., BMI units, maximum family_size) and how out-of-range inputs are sanitized (clamping vs. rejection vs. null).
If sanitization is implemented in the API route, add a brief note linking to the code section that performs it.
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📒 Files selected for processing (2)
app/utilization-model-test/page.tsx(1 hunks)app/utilization-model-test/utilization-predictions-and-risk.md(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- app/utilization-model-test/page.tsx
🧰 Additional context used
🪛 LanguageTool
app/utilization-model-test/utilization-predictions-and-risk.md
[grammar] ~7-~7: There might be a mistake here.
Context: ...d sanitizes them before calling the API: - age_years_2022: 0–120, integer - bmi: 10–100, numb...
(QB_NEW_EN)
[grammar] ~8-~8: There might be a mistake here.
Context: ...PI: - age_years_2022: 0–120, integer - bmi: 10–100, number - gender, `race_eth...
(QB_NEW_EN)
[grammar] ~9-~9: There might be a mistake here.
Context: ...0–120, integer - bmi: 10–100, number - gender, race_ethnicity, census_region, `em...
(QB_NEW_EN)
[grammar] ~10-~10: There might be a mistake here.
Context: ...status: numeric enums (string → Number) - family_size: 0–100, integer - has_usual_source_o...
(QB_NEW_EN)
[grammar] ~11-~11: There might be a mistake here.
Context: ...umber) - family_size: 0–100, integer - has_usual_source_of_care, difficulty_walking_stairs, `any_acti...
(QB_NEW_EN)
[grammar] ~12-~12: There might be a mistake here.
Context: ...y_limitation: 1/2 radio values → Number - k6_distress_score`: 0–24, integer ### Model endpoints - ...
(QB_NEW_EN)
[grammar] ~17-~17: There might be a mistake here.
Context: ...r ### Model endpoints - Health check: GET /api/utilization-model/health - Predict: `POST /api/utilization-model/pr...
(QB_NEW_EN)
[grammar] ~57-~57: There might be a mistake here.
Context: ... - rx_fills → "Prescription drugs" - Severity per prediction (based on `annua...
(QB_NEW_EN)
[grammar] ~60-~60: There might be a mistake here.
Context: ...): - > 10→high -> 5→medium - else →low -reason`: "Model-predic...
(QB_NEW_EN)
[grammar] ~61-~61: There might be a mistake here.
Context: ...gh -> 5→medium - else →low -reason: "Model-predicted annual count" - ba...
(QB_NEW_EN)
[grammar] ~62-~62: There might be a mistake here.
Context: ...reason: "Model-predicted annual count" - basedOn: "condition" - Note: Equipment purc...
(QB_NEW_EN)
[grammar] ~63-~63: There might be a mistake here.
Context: ...-predicted annual count" - basedOn: "condition" - Note: Equipment purchases are not added ...
(QB_NEW_EN)
[grammar] ~90-~90: There might be a mistake here.
Context: ...ed age: +10 (age > 65) or +20 (age > 75) - Pediatric age (< 5): +10 - Chronic con...
(QB_NEW_EN)
[grammar] ~91-~91: There might be a mistake here.
Context: ... (age > 75) - Pediatric age (< 5): +10 - Chronic conditions (Diabetes, Heart Dise...
(QB_NEW_EN)
[grammar] ~92-~92: There might be a mistake here.
Context: ...ney Disease, Cancer): up to +45 combined - Multiple conditions (> 3): +15 - Polyp...
(QB_NEW_EN)
[grammar] ~93-~93: There might be a mistake here.
Context: ...bined - Multiple conditions (> 3): +15 - Polypharmacy (medications > 5): +10 - ...
(QB_NEW_EN)
[grammar] ~94-~94: There might be a mistake here.
Context: ... - Polypharmacy (medications > 5): +10 - Lifestyle: current smoker +15; heavy alc...
(QB_NEW_EN)
[grammar] ~95-~95: There might be a mistake here.
Context: ...12; sedentary (no exercise, age > 30) +8 - BMI: obesity (> 30) +10 or +15 (> 35); u...
(QB_NEW_EN)
[grammar] ~96-~96: There might be a mistake here.
Context: ...0 or +15 (> 35); underweight (< 18.5) +8 - Emergency risk (> 0.2): +20 - Pregnanc...
(QB_NEW_EN)
[grammar] ~97-~97: There might be a mistake here.
Context: ...18.5) +8 - Emergency risk (> 0.2): +20 - Pregnancy: normal +10; high-risk +25 -...
(QB_NEW_EN)
[grammar] ~98-~98: There might be a mistake here.
Context: ... - Pregnancy: normal +10; high-risk +25 - Mental health conditions (Depression/Anx...
(QB_NEW_EN)
[grammar] ~108-~108: There might be a mistake here.
Context: ...se → low - recommendations (examples) - High/critical: consider comprehensive PP...
(QB_NEW_EN)
🔇 Additional comments (3)
app/utilization-model-test/utilization-predictions-and-risk.md (3)
1-121: Solid, concise end-to-end documentation for the test pageClear mapping from model I/O to UI, with thresholds and derived metrics captured well. This will help keep proxy routes, UI, and the Python service aligned.
71-75: Emergency Risk Formula VerifiedThe implementation matches the doc’s formula and annualization intent:
- In app/utilization-model-test/page.tsx (lines 138–140):
const emergencyRisk = Math.min(1, (resp.er_visits + resp.inpatient_admits) / 12);- In lib/utilization-engine.ts (line 405):
emergencyRisk: Math.min(totalEmergencyRisk, 1.0)No changes needed.
88-100: Weightings matchcalculateRiskScoreimplementation
Reviewed the logic in lib/utilization-engine.ts – all documented scores align exactly with the code:
- Advanced Age: +10 (65 < age ≤ 75) / +20 (age > 75)
- Pediatric Age (< 5): +10
- Chronic Conditions: +15 each, capped at +45
- Multiple Conditions (> 3): +15
- Polypharmacy (> 5 medications): +10
- Lifestyle: Current Smoker +15; Heavy Alcohol +12; Sedentary (no exercise, age > 30) +8
- BMI: Obesity (> 30) +10 / (> 35) +15; Underweight (< 18.5) +8
- Emergency Risk (> 0.2): +20
- Pregnancy: Normal +10; High-Risk +25
- Mental Health (Depression/Anxiety/ADHD/Bipolar): +10 single / +15 multiple
No updates needed to the markdown.
Business Value: save time and money by using ML model
Next steps for MEPS model:
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