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783a952 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 | # ml_module/tools/data_tools.py
import re
from typing import Dict, Optional
import pandas as pd
from agno.tools import Toolkit, tool
from ml_module.services.storage_service import MLStorageService
from ml_module.services.project_service import ProjectService
from ml_module.core.constants import ArtifactTypes, DEFAULT_SAMPLE_ROWS, StoragePaths
from ml_module.core.response_formatter import (
FormattedResponse,
Severity,
make_text_response,
metric_block,
simple_table,
simple_table_with_types,
visualization_block,
text_block,
)
class DataAnalysisToolkit(Toolkit):
"""A collection of safe tools for performing data analysis."""
def __init__(
self,
storage_service: MLStorageService,
user_id: str,
project_id: str,
project_service: Optional[ProjectService] = None,
):
super().__init__(name="data_analysis_tools")
self.storage = storage_service
self.user_id = user_id
self.project_id = project_id
self.project_service = project_service
def _get_base_path(self) -> str:
return f"{self.user_id}/{self.project_id}"
def _extract_version_from_path(self, path: str) -> Optional[int]:
match = re.search(r"_v(\d+)", path)
if match:
try:
return int(match.group(1))
except ValueError:
return None
return None
def _resolve_raw_version(self, dataset_path: str, default: int = 1) -> int:
version = self._extract_version_from_path(dataset_path)
if version is None and self.project_service:
try:
version = self.project_service.get_latest_version(self.user_id, self.project_id, "raw")
except Exception:
version = default
return version or default
@tool
def get_data_summary(self, dataset_path: str) -> FormattedResponse:
"""
Calculates and saves a high-level summary of the dataset. This includes
shape (rows and columns), a list of column names, and data types for each column.
This should be the VERY FIRST tool you use to understand the dataset.
Args:
dataset_path (str): The full path to the dataset file within project storage.
Returns:
FormattedResponse: Structured dataset summary with artifact reference.
"""
try:
df = self.storage.load_dataframe(dataset_path)
summary = {
"shape": {"rows": df.shape[0], "columns": df.shape[1]},
"column_names": list(df.columns),
"column_data_types": {col: str(dtype) for col, dtype in df.dtypes.items()},
}
output_path = f"{self._get_base_path()}/analysis/data_profile.json"
info = self.storage.save_json(summary, output_path)
if self.project_service:
version = self._resolve_raw_version(dataset_path)
info.metadata.update({"columns": summary["column_names"]})
self.project_service.register_artifact(
self.user_id,
self.project_id,
ArtifactTypes.DATA_PROFILE,
version,
info,
version_scope="raw",
extra_metadata={"shape": summary["shape"]},
)
dtype_rows = [
{"column": col, "dtype": dtype}
for col, dtype in summary["column_data_types"].items()
]
blocks = [
metric_block("Rows", summary["shape"]["rows"]),
metric_block("Columns", summary["shape"]["columns"]),
simple_table_with_types(dtype_rows, caption="Column data types", block_id="column_dtypes"),
text_block(f"Saved summary to `{output_path}`"),
]
return FormattedResponse(
blocks=blocks,
summary="Data summary generated",
correlation_id=info.path,
done=True,
)
except Exception as e:
error_response = make_text_response(
f"Could not get data summary: {e}",
severity=Severity.ERROR,
)
error_response.done = True
return error_response
@tool
def get_missing_values_summary(self, dataset_path: str) -> FormattedResponse:
"""
Analyzes the dataset for missing (null or NaN) values in each column and saves a
report. This is a crucial step for assessing data quality.
Args:
dataset_path (str): The full path to the dataset file within project storage.
Returns:
FormattedResponse: Structured missing-value overview with artifact reference.
"""
try:
df = self.storage.load_dataframe(dataset_path)
missing_values = df.isnull().sum()
missing_summary = {
"total_missing_values": int(missing_values.sum()),
"missing_percentage": f"{(missing_values.sum() / (df.shape[0] * df.shape[1])):.2%}",
"missing_values_per_column": {
col: int(count) for col, count in missing_values.items() if count > 0
}
}
output_path = f"{self._get_base_path()}/analysis/missing_values_report.json"
info = self.storage.save_json(missing_summary, output_path)
if self.project_service:
version = self._resolve_raw_version(dataset_path)
info.metadata.update({"columns_with_missing": list(missing_summary["missing_values_per_column"].keys())})
self.project_service.register_artifact(
self.user_id,
self.project_id,
ArtifactTypes.MISSING_VALUES,
version,
info,
version_scope="raw",
extra_metadata={
"total_missing": missing_summary["total_missing_values"],
"missing_percentage": missing_summary["missing_percentage"],
},
)
columns_with_missing = list(missing_summary["missing_values_per_column"].keys())
table_rows = [
{"column": col, "missing": count}
for col, count in missing_summary["missing_values_per_column"].items()
]
blocks = [
metric_block(
"Total Missing",
missing_summary["total_missing_values"],
unit="cells",
),
text_block(
f"Overall missing percentage: {missing_summary['missing_percentage']}",
severity=Severity.INFO,
),
simple_table(table_rows, caption="Missing values per column", block_id="missing_values"),
text_block(f"Saved missing-values report to `{output_path}`"),
]
summary_text = (
"No missing values detected"
if not columns_with_missing
else f"Missing values recorded for {len(columns_with_missing)} columns"
)
return FormattedResponse(
blocks=blocks,
summary=summary_text,
correlation_id=info.path,
done=True,
)
except Exception as e:
error_response = make_text_response(
f"Could not analyze missing values: {e}",
severity=Severity.ERROR,
)
error_response.done = True
return error_response
@tool
def save_sample_head(
self,
dataset_path: str,
limit: Optional[int] = None,
version: Optional[int] = None,
) -> FormattedResponse:
"""
Saves the first N rows of the dataset as a JSON file for UI preview.
Includes both the data sample and schema information.
Args:
dataset_path (str): The full path to the dataset file within project storage.
limit (Optional[int]): Number of rows to sample (defaults to DEFAULT_SAMPLE_ROWS).
Returns:
FormattedResponse: Structured sample preview with artifact reference.
"""
try:
df = self.storage.load_dataframe(dataset_path)
rows_to_sample = limit or DEFAULT_SAMPLE_ROWS
resolved_version = version or self._resolve_raw_version(dataset_path)
# Get sample data (first N rows)
sample_df = df.head(rows_to_sample)
# Create comprehensive sample data structure
sample_data = {
"dataset_info": {
"total_rows": len(df),
"total_columns": len(df.columns),
"sample_rows": len(sample_df),
"source_path": dataset_path
},
"schema": {
"columns": list(df.columns),
"dtypes": {col: str(dtype) for col, dtype in df.dtypes.items()},
"null_counts": {col: int(count) for col, count in df.isnull().sum().items()}
},
"sample_data": {
"columns": list(sample_df.columns),
"rows": sample_df.to_dict(orient="records")
}
}
# Use versioned path from constants
output_path = StoragePaths.SAMPLE_RAW_HEAD.format(
user_id=self.user_id,
project_id=self.project_id,
version=resolved_version
)
info = self.storage.save_json(sample_data, output_path)
if self.project_service:
info.metadata.update({
"sample_rows": sample_data["dataset_info"].get("sample_rows"),
"total_rows": sample_data["dataset_info"].get("total_rows"),
})
self.project_service.register_artifact(
self.user_id,
self.project_id,
ArtifactTypes.SAMPLE_RAW_HEAD,
resolved_version,
info,
version_scope="raw",
extra_metadata={
"columns": sample_data["schema"].get("columns", []),
},
)
preview_rows = sample_df.head(min(rows_to_sample, 10)).to_dict(orient="records")
blocks = [
metric_block("Rows Sampled", len(sample_df)),
metric_block("Total Rows", len(df)),
simple_table_with_types(preview_rows, caption="First rows preview", block_id="sample_preview"),
text_block(f"Sample saved to `{output_path}`"),
]
return FormattedResponse(
blocks=blocks,
summary=f"Saved sample head (first {len(sample_df)} rows)",
correlation_id=info.path,
done=True,
)
except Exception as e:
error_response = make_text_response(
f"Could not save dataset sample: {e}",
severity=Severity.ERROR,
)
error_response.done = True
return error_response |