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Mar 12, 2024
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GSK-2604 GSK-2766 Renamed tittle of wage classification notebook #1841

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GSK-2604 Renamed tittle of wage classification notebook
kevinmessiaen Mar 12, 2024
6f20371
GSK-2766 Removed reference to dataprocessor since it's only internall…
kevinmessiaen Mar 12, 2024
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23 changes: 13 additions & 10 deletions docs/reference/notebooks/wage_classification.ipynb
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Expand Up @@ -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",
Expand Down Expand Up @@ -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"
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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)"
]
},
Expand Down Expand Up @@ -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",
")"
]
},
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand Down
2 changes: 0 additions & 2 deletions giskard/datasets/base/__init__.py
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Expand Up @@ -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]
Expand Down