Test/add testing cases

.gitignore

@@ -11,3 +11,4 @@ schemas.json
 *_metrics.json
 *_recommendations.json
 *_schemas.json
+*.log

accuracy_pack/main.py

@@ -11,37 +11,37 @@
     print("No float columns found. metrics.json will not be created.")
     raise
 
-total_proportion_score = 0  # Initialize total proportion score
+total_proportion_score = 0  # Initialize total proportion score used for original mean
 valid_columns_count = 0  # Count of columns that have at least one non-NaN value
 
+float_total_proportion_score = 0  # Initialize total proportion score for float_mean
+valid_points_count = 0  # Total count of valid data points (non-NaN) across all valid columns
+
 for column in float_columns:
     column_data = pack.df_source[column].dropna()
-
-    # Skip the column if it only contains NaN values
+
     if column_data.empty:
         continue
-
+
+    valid_data_points = len(column_data)  # Number of non-NaN data points in the current column
     decimals_count = column_data.apply(
         lambda x: len(str(x).split(".")[1]) if "." in str(x) else 0
     )
     max_decimals = decimals_count.max()
     most_common_decimals_series = decimals_count.mode()
-
-    # Handle the scenario when the mode() returns an empty series
+
     if most_common_decimals_series.empty:
-        print(f"No common decimal count found for column {column}.")
-        most_common_decimals = 0
         proportion_score = 0
     else:
-        most_common_decimals = most_common_decimals_series[
-            0
-        ]  # Get the most common decimals count
-        proportion_score = decimals_count[
-            decimals_count == most_common_decimals
-        ].count() / len(decimals_count)
-
-    total_proportion_score += proportion_score  # Add proportion score to the total
+        most_common_decimals = most_common_decimals_series[0]
+        proportion_score = decimals_count[decimals_count == most_common_decimals].count() / valid_data_points
+
+    total_proportion_score += proportion_score  # For original mean calculation
     valid_columns_count += 1  # Increment valid columns count
+
+    # For float_mean calculation:
+    float_total_proportion_score += proportion_score * valid_data_points  
+    valid_points_count += valid_data_points  
 
     if max_decimals > 0:
         pack.metrics.data.append(
@@ -68,11 +68,21 @@
     total_proportion_score / valid_columns_count if valid_columns_count > 0 else 0
 )
 
-# Add the mean proportion score to the precision data
+# Calculate the float mean proportion score considering all data points accurately
+float_mean_proportion_score = float_total_proportion_score / valid_points_count if valid_points_count > 0 else 0
+
+pack.metrics.data.append(
+    {
+        "key": "float_score", 
+        "value": str(round(float_mean_proportion_score, 2)),
+        "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
+    }
+)
+
 pack.metrics.data.append(
     {
         "key": "score",
-        "value": str(round(mean_proportion_score, 2)),  # Mean proportion score
+        "value": str(round(mean_proportion_score, 2)),
         "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
     }
 )

data/hepatitis-c/source_conf.json

@@ -0,0 +1,11 @@
+{
+    "config": {
+        "path": "../data/hepatitis-c/HepatitisCdata.csv"
+    },
+    "description": "Laboratory values of blood donors and Hepatitis C patients",
+    "name": "Hepatitis C Prediction Dataset",
+    "reference": false,
+    "sensitive": false,
+    "type": "file",
+    "visibility": "public"
+}

data_compare_pack/main.py

@@ -120,7 +120,7 @@
     pack.metrics.data.append(
         {
             "key": "score",
-            "value": score,
+            "value": str(round(score, 2)),
             "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
         }
     )

duplicates_finder_pack/main.py

@@ -10,6 +10,7 @@
 if (
     "job" in pack.pack_config
     and "compute_uniqueness_columns" in pack.pack_config["job"]
+    and len(pack.pack_config["job"]["compute_uniqueness_columns"]) > 0
 ):
     uniqueness_columns = pack.pack_config["job"]["compute_uniqueness_columns"]
 else:
@@ -19,11 +20,16 @@
 
 # Step 1: Filter the DataFrame based on the specified columns
 print("Columns used for checking duplicates:", uniqueness_columns)
-df_subset = pack.df_source[uniqueness_columns]
+df_subset = pack.df_source[uniqueness_columns].copy()
+duplicates = df_subset.duplicated()
 total_rows = len(pack.df_source)
 
+print("total rows "+str(total_rows))
+
 # Step 2: Calculate the number of duplicate rows based on this subset
-total_duplicates = df_subset.duplicated().sum()
+total_duplicates = duplicates.sum()
+
+print("total duplicates "+str(total_duplicates))
 
 # Calculate the scoped duplication score
 duplication_score = round(total_duplicates / total_rows if total_rows > 0 else 0, 2)
@@ -35,7 +41,7 @@
 pack.metrics.data.append(
     {
         "key": "score",
-        "value": score,
+        "value": str(round(score, 2)),
         "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
     }
 )
@@ -69,7 +75,7 @@
 if score < 0.9:
 
     recommendation = {
-        "content": f"dataset '{pack.source_config['name']}' has a duplication rate of {duplication_score*100}%. on the scope {uniqueness_columns} .",
+        "content": f"dataset '{pack.source_config['name']}' has a duplication rate of {duplication_score*100}%. on the scope {uniqueness_columns.to_list()} .",
         "type": "Duplicates",
         "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
         "level": determine_recommendation_level(duplication_score),
@@ -84,25 +90,13 @@
 # Step 1: Retrieve 'id_columns' from pack_config
 id_columns = pack.pack_config.get("job", {}).get("id_columns", [])
 
-# Check if uniqueness_columns is empty and handle accordingly
-if not uniqueness_columns:
-    print("No columns specified for checking duplicates. Using all columns.")
-    uniqueness_columns = (
-        pack.df_source.columns.tolist()
-    )  # Use all columns if none are specified
-
 # Step 2: Identify duplicated rows
-duplicated_rows = pack.df_source[
-    pack.df_source.duplicated(subset=uniqueness_columns, keep=False)
-]
+duplicated_rows = pack.df_source[duplicates]
 
 # Check if there are any duplicates
-if duplicated_rows.empty:
+if duplicates.empty:
     print("No duplicates found. No report will be generated.")
 else:
-    # If there are duplicates, proceed with sorting and exporting
-    duplicated_rows = duplicated_rows.sort_values(by=uniqueness_columns)
-
     # Step 3: Set index or create 'index' column for the Excel export
     if id_columns:
         # Ensure all id_columns are in the DataFrame columns

duplicates_finder_pack/properties.yaml

@@ -3,5 +3,5 @@ icon: icon.png
 name: duplicates_finder
 type: uniqueness
 url: https://github.com/qalita-io/packs/tree/main/duplicates_finder_pack
-version: 2.0.0
+version: 2.0.1
 visibility: public

outlier_detection_pack/main.py

@@ -1,5 +1,15 @@
 from qalita_core.pack import Pack
-from qalita_core.utils import determine_recommendation_level
+
+
+# Define a function to determine recommendation level based on the proportion of outliers
+def determine_recommendation_level(proportion_outliers):
+    if proportion_outliers > 0.5:  # More than 50% of data are outliers
+        return "high"
+    elif proportion_outliers > 0.3:  # More than 30% of data are outliers
+        return "warning"
+    else:
+        return "info"
+
 
 import os
 import numpy as np
@@ -14,7 +24,9 @@
 # Fill missing values with mean
 for column in pack.df_source.columns:
     if np.issubdtype(pack.df_source[column].dtype, np.number):
-        pack.df_source[column] = pack.df_source[column].fillna(pack.df_source[column].mean())
+        pack.df_source[column] = pack.df_source[column].fillna(
+            pack.df_source[column].mean()
+        )
 
 # Identify columns that still contain NaN values after filling
 columns_with_nan = [
@@ -65,7 +77,7 @@
         )
 
         # Identify outliers based on the inlier score and threshold
-        outliers = pack.df_source[[column]][inlier_score < outlier_threshold]
+        outliers = pack.df_source[[column]][inlier_score < outlier_threshold].copy()
         univariate_outliers[column] = outliers
 
         outlier_count = len(outliers)
@@ -77,6 +89,23 @@
             }
         )
 
+        if outlier_count > 0:
+            pack.recommendations.data.append(
+                {
+                    "content": f"Column '{column}' has {outlier_count} outliers.",
+                    "type": "Outliers",
+                    "scope": {"perimeter": "column", "value": column},
+                    "level": determine_recommendation_level(
+                        outlier_count / len(pack.df_source[[column]])
+                    ),
+                }
+            )
+
+
+total_univariate_outliers = sum(
+    len(outliers) for outliers in univariate_outliers.values()
+)
+
 # Identify non-numeric columns
 non_numeric_columns = pack.df_source.select_dtypes(exclude=[np.number]).columns
 
@@ -129,13 +158,25 @@
     pack.metrics.data.append(
         {
             "key": "score",
-            "value": round(inlier_score.mean().item(), 2),
+            "value": str(round(inlier_score.mean().item(), 2)),
             "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
         }
     )
 except ValueError as e:
     print(f"Error fitting the model: {e}")
 
+
+total_multivariate_outliers = len(multivariate_outliers)
+# total_outliers_count = total_univariate_outliers + total_multivariate_outliers
+total_outliers_count = total_univariate_outliers
+pack.metrics.data.append(
+    {
+        "key": "total_outliers_count",
+        "value": total_outliers_count,
+        "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
+    }
+)
+
 pack.metrics.save()
 
 # Define a threshold for considering a data point as an outlier
@@ -180,35 +221,60 @@
         "content": f"The dataset '{pack.source_config['name']}' has a normality score of {dataset_normality_score*100}%.",
         "type": "Outliers",
         "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
-        "level": determine_recommendation_level(
-            1 - dataset_normality_score
-        ),  # Convert percentage to proportion
+        "level": determine_recommendation_level(1 - dataset_normality_score),
     }
     pack.recommendations.data.append(recommendation)
 
+pack.recommendations.data.append(
+    {
+        "content": f"The dataset '{pack.source_config['name']}' has a total of {total_outliers_count} outliers. Check them in output file.",
+        "type": "Outliers",
+        "scope": {"perimeter": "dataset", "value": pack.source_config["name"]},
+        "level": determine_recommendation_level(
+            total_outliers_count / len(pack.df_source)
+        ),
+    }
+)
+
 pack.recommendations.save()
 ####################### Export
 
 # Step 1: Compile Univariate Outliers
 all_univariate_outliers = pd.DataFrame()
 for column, outliers in univariate_outliers.items():
+    # Select only the rows that are outliers, preserving the index
     outliers_with_id = pack.df_source.loc[outliers.index, id_columns + [column]].copy()
+    # Create a new 'value' column to store the outlier values
+    outliers_with_id["value"] = outliers_with_id[column]
+    # Add column name as 'OutlierAttribute'
     outliers_with_id["OutlierAttribute"] = column
-    outliers_with_id["index"] = outliers_with_id.index  # Capture the original index
+    # Optionally, if you want to keep the original index as a column
+    outliers_with_id["index"] = outliers_with_id.index
+    # Drop the original value columns as we have captured it in 'value'
+    outliers_with_id = outliers_with_id[
+        id_columns + ["index", "OutlierAttribute", "value"]
+    ]
+    # Concatenate to the all_univariate_outliers DataFrame
     all_univariate_outliers = pd.concat(
         [all_univariate_outliers, outliers_with_id], ignore_index=True
     )
 
+# Step 1: Compile Univariate Outliers
+all_univariate_outliers_simple = pd.DataFrame()
+for column, outliers in univariate_outliers.items():
+    outliers_with_id = pack.df_source.loc[outliers.index, id_columns + [column]].copy()
+    outliers_with_id["OutlierAttribute"] = column
+    outliers_with_id["index"] = outliers_with_id.index
+    all_univariate_outliers_simple = pd.concat(
+        [all_univariate_outliers_simple, outliers_with_id], ignore_index=True
+    )
+
 # Rearrange columns for all_univariate_outliers
+# Ensure the 'value' column is correctly placed if needed here
 id_and_other_columns = (
     ["index"]
     + id_columns
-    + ["OutlierAttribute"]
-    + [
-        col
-        for col in all_univariate_outliers.columns
-        if col not in ["index"] + id_columns + ["OutlierAttribute"]
-    ]
+    + ["OutlierAttribute", "value"]  # Include "value" in the list
 )
 all_univariate_outliers = all_univariate_outliers[id_and_other_columns]
 
@@ -234,7 +300,7 @@
 
 # Step 3: Combine Data
 all_outliers = pd.concat(
-    [all_univariate_outliers, multivariate_outliers], ignore_index=True
+    [all_univariate_outliers_simple, multivariate_outliers], ignore_index=True
 )
 
 # Ensure that all_outliers has the same column order
@@ -261,13 +327,3 @@
         all_outliers.to_excel(writer, sheet_name="All Outliers", index=False)
 
     print(f"Outliers report saved to {excel_file_path}")
-
-
-# Define a function to determine recommendation level based on the proportion of outliers
-def determine_recommendation_level(proportion_outliers):
-    if proportion_outliers > 0.5:  # More than 20% of data are outliers
-        return "high"
-    elif proportion_outliers > 0.3:  # More than 10% of data are outliers
-        return "warning"
-    else:
-        return "info"

outlier_detection_pack/pack_conf.json

@@ -14,6 +14,12 @@
                 "chart_type": "text",
                 "display_title": true,
                 "justify": true
+            },
+            {
+                "metric_key": "total_outliers_count",
+                "chart_type": "text",
+                "display_title": true,
+                "justify": true
             }
         ],
         "scoped": [
@@ -37,4 +43,4 @@
             }
         ]
     }
-}
+}

outlier_detection_pack/properties.yaml

@@ -3,5 +3,5 @@ icon: icon.png
 name: outlier_detection
 type: reasonability
 url: https://github.com/qalita-io/packs/tree/main/outlier_detection_pack
-version: 2.0.1
+version: 2.0.2
 visibility: public