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import pandas as pd |
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import numpy as np |
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import lightgbm as lgb |
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from lightgbm.callback import early_stopping |
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from sklearn.ensemble import GradientBoostingRegressor |
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from sklearn.model_selection import train_test_split |
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from sklearn.metrics import mean_squared_error |
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from sklearn.metrics import r2_score |
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from sklearn.metrics import accuracy_score |
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train_data = pd.read_csv("train.csv") |
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test_data = pd.read_csv("test.csv") |
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cleanup_cats = {"MSZoning": {"A": 1, "C (all)": 2, "FV": 3, "I": 4, "RH": 5, "RL": 6, "RP": 7, "RM": 0}, |
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"Street": {"Grvl": 1, "Pave": 0}, |
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"Alley" : {"NA": 1, "Grvl": 2, "Pave": 0}, |
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"LotShape": {"Reg": 1, "IR1": 2, "IR2": 3, "IR3": 0}, |
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"LandContour": {"Lvl": 1, "Bnk": 2, "HLS": 3, "Low": 0}, |
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"Utilities": {"AllPub": 0, "NoSewr": 3, "NoSeWa" : 2, "ELO": 1}, |
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"LotConfig": {"Inside": 1, "Corner": 2, "CulDSac": 3, "FR2": 4, "FR3": 0}, |
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"LandSlope": {"Gtl": 1, "Mod": 2, "Sev": 0}, |
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"Neighborhood": {"Blmngtn": 1, "Blueste": 2, "BrDale": 3, "BrkSide": 4, "ClearCr": 5, "CollgCr": 6, "Crawfor": 7, "Edwards" : 8, "Gilbert": 9, "IDOTRR": 10, "MeadowV": 11, "Mitchel": 12, "NAmes": 13, "NoRidge": 14, "NPkVill": 15, "NridgHt": 16, "NWAmes": 17, "OldTown": 18, "SWISU": 19, "Sawyer": 20, "SawyerW": 21, "Somerst": 22, "StoneBr": 23, "Timber": 24, "Veenker": 0}, |
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"Condition1": {"Artery": 1, "Feedr": 2, "Norm": 3, "RRNn": 4, "RRAn": 5, "PosN": 6, "PosA": 7, "RRNe": 8, "RRAe": 0}, |
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"Condition2": {"Artery": 1, "Feedr": 2, "Norm": 3, "RRNn": 4, "RRAn": 5, "PosN": 6, "PosA": 7, "RRNe": 8, "RRAe": 0}, |
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"BldgType": {"1Fam": 1, "2fmCon": 2, "Duplex": 3, "TwnhsE": 4, "TwnhsI": 5, "Twnhs": 0}, |
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"HouseStyle": {"1Story": 1, "1.5Fin": 2, "1.5Unf": 3, "2Story": 4, "2.5Fin": 5, "2.5Unf": 6, "SFoyer": 7, "SLvl": 0}, |
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"RoofStyle": {"Flat": 1, "Gable": 2, "Gambrel": 3, "Hip": 4, "Mansard": 5, "Shed": 0}, |
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"RoofMatl": {"ClyTile": 1, "CompShg": 2, "Membran": 3, "Metal": 4, "Roll": 5, "Tar&Grv": 6, "WdShake": 7, "WdShngl": 0}, |
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"Exterior1st": {"AsbShng": 1, "AsphShn": 2, "BrkComm": 3, "BrkFace": 4, "CBlock": 5, "CemntBd": 6, "HdBoard": 7, "ImStucc": 8, "MetalSd": 9, "Other": 10, "Plywood": 11, "Precast": 12, "Stone": 13, "Stucco": 14, "VinylSd": 15, "WdShing": 16, "Wd Sdng": 0}, |
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"Exterior2nd": {"AsbShng": 1, "AsphShn": 2, "Brk Cmn": 3, "BrkFace": 4, "CBlock": 5, "CmentBd": 6, "HdBoard": 7, "ImStucc": 8, "MetalSd": 9, "Other": 10, "Plywood": 11, "Precast": 12, "Stone": 13, "Stucco": 14, "VinylSd": 15, "Wd Shng": 16, "Wd Sdng": 0}, |
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"MasVnrType": {"None": 1, "BrkCmn": 2, "BrkFace": 3, "CBlock": 4, "Stone": 0}, |
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"ExterQual": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 0}, |
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"ExterCond": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 0}, |
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"Foundation": {"BrkTil": 1, "CBlock": 2, "PConc": 3, "Slab": 4, "Stone": 5, "Wood": 0}, |
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"BsmtQual": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5, "NA": 0}, |
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"BsmtCond": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5, "NA": 0}, |
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"BsmtExposure": {"NA": 1, "No": 2, "Mn": 3, "Av": 4, "Gd": 0}, |
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"BsmtFinType1": {"GLQ": 1, "ALQ": 2, "BLQ": 3, "Rec": 4, "LwQ": 5, "Unf": 6, "NA": 0}, |
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"BsmtFinType2": {"GLQ": 1, "ALQ": 2, "BLQ": 3, "Rec": 4, "LwQ": 5, "Unf": 6, "NA": 0}, |
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"Heating": {"Floor": 1, "GasA": 2, "GasW": 3, "Grav": 4, "OthW": 5, "Wall": 0}, |
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"HeatingQC": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 0}, |
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"CentralAir": {"N": 0, "Y": 1}, |
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"Electrical": {"SBrkr": 1, "FuseA": 2, "FuseF": 3, "FuseP": 4, "Mix": 0}, |
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"KitchenQual": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 0}, |
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"Functional": {"Typ": 1, "Min1": 2, "Min2": 3, "Mod": 4, "Maj1": 5, "Maj2": 6, "Sev": 7, "Sal": 0}, |
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"FireplaceQu": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5, "NA": 0}, |
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"GarageType": {"2Types": 1, "Attchd": 2, "Basment": 3, "BuiltIn": 4, "CarPort": 5, "Detchd": 6, "NA": 0}, |
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"GarageFinish": {"NA": 1, "Unf": 2, "RFn": 3, "Fin": 0}, |
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"GarageQual": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5, "NA": 0}, |
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"GarageCond": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5, "NA": 0}, |
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"PavedDrive": {"N": 0, "P": 1, "Y": 2}, |
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"PoolQC": {"Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 0}, |
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"Fence": {"NA": 1, "MnWw": 2,"GdWo": 3, "MnPrv": 4, "GdPrv": 0}, |
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"MiscFeature": {"Elev": 1, "Gar2": 2, "Othr": 3, "Shed": 4, "TenC": 5, "NA": 0}, |
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"SaleType": {"WD": 1, "CWD": 2, "VWD": 3, "New": 4, "COD": 5, "Con": 6, "ConLw": 7, "ConLI": 8, "ConLD": 9, "Oth": 0}, |
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"SaleCondition":{"Normal": 1, "Abnorml": 2, "AdjLand": 3, "Alloca": 4, "Family": 5, "Partial": 0}} |
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train_data = train_data.drop("Id", axis = 1) |
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test_data = test_data.drop("Id", axis = 1) |
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train_data = train_data.replace(cleanup_cats); |
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test_data = test_data.replace(cleanup_cats); |
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removals = train_data.columns[train_data.isnull().any()] |
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Train_data = train_data.drop(removals, axis = 1) |
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Test_data = test_data.drop(removals,axis=1 ) |
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X_train, X_test, y_train, y_test = train_test_split(Train_data.drop('SalePrice', axis=1), Train_data['SalePrice'], test_size=0.2, shuffle = True, random_state=42) |
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training_data=lgb.Dataset(X_train,label=y_train) |
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valid_data=lgb.Dataset(X_test,label=y_test) |
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params = { |
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"objective": "regression", |
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"n_estimators": 10000, |
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"learning_rate": 0.08828308704850689, |
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"num_leaves": 256, |
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"max_depth": 11, |
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"min_data_in_leaf": 100, |
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"lambda_l1": 55, |
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"lambda_l2": 60, |
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"min_gain_to_split": 13.827512822883651, |
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"bagging_fraction": 1.0, |
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"bagging_freq": 1, |
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"feature_fraction": 0.4 |
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} |
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model = lgb.train(params, |
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training_data, |
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valid_sets=valid_data, |
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callbacks = [early_stopping(300)] |
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) |
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def make_a_prediction(M, X): |
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prediction = M.predict(X) |
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return prediction |
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predictions = make_a_prediction(model, X_test) |
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print(predictions) |
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import shap |
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explainer = shap.TreeExplainer(model) |
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shap_values = explainer.shap_values(X_test) |
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shap.initjs() |
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shap.force_plot(explainer.expected_value, shap_values=shap_values, feature_names=X_test.columns) |
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shap.initjs() |
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shap.decision_plot(explainer.expected_value, shap_values, feature_names=np.array(X_test.columns)) |
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shap.initjs() |
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shap.summary_plot(shap_values=shap_values, feature_names=X_test.columns) |
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interaction_values = explainer.shap_interaction_values(X_test) |
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interaction_values[0].round(2) |
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pd.DataFrame(interaction_values[0].round(2)).head(60) |
