Update validateDataset.py to show errors on uploads more user-friendly

lab/pyutils/validateDataset.py

@@ -54,16 +54,63 @@
 MIN_ROW_PER_CLASS = 2
 
 
+def check_dataframe(df, target_column):
+    '''
+    check_dataframe function checks whether each column contains only numeric data or not.
+    missing values are not allowed in df.
+    strings are not allowed in df.
+    inf or -inf are not allowed in df.
+    '''
+
+    error_message = "Found error in data:"
+
+    # find columns contain missing value(NaN) in df
+    nan_cols = df.columns[df.isnull().any()].tolist()
+    if len(nan_cols) > 0:
+        error_message += "* 'MISSING VALUE' in " + \
+            str(nan_cols) + ""
+
+    df_non_target = df.drop(columns=target_column, axis=1)
+    inf_cols_list = []
+
+    # find which features contain infinity or -infinity in df_non_target
+
+    # find columns whose data type is object
+    # object dtype for storing strings in pandas
+    # if a column contains both string and numeric data, its dtype is object.
+    str_cols = df.columns[df.dtypes == object].tolist()
+
+    non_str_cols = df_non_target.columns.difference(str_cols)
+
+    for col in non_str_cols:
+
+        if np.isinf(df[col]).any():
+            inf_cols_list.append(col)
+
+    if len(inf_cols_list) > 0:
+        error_message += "* '+INFINITY or -INFINITY' in " + \
+            str(inf_cols_list) + "  "
+
+    # str_trigger = False
+    if len(str_cols) > 0:
+        error_message += "* 'STRING' in " + \
+            str(str_cols)+" "
+
+    return error_message
+
+
 def validate_data_from_server(file_id, prediction_type, target_field, categories=None, ordinals=None, **kwargs):
     # Read the data set into memory
     raw_data = get_file_from_server(file_id)
     df = pd.read_csv(StringIO(raw_data), sep=None, engine='python', **kwargs)
-    return validate_data(df, prediction_type, target_field, categories, ordinals)
+    # return validate_data(df, prediction_type, target_field, categories, ordinals)
+    return validate_data_updated(df, prediction_type, target_field, categories, ordinals)
 
 
 def validate_data_from_filepath(file_id, prediction_type, target_field, categories=None, ordinals=None, **kwargs):
     # Read the data set into memory
     df = pd.read_csv(file_id, sep=None, engine='python', **kwargs)
+    # print("dfprint", df)
     return validate_data(df, prediction_type, target_field, categories, ordinals)
 
 
@@ -190,6 +237,87 @@ def validate_data(df, prediction_type="classification", target_column=None, cate
     return True, None
 
 
+def validate_data_updated(df, prediction_type="classification", target_column=None, categories=None, ordinals=None):
+    '''
+    Check that a df is valid
+    This function checks for the following:
+        - prediction_type is valid
+        - number of rows and columns is valid
+        - target column is valid
+        - missing values in df.
+        - strings in df.
+        - inf or -inf in df.
+
+
+
+    @return tuple
+        boolean - validation result
+        string  - message
+    '''
+
+    # check prediction type is valid
+    if prediction_type not in ["classification", "regression"]:
+        logger.warn(f"Invalid prediction type: '{prediction_type}'")
+        return False, f"Invalid prediction type: '{prediction_type}'"
+
+    # check the number of rows and columns is valid
+    if df.shape[0] < MIN_ROWS:
+        logger.warn("Dataset has dimensions {}, classification datasets must have at least {} rows.".format(
+            df.shape, MIN_ROWS))
+        return False, "Dataset has dimensions {}, classification datasets must have at least {} rows.".format(df.shape, MIN_ROWS)
+
+    # check the number of columns is valid
+    if df.shape[1] < MIN_COLS:
+        logger.warn("Dataset has dimensions {}, classification datasets must have at least {} columns.".format(
+            df.shape, MIN_COLS))
+        return False, "Dataset has dimensions {}, classification datasets must have at least {} columns.".format(df.shape, MIN_COLS)
+
+    # target column validation
+    if (target_column != None):
+        if not (target_column in df.columns):
+            logger.warn("Target column '" + target_column + "' not in data")
+            return False, "Target column '" + target_column + "' not in data"
+        if categories and target_column in categories:
+            logger.warn("Target column '" + target_column +
+                        "' cannot be a categorical feature")
+            return False, "Target column '" + target_column + "' cannot be a categorical feature"
+        if ordinals and target_column in ordinals:
+            logger.warn("Target column '" + target_column +
+                        "' cannot be an ordinal feature")
+            return False, "Target column '" + target_column + "' cannot be an ordinal feature"
+
+    # check only check if target is specified
+    if target_column:
+
+        # classification
+        if (prediction_type == "classification"):
+            # target column of classification problem does not need to be numeric
+            df_non_target = df.drop(columns=target_column, axis=1)
+
+            # Check rows per class
+            counts = df.groupby(target_column).count()
+            fails_validation = counts[counts[counts.columns[1]]
+                                      < MIN_ROW_PER_CLASS]
+            if (not fails_validation.empty):
+                msg = "Classification datasets must have at least 2 rows per class, class(es) '{}' have only 1 row.".format(
+                    list(fails_validation.index.values))
+                logger.warn(msg)
+                return False, msg
+
+    # In the below code,the check_dataframe() checks whether features and target column contain only processed data.
+    # check whether each column contains only processed data or not
+    # missing values are not allowed in df
+    # strings are not allowed in df
+    # inf or -inf are not allowed in df
+    if (len(df.columns)) > 0:
+        error_message = check_dataframe(df, target_column)
+        if error_message != "Found error in data:":
+            logger.warn(str(error_message))
+            return False, str(error_message)
+
+    return True, None
+
+
 def get_file_from_server(file_id):
     '''
     Retrieve a file from the main Aliro server
@@ -262,8 +390,6 @@ def main():
         if args.JSON_ORDINALS:
             ordinals = simplejson.loads(args.JSON_ORDINALS)
         prediction_type = args.PREDICTION_TYPE
-        # print("categories: ")
-        # print(categories)
 
         if (args.IDENTIFIER_TYPE == 'filepath'):
             success, errorMessage = validate_data_from_filepath(

lab/webapp/src/components/FileUpload/index.js

@@ -2448,6 +2448,7 @@ handleCatFeaturesUserTextCancel() {
         <Modal.Content>{this.state.errorModalContent}</Modal.Content>
 
 
+
       </Modal>
       )
     }