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Update notebooks
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‎docs/Workshop/202206Workshop-Part1 InterpretableML_BikeSharing.ipynb‎

Lines changed: 12 additions & 18 deletions
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@@ -667,10 +667,7 @@
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"source": [
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"import xgboost as xgb\n",
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"model = xgb.XGBRegressor(max_depth=7, n_estimators=500)\n",
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"\n",
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"pipeline = exp.make_pipeline(model=model, name='XGBoost')\n",
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"pipeline.fit()\n",
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"exp.register(pipeline=pipeline)"
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"exp.model_train(model, name=\"XGBoost\")"
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]
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},
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{
@@ -863,7 +860,7 @@
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],
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"source": [
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"# Check model performance\n",
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"exp.model_diagnose(model=\"XGBoost\", show='accuracy_result')"
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"exp.model_diagnose(model=\"XGBoost\", show='accuracy_table')"
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]
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},
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{
@@ -929,9 +926,9 @@
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams[\"figure.figsize\"] = (6, 5)\n",
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"\n",
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"_, _, _, _, feature_names, _ = exp.get_clean_data()\n",
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"clf.get_booster().feature_names = feature_names\n",
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"xgb.plot_importance(clf, title=\"XGBoost Variable Importance\", show_values=False)\n",
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"feature_names = exp.get_feature_names()\n",
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"model.get_booster().feature_names = feature_names\n",
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"xgb.plot_importance(model, title=\"XGBoost Variable Importance\", show_values=False)\n",
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"plt.show()"
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]
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},
@@ -1308,7 +1305,7 @@
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],
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"source": [
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"# Local - LIME\n",
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"exp.model_explain(model='XGBoost', show='local_lime', sample_id=1, figsize=(6, 5))"
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"exp.model_explain(model='XGBoost', show='lime', sample_id=1, figsize=(6, 5))"
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]
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},
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{
@@ -1369,7 +1366,7 @@
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],
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"source": [
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"# Local - SHAP (TreeSHAP)\n",
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"exp.model_explain(model='XGBoost', show='local_shap', sample_id=1, figsize=(6, 5))"
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"exp.model_explain(model='XGBoost', show='shap_waterfall', sample_id=1, figsize=(6, 5))"
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]
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},
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{
@@ -3974,11 +3971,8 @@
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],
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"source": [
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"import xgboost as xgb\n",
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"clf = xgb.XGBRegressor(max_depth=7, n_estimators=500)\n",
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"\n",
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"pipeline = exp.make_pipeline(model=clf, name='XGBoost')\n",
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"pipeline.fit()\n",
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"exp.register(pipeline=pipeline)"
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"model = xgb.XGBRegressor(max_depth=7, n_estimators=500)\n",
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"exp.model_train(model, name='XGBoost')"
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]
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},
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{
@@ -4156,7 +4150,7 @@
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}
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],
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"source": [
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"exp.model_compare(models=['XGBoost', 'MLP'], show='accuracy')"
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"exp.model_compare(models=['XGBoost', 'MLP'], show='accuracy_plot')"
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]
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},
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{
@@ -4250,7 +4244,7 @@
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}
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],
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"source": [
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"exp.model_compare(models=['XGBoost', 'MLP'], show='robustness')"
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"exp.model_compare(models=['XGBoost', 'MLP'], show='robustness_perf')"
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]
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},
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{
@@ -4344,7 +4338,7 @@
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}
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],
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"source": [
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"exp.model_compare(models=['XGBoost', 'MLP'], show='resilience', alpha=0.2)"
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"exp.model_compare(models=['XGBoost', 'MLP'], show='resilience_perf', alpha=0.2)"
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]
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}
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],

‎docs/Workshop/202206Workshop-Part2 Model_Diagnostics_BikeSharing.ipynb‎

Lines changed: 6 additions & 4 deletions
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@@ -2427,7 +2427,9 @@
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"outputs": [],
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"source": [
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"import xgboost as xgb\n",
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"exp.model_train(model=xgb.XGBRegressor(max_depth=7, n_estimators=500), name='XGBoost')"
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"\n",
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"model = xgb.XGBRegressor(max_depth=7, n_estimators=500)\n",
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"exp.model_train(model=model, name='XGBoost')"
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]
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},
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{
@@ -2504,9 +2506,9 @@
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams[\"figure.figsize\"] = (6, 5)\n",
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"\n",
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"_, _, _, _, feature_names, _ = exp.get_clean_data()\n",
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"clf.get_booster().feature_names = feature_names\n",
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"xgb.plot_importance(clf, title=\"XGBoost Variable Importance\", show_values=False)\n",
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"feature_names = exp.get_feature_names()\n",
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"model.get_booster().feature_names = feature_names\n",
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"xgb.plot_importance(model, title=\"XGBoost Variable Importance\", show_values=False)\n",
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"plt.show()"
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]
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},

‎docs/Workshop/202206Workshop-Part2 Model_Diagnostics_CaliforniaHousing.ipynb‎

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@@ -4611,7 +4611,8 @@
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],
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"source": [
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"import xgboost as xgb\n",
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"exp.model_train(model=xgb.XGBRegressor(max_depth=7, n_estimators=500), name='XGBoost')"
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"model = xgb.XGBRegressor(max_depth=7, n_estimators=500)\n",
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"exp.model_train(model=model, name='XGBoost')"
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]
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},
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{
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams[\"figure.figsize\"] = (6, 5)\n",
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"\n",
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"_, _, _, _, feature_names, _ = exp.get_clean_data()\n",
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"clf.get_booster().feature_names = feature_names\n",
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"xgb.plot_importance(clf, title=\"XGBoost Variable Importance\", show_values=False)\n",
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"feature_names = exp.get_feature_names()\n",
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"model.get_booster().feature_names = feature_names\n",
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"xgb.plot_importance(model, title=\"XGBoost Variable Importance\", show_values=False)\n",
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"plt.show()"
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]
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},

‎docs/Workshop/202206Workshop-Part2 Model_Diagnostics_TaiwanCredit.ipynb‎

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@@ -2007,11 +2007,8 @@
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],
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"source": [
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"import xgboost as xgb\n",
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"clf = xgb.XGBClassifier(max_depth=7, n_estimators=500)\n",
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"\n",
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"pipeline = exp.make_pipeline(model=clf, name='XGBoost')\n",
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"pipeline.fit()\n",
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"exp.register(pipeline=pipeline)"
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"model = xgb.XGBClassifier(max_depth=7, n_estimators=500)\n",
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"exp.model_train(model, name='XGBoost')"
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]
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},
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{

‎docs/Workshop/202207DemoNotebook_BikeSharing.ipynb‎

Lines changed: 2 additions & 5 deletions
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],
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"source": [
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"from xgboost import XGBRegressor\n",
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"\n",
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"clf = XGBRegressor(n_estimators=500, max_depth=7)\n",
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"pipeline = exp.make_pipeline(clf)\n",
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"pipeline.fit()\n",
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"exp.register(pipeline=pipeline, name='XGBoost7')"
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"model = xgb.XGBRegressor(max_depth=7, n_estimators=500)\n",
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"exp.model_train(model, name='XGBoost')"
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]
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},
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{

‎docs/Workshop/202207DemoNotebook_TaiwanCredit.ipynb‎

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@@ -1379,10 +1379,8 @@
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"source": [
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"from xgboost import XGBClassifier\n",
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"\n",
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"clf = XGBClassifier(n_estimators=500, max_depth=5)\n",
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"pipeline = exp.make_pipeline(clf)\n",
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"pipeline.fit()\n",
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"exp.register(pipeline=pipeline, name='XGBoost5')"
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"model = XGBClassifier(n_estimators=500, max_depth=5)\n",
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"exp.model_train(model, name=\"XGBoost5\")"
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]
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},
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{

‎docs/Workshop/202307-ResML/20230713ResML_BikeSharing.ipynb‎

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