Merge pull request #1841 from Giskard-AI/GSK-2604

GSK-2604 GSK-2766 Renamed tittle of wage classification notebook

Author: luca-martial

docs/reference/notebooks/wage_classification.ipynb

@@ -6,7 +6,7 @@
     "collapsed": false
    },
    "source": [
-    "# German credit scoring [scikit-learn]\n",
+    "# Wage classification [scikit-learn]\n",
     "\n",
     "Giskard is an open-source framework for testing all ML models, from LLMs to tabular models. Don’t hesitate to give the project a [star on GitHub](https://github.com/Giskard-AI/giskard) ⭐️ if you find it useful!\n",
     "\n",
@@ -75,11 +75,11 @@
     "from urllib.request import urlretrieve\n",
     "\n",
     "import pandas as pd\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.metrics import accuracy_score\n",
     "from sklearn.compose import ColumnTransformer\n",
     "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
     "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.pipeline import Pipeline\n",
     "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
     "\n",
     "from giskard import Model, Dataset, scan, testing, GiskardClient, Suite"
@@ -111,8 +111,8 @@
     "TEST_RATIO = 0.2\n",
     "\n",
     "DROP_FEATURES = [\n",
-    "    'education', \n",
-    "    'native-country', \n",
+    "    'education',\n",
+    "    'native-country',\n",
     "    'occupation',\n",
     "    'marital-status',\n",
     "    'educational-num'\n",
@@ -229,7 +229,7 @@
    },
    "outputs": [],
    "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(income_df.drop(columns=TARGET_COLUMN), income_df[TARGET_COLUMN], \n",
+    "X_train, X_test, y_train, y_test = train_test_split(income_df.drop(columns=TARGET_COLUMN), income_df[TARGET_COLUMN],\n",
     "                                                    test_size=TEST_RATIO, random_state=RANDOM_SEED)"
    ]
   },
@@ -257,10 +257,12 @@
    "source": [
     "raw_data = pd.concat([X_test, y_test], axis=1)\n",
     "giskard_dataset = Dataset(\n",
-    "    df=raw_data,  # A pandas.DataFrame that contains the raw data (before all the pre-processing steps) and the actual ground truth variable (target).\n",
+    "    df=raw_data,\n",
+    "    # A pandas.DataFrame that contains the raw data (before all the pre-processing steps) and the actual ground truth variable (target).\n",
     "    target=TARGET_COLUMN,  # Ground truth variable.\n",
     "    name=\"salary_data\",  # Optional.\n",
-    "    cat_columns=CATEGORICAL_FEATURES  # List of categorical columns. Optional, but is a MUST if available. Inferred automatically if not.\n",
+    "    cat_columns=CATEGORICAL_FEATURES\n",
+    "    # List of categorical columns. Optional, but is a MUST if available. Inferred automatically if not.\n",
     ")"
    ]
   },
@@ -351,7 +353,8 @@
    "outputs": [],
    "source": [
     "giskard_model = Model(\n",
-    "    model=pipeline,  # A prediction function that encapsulates all the data pre-processing steps and that could be executed with the dataset used by the scan.\n",
+    "    model=pipeline,\n",
+    "    # A prediction function that encapsulates all the data pre-processing steps and that could be executed with the dataset used by the scan.\n",
     "    model_type=\"classification\",  # Either regression, classification or text_generation.\n",
     "    name=\"salary_cls\",  # Optional.\n",
     "    classification_labels=pipeline.classes_,  # Their order MUST be identical to the prediction_function's output order.\n",
@@ -618,7 +621,7 @@
    "outputs": [],
    "source": [
     "# Create a Giskard client after having install the Giskard server (see documentation)\n",
-    "api_key = \"<Giskard API key>\" #This can be found in the Settings tab of the Giskard hub\n",
+    "api_key = \"<Giskard API key>\"  #This can be found in the Settings tab of the Giskard hub\n",
     "#hf_token = \"<Your Giskard Space token>\" #If the Giskard Hub is installed on HF Space, this can be found on the Settings tab of the Giskard Hub\n",
     "\n",
     "client = GiskardClient(\n",

giskard/datasets/base/__init__.py

@@ -143,8 +143,6 @@ class Dataset(ColumnMetadataMixin):
         column_types (Optional[Dict[str, str]]):
             A dictionary of column names and their types (numeric, category or text) for all columns of df. If not provided,
             the categorical columns will be automatically inferred.
-        data_processor (DataProcessor):
-            An instance of the `DataProcessor` class used for data processing.
     """
 
     name: Optional[str]