Update classification runner.
junos
parent
075fdab9ea
commit
f58d20ffc2
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@ -6,7 +6,7 @@
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# extension: .py
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# extension: .py
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# format_name: percent
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# format_name: percent
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# format_version: '1.3'
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# format_version: '1.3'
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# jupytext_version: 1.13.0
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# jupytext_version: 1.14.5
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# kernelspec:
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# kernelspec:
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# display_name: straw2analysis
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# display_name: straw2analysis
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# language: python
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# language: python
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@ -18,445 +18,86 @@
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import os
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import os
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import sys
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import pandas as pd
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from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
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from sklearn.model_selection import LeaveOneGroupOut, cross_validate, StratifiedKFold
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from sklearn.dummy import DummyClassifier
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from sklearn.impute import SimpleImputer
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from lightgbm import LGBMClassifier
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import xgboost as xg
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from IPython.core.interactiveshell import InteractiveShell
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from IPython.core.interactiveshell import InteractiveShell
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from machine_learning.helper import (
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impute_encode_categorical_features,
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prepare_cross_validator,
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prepare_sklearn_data_format,
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run_all_classification_models,
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)
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InteractiveShell.ast_node_interactivity = "all"
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InteractiveShell.ast_node_interactivity = "all"
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nb_dir = os.path.split(os.getcwd())[0]
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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sys.path.append(nb_dir)
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import machine_learning.helper
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# %% [markdown]
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# %%
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# # RAPIDS models
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CV_METHOD = "logo" # logo, half_logo, 5kfold
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# Cross-validation method (could be regarded as a hyperparameter)
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# %% [markdown]
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N_SL = 3 # Number of largest/smallest accuracies (of particular CV) outputs
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# ## Set script's parameters
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UNDERSAMPLING = False
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#
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# (bool) If True this will train and test data on balanced dataset
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# (using undersampling method)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
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cv_method_str = 'logo' # logo, half_logo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
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n_sl = 3 # Number of largest/smallest accuracies (of particular CV) outputs
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undersampling = False # (bool) If True this will train and test data on balanced dataset (using undersampling method)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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model_input = pd.read_csv("../data/stressfulness_event_with_target_0_ver2/input_appraisal_stressfulness_event_mean.csv")
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model_input = pd.read_csv(
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# model_input = model_input[model_input.columns.drop(list(model_input.filter(regex='empatica_temperature')))]
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"E:/STRAWresults/20230415/daily/input_PANAS_negative_affect_mean.csv"
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)
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# model_input =
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# model_input[model_input.columns.drop(
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# list(model_input.filter(regex='empatica_temperature'))
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# )]
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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model_input["target"].value_counts()
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model_input.set_index(index_columns, inplace=True)
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model_input['target'].value_counts()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# bins = [-10, 0, 10] # bins for z-scored targets
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# bins = [-10, 0, 10] # bins for z-scored targets
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bins = [-1, 0, 4] # bins for stressfulness (0-4) target
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bins = [-1, 0, 4] # bins for stressfulness (0-4) target
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model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'high'], retbins=True, right=True) #['low', 'medium', 'high']
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model_input["target"], edges = pd.cut(
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model_input['target'].value_counts(), edges
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model_input.target, bins=bins, labels=["low", "high"], retbins=True, right=True
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) # ['low', 'medium', 'high']
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model_input["target"].value_counts(), edges
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# model_input = model_input[model_input['target'] != "medium"]
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# model_input = model_input[model_input['target'] != "medium"]
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model_input['target'] = model_input['target'].astype(str).apply(lambda x: 0 if x == "low" else 1)
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model_input["target"] = (
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model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
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)
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model_input['target'].value_counts()
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model_input["target"].value_counts()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# UnderSampling
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# UnderSampling
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if undersampling:
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if UNDERSAMPLING:
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no_stress = model_input[model_input['target'] == 0]
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no_stress = model_input[model_input["target"] == 0]
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stress = model_input[model_input['target'] == 1]
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stress = model_input[model_input["target"] == 1]
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no_stress = no_stress.sample(n=len(stress))
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no_stress = no_stress.sample(n=len(stress))
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model_input = pd.concat([stress,no_stress], axis=0)
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model_input = pd.concat([stress, no_stress], axis=0)
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# model_input_new = pd.DataFrame(columns=model_input.columns)
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# for pid in model_input["pid"].unique():
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# stress = model_input[(model_input["pid"] == pid) & (model_input['target'] == 1)]
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# no_stress = model_input[(model_input["pid"] == pid) & (model_input['target'] == 0)]
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# if (len(stress) == 0):
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# continue
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# if (len(no_stress) == 0):
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# continue
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# model_input_new = pd.concat([model_input_new, stress], axis=0)
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# no_stress = no_stress.sample(n=min(len(stress), len(no_stress)))
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# # In case there are more stress samples than no_stress, take all instances of no_stress.
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# model_input_new = pd.concat([model_input_new, no_stress], axis=0)
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# model_input = model_input_new
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# model_input_new = pd.concat([model_input_new, no_stress], axis=0)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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if cv_method_str == 'half_logo':
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model_input_encoded = impute_encode_categorical_features(model_input)
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model_input['pid_index'] = model_input.groupby('pid').cumcount()
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model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
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model_input["pid_index"] = (model_input['pid_index'] / model_input['pid_count'] + 1).round()
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model_input["pid_half"] = model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
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data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
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else:
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data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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categorical_feature_colnames = ["gender", "startlanguage"]
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additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
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categorical_feature_colnames += additional_categorical_features
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categorical_features = data_x[categorical_feature_colnames].copy()
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mode_categorical_features = categorical_features.mode().iloc[0]
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# fillna with mode
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categorical_features = categorical_features.fillna(mode_categorical_features)
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# one-hot encoding
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categorical_features = categorical_features.apply(lambda col: col.astype("category"))
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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
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train_x = pd.concat([numerical_features, categorical_features], axis=1)
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train_x.dtypes
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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cv_method = StratifiedKFold(n_splits=5, shuffle=True) # Defaults to 5 k-folds in cross_validate method
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if cv_method_str == 'logo' or cv_method_str == 'half_logo':
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cv_method = LeaveOneGroupOut()
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cv_method.get_n_splits(
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train_x,
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data_y,
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groups=data_groups,
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)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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imputer = SimpleImputer(missing_values=np.nan, strategy='median')
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# %% [markdown]
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# ### Baseline: Dummy Classifier (most frequent)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
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dummy_class = DummyClassifier(strategy="most_frequent")
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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dummy_classifier = cross_validate(
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dummy_class,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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print("Acc (median)", np.nanmedian(dummy_classifier['test_accuracy']))
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print("Acc (mean)", np.mean(dummy_classifier['test_accuracy']))
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print("Precision", np.mean(dummy_classifier['test_precision']))
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print("Recall", np.mean(dummy_classifier['test_recall']))
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print("F1", np.mean(dummy_classifier['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown] nteract={"transient": {"deleting": false}}
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# ### All models
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# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
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final_scores = machine_learning.helper.run_all_classification_models(imputer.fit_transform(train_x), data_y, data_groups, cv_method)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
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# %%
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# %%
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final_scores.index.name = "metric"
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data_x, data_y, data_groups = prepare_sklearn_data_format(
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final_scores = final_scores.set_index(["method", final_scores.index])
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model_input_encoded, CV_METHOD
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final_scores.to_csv(f"../presentation/event_stressful_detection_{cv_method_str}.csv")
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# %% [markdown]
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# ### Logistic Regression
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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logistic_regression = linear_model.LogisticRegression()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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log_reg_scores = cross_validate(
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logistic_regression,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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cross_validator = prepare_cross_validator(data_x, data_y, data_groups, CV_METHOD)
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print("Acc (median)", np.nanmedian(log_reg_scores['test_accuracy']))
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print("Acc (mean)", np.mean(log_reg_scores['test_accuracy']))
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print("Precision", np.mean(log_reg_scores['test_precision']))
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print("Recall", np.mean(log_reg_scores['test_recall']))
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print("F1", np.mean(log_reg_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-log_reg_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(log_reg_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# %%
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# ### Support Vector Machine
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data_y.head()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %%
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svc = svm.SVC()
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data_y.tail()
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# %%
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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data_y.shape
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svc_scores = cross_validate(
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# %%
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svc,
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scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
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X=imputer.fit_transform(train_x),
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# %%
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y=data_y,
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scores.to_csv(
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groups=data_groups,
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"../presentation/JCQ_supervisor_support_regression_" + CV_METHOD + ".csv",
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cv=cv_method,
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index=False,
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n_jobs=-1,
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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print("Acc (median)", np.nanmedian(svc_scores['test_accuracy']))
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print("Acc (mean)", np.mean(svc_scores['test_accuracy']))
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print("Precision", np.mean(svc_scores['test_precision']))
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print("Recall", np.mean(svc_scores['test_recall']))
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print("F1", np.mean(svc_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-svc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(svc_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Gaussian Naive Bayes
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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gaussian_nb = naive_bayes.GaussianNB()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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gaussian_nb_scores = cross_validate(
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gaussian_nb,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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print("Acc (median)", np.nanmedian(gaussian_nb_scores['test_accuracy']))
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print("Acc (mean)", np.mean(gaussian_nb_scores['test_accuracy']))
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print("Precision", np.mean(gaussian_nb_scores['test_precision']))
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print("Recall", np.mean(gaussian_nb_scores['test_recall']))
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print("F1", np.mean(gaussian_nb_scores['test_f1']))
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print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-gaussian_nb_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
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print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gaussian_nb_scores['test_accuracy'], n_sl)[:n_sl]))
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# %% [markdown]
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# ### Stochastic Gradient Descent Classifier
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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sgdc = linear_model.SGDClassifier()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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sgdc_scores = cross_validate(
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sgdc,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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scoring=('accuracy', 'precision', 'recall', 'f1')
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)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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print("Acc (median)", np.nanmedian(sgdc_scores['test_accuracy']))
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print("Acc (mean)", np.mean(sgdc_scores['test_accuracy']))
|
|
||||||
print("Precision", np.mean(sgdc_scores['test_precision']))
|
|
||||||
print("Recall", np.mean(sgdc_scores['test_recall']))
|
|
||||||
print("F1", np.mean(sgdc_scores['test_f1']))
|
|
||||||
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-sgdc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
|
|
||||||
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(sgdc_scores['test_accuracy'], n_sl)[:n_sl]))
|
|
||||||
|
|
||||||
# %% [markdown]
|
|
||||||
# ### K-nearest neighbors
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
knn = neighbors.KNeighborsClassifier()
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
knn_scores = cross_validate(
|
|
||||||
knn,
|
|
||||||
X=imputer.fit_transform(train_x),
|
|
||||||
y=data_y,
|
|
||||||
groups=data_groups,
|
|
||||||
cv=cv_method,
|
|
||||||
n_jobs=-1,
|
|
||||||
error_score='raise',
|
|
||||||
scoring=('accuracy', 'precision', 'recall', 'f1')
|
|
||||||
)
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
print("Acc (median)", np.nanmedian(knn_scores['test_accuracy']))
|
|
||||||
print("Acc (mean)", np.mean(knn_scores['test_accuracy']))
|
|
||||||
print("Precision", np.mean(knn_scores['test_precision']))
|
|
||||||
print("Recall", np.mean(knn_scores['test_recall']))
|
|
||||||
print("F1", np.mean(knn_scores['test_f1']))
|
|
||||||
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-knn_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
|
|
||||||
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(knn_scores['test_accuracy'], n_sl)[:n_sl]))
|
|
||||||
|
|
||||||
# %% [markdown]
|
|
||||||
# ### Decision Tree
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
dtree = tree.DecisionTreeClassifier()
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
dtree_scores = cross_validate(
|
|
||||||
dtree,
|
|
||||||
X=imputer.fit_transform(train_x),
|
|
||||||
y=data_y,
|
|
||||||
groups=data_groups,
|
|
||||||
cv=cv_method,
|
|
||||||
n_jobs=-1,
|
|
||||||
error_score='raise',
|
|
||||||
scoring=('accuracy', 'precision', 'recall', 'f1')
|
|
||||||
)
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
print("Acc (median)", np.nanmedian(dtree_scores['test_accuracy']))
|
|
||||||
print("Acc (mean)", np.mean(dtree_scores['test_accuracy']))
|
|
||||||
print("Precision", np.mean(dtree_scores['test_precision']))
|
|
||||||
print("Recall", np.mean(dtree_scores['test_recall']))
|
|
||||||
print("F1", np.mean(dtree_scores['test_f1']))
|
|
||||||
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dtree_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
|
|
||||||
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dtree_scores['test_accuracy'], n_sl)[:n_sl]))
|
|
||||||
|
|
||||||
# %% [markdown]
|
|
||||||
# ### Random Forest Classifier
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
rfc = ensemble.RandomForestClassifier()
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
rfc_scores = cross_validate(
|
|
||||||
rfc,
|
|
||||||
X=imputer.fit_transform(train_x),
|
|
||||||
y=data_y,
|
|
||||||
groups=data_groups,
|
|
||||||
cv=cv_method,
|
|
||||||
n_jobs=-1,
|
|
||||||
error_score='raise',
|
|
||||||
scoring=('accuracy', 'precision', 'recall', 'f1'),
|
|
||||||
return_estimator=True
|
|
||||||
)
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
print("Acc (median)", np.nanmedian(rfc_scores['test_accuracy']))
|
|
||||||
print("Acc (mean)", np.mean(rfc_scores['test_accuracy']))
|
|
||||||
print("Precision", np.mean(rfc_scores['test_precision']))
|
|
||||||
print("Recall", np.mean(rfc_scores['test_recall']))
|
|
||||||
print("F1", np.mean(rfc_scores['test_f1']))
|
|
||||||
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-rfc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
|
|
||||||
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(rfc_scores['test_accuracy'], n_sl)[:n_sl]))
|
|
||||||
|
|
||||||
# %% [markdown]
|
|
||||||
# ### Feature importance (RFC)
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
rfc_es_fimp = pd.DataFrame(columns=list(train_x.columns))
|
|
||||||
for idx, estimator in enumerate(rfc_scores['estimator']):
|
|
||||||
feature_importances = pd.DataFrame(estimator.feature_importances_,
|
|
||||||
index = list(train_x.columns),
|
|
||||||
columns=['importance'])
|
|
||||||
# print("\nFeatures sorted by their score for estimator {}:".format(idx))
|
|
||||||
# print(feature_importances.sort_values('importance', ascending=False).head(10))
|
|
||||||
rfc_es_fimp = pd.concat([rfc_es_fimp, feature_importances]).groupby(level=0).mean()
|
|
||||||
|
|
||||||
pd.set_option('display.max_rows', 100)
|
|
||||||
print(rfc_es_fimp.sort_values('importance', ascending=False).head(30))
|
|
||||||
|
|
||||||
rfc_es_fimp.sort_values('importance', ascending=False).head(30).plot.bar()
|
|
||||||
|
|
||||||
rfc_es_fimp.sort_values('importance', ascending=False).tail(30).plot.bar()
|
|
||||||
|
|
||||||
train_x['empatica_temperature_cr_stdDev_X_SO_mean'].value_counts()
|
|
||||||
|
|
||||||
# %% [markdown]
|
|
||||||
# ### Gradient Boosting Classifier
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
gbc = ensemble.GradientBoostingClassifier()
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
gbc_scores = cross_validate(
|
|
||||||
gbc,
|
|
||||||
X=imputer.fit_transform(train_x),
|
|
||||||
y=data_y,
|
|
||||||
groups=data_groups,
|
|
||||||
cv=cv_method,
|
|
||||||
n_jobs=-1,
|
|
||||||
error_score='raise',
|
|
||||||
scoring=('accuracy', 'precision', 'recall', 'f1')
|
|
||||||
)
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
print("Acc (median)", np.nanmedian(gbc_scores['test_accuracy']))
|
|
||||||
print("Acc (mean)", np.mean(gbc_scores['test_accuracy']))
|
|
||||||
print("Precision", np.mean(gbc_scores['test_precision']))
|
|
||||||
print("Recall", np.mean(gbc_scores['test_recall']))
|
|
||||||
print("F1", np.mean(gbc_scores['test_f1']))
|
|
||||||
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-gbc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
|
|
||||||
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gbc_scores['test_accuracy'], n_sl)[:n_sl]))
|
|
||||||
|
|
||||||
# %% [markdown]
|
|
||||||
# ### LGBM Classifier
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
lgbm = LGBMClassifier()
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
lgbm_scores = cross_validate(
|
|
||||||
lgbm,
|
|
||||||
X=imputer.fit_transform(train_x),
|
|
||||||
y=data_y,
|
|
||||||
groups=data_groups,
|
|
||||||
cv=cv_method,
|
|
||||||
n_jobs=-1,
|
|
||||||
error_score='raise',
|
|
||||||
scoring=('accuracy', 'precision', 'recall', 'f1')
|
|
||||||
)
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
print("Acc (median)", np.nanmedian(lgbm_scores['test_accuracy']))
|
|
||||||
print("Acc (mean)", np.mean(lgbm_scores['test_accuracy']))
|
|
||||||
print("Precision", np.mean(lgbm_scores['test_precision']))
|
|
||||||
print("Recall", np.mean(lgbm_scores['test_recall']))
|
|
||||||
print("F1", np.mean(lgbm_scores['test_f1']))
|
|
||||||
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-lgbm_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
|
|
||||||
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(lgbm_scores['test_accuracy'], n_sl)[:n_sl]))
|
|
||||||
|
|
||||||
# %% [markdown]
|
|
||||||
# ### XGBoost Classifier
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
xgb_classifier = xg.sklearn.XGBClassifier()
|
|
||||||
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
xgb_classifier_scores = cross_validate(
|
|
||||||
xgb_classifier,
|
|
||||||
X=imputer.fit_transform(train_x),
|
|
||||||
y=data_y,
|
|
||||||
groups=data_groups,
|
|
||||||
cv=cv_method,
|
|
||||||
n_jobs=-1,
|
|
||||||
error_score='raise',
|
|
||||||
scoring=('accuracy', 'precision', 'recall', 'f1')
|
|
||||||
)
|
|
||||||
# %% jupyter={"source_hidden": false, "outputs_hidden": false}
|
|
||||||
print("Acc (median)", np.nanmedian(xgb_classifier_scores['test_accuracy']))
|
|
||||||
print("Acc (mean)", np.mean(xgb_classifier_scores['test_accuracy']))
|
|
||||||
print("Precision", np.mean(xgb_classifier_scores['test_precision']))
|
|
||||||
print("Recall", np.mean(xgb_classifier_scores['test_recall']))
|
|
||||||
print("F1", np.mean(xgb_classifier_scores['test_f1']))
|
|
||||||
print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
|
|
||||||
print(f"Smallest {n_sl} ACC:", np.sort(np.partition(xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl]))
|
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue