Add modeling module
Meng Li
parent
b688a0827f
commit
7cbb227214
33
Snakefile
33
Snakefile
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@ -73,22 +73,35 @@ rule all:
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segment = config["WIFI"]["DAY_SEGMENTS"]),
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# Models
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expand("data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_original.csv",
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pid = config["PIDS"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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pid = config["PIDS"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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expand("data/processed/data_for_population_model/{source}_{day_segment}_original.csv",
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_clean.csv",
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pid = config["PIDS"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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pid = config["PIDS"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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expand("data/processed/data_for_population_model/{source}_{day_segment}_clean.csv",
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
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expand("data/processed/data_for_population_model/demographic_features.csv"),
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expand("data/processed/data_for_population_model/targets_{summarised}.csv",
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summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
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expand("data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/{result_component}.csv",
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rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
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cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
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days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
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days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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model = config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"],
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cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"],
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source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
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day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
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summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"],
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scaler = config["PARAMS_FOR_ANALYSIS"]["SCALER"],
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result_component = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"]),
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# Vizualisations
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expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
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expand("reports/figures/{pid}/compliance_heatmap.html", pid=config["PIDS"]),
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28
config.yaml
28
config.yaml
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@ -136,6 +136,7 @@ PARAMS_FOR_ANALYSIS:
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FITBIT_FEATURES: [fitbit_heartrate, fitbit_step]
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PHONE_FITBIT_FEATURES: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
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DEMOGRAPHIC_FEATURES: [age, gender, inpatientdays]
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CATEGORICAL_DEMOGRAPHIC_FEATURES: ["gender"]
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# Whether or not to include only days with enough valid sensed hours
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# logic can be found in rule phone_valid_sensed_days of rules/preprocessing.snakefile
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@ -157,7 +158,34 @@ PARAMS_FOR_ANALYSIS:
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PARTICIPANT_DAYS_BEFORE_THRESHOLD: 7
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PARTICIPANT_DAYS_AFTER_THRESHOLD: 4
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# Extract summarised features from daily features with any of the following substrings
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NUMERICAL_OPERATORS: ["count", "sum", "length", "avg"]
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CATEGORICAL_OPERATORS: ["mostcommon"]
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MODEL_NAMES: ["LogReg", "kNN", "SVM", "DT", "RF", "GB", "XGBoost", "LightGBM"]
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CV_METHODS: ["LeaveOneOut"]
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SUMMARISED: ["summarised"] # "summarised" or "notsummarised"
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SCALER: ["notnormalized", "minmaxscaler", "standardscaler", "robustscaler"]
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RESULT_COMPONENTS: ["fold_predictions", "fold_metrics", "overall_results", "fold_feature_importances"]
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MODEL_HYPERPARAMS:
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LogReg:
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{"clf__C": [0.01, 0.1, 1, 10, 100], "clf__solver": ["newton-cg", "lbfgs", "liblinear", "saga"], "clf__penalty": ["l2"]}
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kNN:
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{"clf__n_neighbors": range(1, 21, 2), "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]}
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SVM:
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{"clf__C": [0.01, 0.1, 1, 10, 100], "clf__gamma": ["scale", "auto"], "clf__kernel": ["rbf", "poly", "sigmoid"]}
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DT:
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{"clf__criterion": ["gini", "entropy"], "clf__max_depth": [None, 3, 5, 7, 9], "clf__max_features": [None, "auto", "sqrt", "log2"]}
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RF:
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{"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [None, 3, 5, 7, 9]}
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GB:
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__subsample": [0.5, 0.7, 1.0], "clf__max_depth": [3, 5, 7, 9]}
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XGBoost:
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]}
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LightGBM:
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]}
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# Target Settings:
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# 1 => TARGETS_RATIO_THRESHOLD (ceiling) or more of available CESD scores were TARGETS_VALUE_THRESHOLD or higher; 0 => otherwise
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@ -85,3 +85,28 @@ rule clean_features_for_population_model:
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script:
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"../src/models/clean_features_for_model.R"
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rule modeling:
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input:
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cleaned_features = "data/processed/data_for_population_model/{source}_{day_segment}_clean.csv",
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demographic_features = "data/processed/data_for_population_model/demographic_features.csv",
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targets = "data/processed/data_for_population_model/targets_{summarised}.csv",
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params:
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model = "{model}",
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cv_method = "{cv_method}",
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source = "{source}",
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day_segment = "{day_segment}",
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summarised = "{summarised}",
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scaler = "{scaler}",
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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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numerical_operators = config["PARAMS_FOR_ANALYSIS"]["NUMERICAL_OPERATORS"],
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categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"],
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categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_DEMOGRAPHIC_FEATURES"],
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model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"],
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rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}"
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output:
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fold_predictions = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_predictions.csv",
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fold_metrics = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_metrics.csv",
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overall_results = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/overall_results.csv",
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fold_feature_importances = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_feature_importances.csv"
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script:
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"../src/models/modeling.py"
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@ -0,0 +1,172 @@
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import pandas as pd
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from modeling_utils import dropZeroVarianceCols, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline
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from sklearn.model_selection import train_test_split, LeaveOneOut, GridSearchCV, cross_val_score, KFold
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def getMatchingColNames(operators, features):
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col_names = []
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for col in features.columns:
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if any(operator in col for operator in operators):
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col_names.append(col)
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return col_names
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def summarisedNumericalFeatures(col_names, features):
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numerical_features = features.groupby(["pid"])[col_names].var()
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numerical_features.columns = numerical_features.columns.str.replace("daily", "overallvar")
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return numerical_features
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def summarisedCategoricalFeatures(col_names, features):
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categorical_features = features.groupby(["pid"])[col_names].agg(lambda x: int(pd.Series.mode(x)[0]))
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categorical_features.columns = categorical_features.columns.str.replace("daily", "overallmode")
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return categorical_features
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def summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold):
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numerical_col_names = getMatchingColNames(numerical_operators, features)
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categorical_col_names = getMatchingColNames(categorical_operators, features)
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numerical_features = summarisedNumericalFeatures(numerical_col_names, features)
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categorical_features = summarisedCategoricalFeatures(categorical_col_names, features)
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features = pd.concat([numerical_features, categorical_features], axis=1)
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if cols_var_threshold: # double check the categorical features
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features = dropZeroVarianceCols(features)
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return features
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def preprocessNumericalFeatures(train_numerical_features, test_numerical_features, scaler, flag):
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# fillna with mean
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if flag == "train":
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numerical_features = train_numerical_features.fillna(train_numerical_features.mean())
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elif flag == "test":
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numerical_features = test_numerical_features.fillna(train_numerical_features.mean())
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else:
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raise ValueError("flag should be 'train' or 'test'")
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# normalize
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if scaler != "notnormalized":
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scaler = getNormAllParticipantsScaler(train_numerical_features, scaler)
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numerical_features = pd.DataFrame(scaler.transform(numerical_features), index=numerical_features.index, columns=numerical_features.columns)
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return numerical_features
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def preprocessCategoricalFeatures(categorical_features, mode_categorical_features):
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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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categorical_features = pd.get_dummies(categorical_features)
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return categorical_features
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def splitNumericalCategoricalFeatures(features, categorical_feature_colnames):
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numerical_features = features.drop(categorical_feature_colnames, axis=1)
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categorical_features = features[categorical_feature_colnames].copy()
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return numerical_features, categorical_features
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def preprocesFeatures(train_numerical_features, test_numerical_features, categorical_features, mode_categorical_features, scaler, flag):
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numerical_features = preprocessNumericalFeatures(train_numerical_features, test_numerical_features, scaler, flag)
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categorical_features = preprocessCategoricalFeatures(categorical_features, mode_categorical_features)
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features = pd.concat([numerical_features, categorical_features], axis=1)
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return features
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##############################################################
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# Summary of the workflow
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# Step 1. Read parameters, features and targets
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# Step 2. Extract summarised features based on daily features
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# Step 3. Create pipeline
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# Step 4. Nested cross validation
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# Step 5. Model evaluation
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# Step 6. Save results, parameters, and metrics to CSV files
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##############################################################
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# Step 1. Read parameters, features and targets
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# Read parameters
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model = snakemake.params["model"]
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source = snakemake.params["source"]
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summarised = snakemake.params["summarised"]
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day_segment = snakemake.params["day_segment"]
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scaler = snakemake.params["scaler"]
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cv_method = snakemake.params["cv_method"]
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cols_var_threshold = snakemake.params["cols_var_threshold"]
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numerical_operators = snakemake.params["numerical_operators"]
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categorical_operators = snakemake.params["categorical_operators"]
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categorical_colnames_demographic_features = snakemake.params["categorical_demographic_features"]
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model_hyperparams = snakemake.params["model_hyperparams"][model]
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rowsnan_colsnan_days_colsvar_threshold = snakemake.params["rowsnan_colsnan_days_colsvar_threshold"] # thresholds for data cleaning
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# Read features and targets
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demographic_features = pd.read_csv(snakemake.input["demographic_features"], index_col=["pid"])
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targets = pd.read_csv(snakemake.input["targets"], index_col=["pid"])
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features = pd.read_csv(snakemake.input["cleaned_features"], parse_dates=["local_date"])
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# Step 2. Extract summarised features based on daily features:
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# for categorical features: calculate variance across all days
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# for numerical features: calculate mode across all days
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if summarised == "summarised":
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features = summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold)
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categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, features)
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data = pd.concat([features, demographic_features, targets], axis=1, join="inner")
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data_x, data_y = data.drop("target", axis=1), data[["target"]]
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# Step 3. Create pipeline
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pipeline = createPipeline(model)
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cv_class = globals()[cv_method]
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inner_cv = cv_class()
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outer_cv = cv_class()
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# Step 4. Nested cross validation
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fold_id, pid, best_params, true_y, pred_y, pred_y_prob = [], [], [], [], [], []
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feature_importances_all_folds = pd.DataFrame()
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fold_count = 1
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# Outer cross validation
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for train_index, test_index in outer_cv.split(data_x):
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# Split train and test, numerical and categorical features
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train_x, test_x = data_x.iloc[train_index], data_x.iloc[test_index]
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train_numerical_features, train_categorical_features = splitNumericalCategoricalFeatures(train_x, categorical_feature_colnames)
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train_y, test_y = data_y.iloc[train_index], data_y.iloc[test_index]
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test_numerical_features, test_categorical_features = splitNumericalCategoricalFeatures(test_x, categorical_feature_colnames)
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# Preprocess: impute and normalize
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mode_categorical_features = train_categorical_features.mode().iloc[0]
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train_x = preprocesFeatures(train_numerical_features, None, train_categorical_features, mode_categorical_features, scaler, "train")
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test_x = preprocesFeatures(train_numerical_features, test_numerical_features, test_categorical_features, mode_categorical_features, scaler, "test")
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train_x, test_x = train_x.align(test_x, join='outer', axis=1, fill_value=0) # in case we get rid off categorical columns
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# Compute number of participants, features and proportion of missing value cells among all features,
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# values do not change between folds
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if fold_count == 1:
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num_of_participants = train_x.shape[0] + test_x.shape[0]
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num_of_features = train_x.shape[1]
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nan_ratio = (train_x.isnull().sum().sum() + test_x.isnull().sum().sum()) / ((train_x.shape[0] + test_x.shape[0]) * train_x.shape[1])
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# Inner cross validation
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clf = GridSearchCV(estimator=pipeline, param_grid=model_hyperparams, cv=inner_cv, scoring="f1_micro")
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clf.fit(train_x, train_y.values.ravel())
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# Collect results and parameters
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best_params = best_params + [clf.best_params_]
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pred_y = pred_y + clf.predict(test_x).tolist()
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pred_y_prob = pred_y_prob + clf.predict_proba(test_x)[:, 1].tolist()
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true_y = true_y + test_y.values.ravel().tolist()
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pid = pid + test_y.index.tolist() # each test partition (fold) in the outer cv is a participant (LeaveOneOut cv)
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feature_importances_current_fold = getFeatureImportances(model, clf.best_estimator_.steps[1][1], train_x.columns)
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feature_importances_all_folds = pd.concat([feature_importances_all_folds, feature_importances_current_fold], sort=False, axis=0)
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fold_id.append(fold_count)
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fold_count = fold_count + 1
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# Step 5. Model evaluation
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acc, pre1, recall1, f11, auc, kappa = getMetrics(pred_y, pred_y_prob, true_y)
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# Step 6. Save results, parameters, and metrics to CSV files
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fold_predictions = pd.DataFrame({"fold_id": fold_id, "pid": pid, "hyperparameters": best_params, "true_y": true_y, "pred_y": pred_y, "pred_y_prob": pred_y_prob})
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fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []})
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overall_results = pd.DataFrame({"num_of_participants": [num_of_participants], "num_of_features": [num_of_features], "nan_ratio": [nan_ratio], "rowsnan_colsnan_days_colsvar_threshold": [rowsnan_colsnan_days_colsvar_threshold], "model": [model], "cv_method": [cv_method], "source": [source], "scaler": [scaler], "day_segment": [day_segment], "summarised": [summarised], "accuracy": [acc], "precision1": [pre1], "recall1": [recall1], "f11": [f11], "auc": [auc], "kappa": [kappa]})
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feature_importances_all_folds.insert(loc=0, column='fold_id', value=fold_id)
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feature_importances_all_folds.insert(loc=1, column='pid', value=pid)
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fold_predictions.to_csv(snakemake.output["fold_predictions"], index=False)
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fold_metrics.to_csv(snakemake.output["fold_metrics"], index=False)
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overall_results.to_csv(snakemake.output["overall_results"], index=False)
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feature_importances_all_folds.to_csv(snakemake.output["fold_feature_importances"], index=False)
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@ -0,0 +1,138 @@
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import pandas as pd
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from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
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from imblearn.over_sampling import SMOTE
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix
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from sklearn.metrics import precision_recall_fscore_support
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from sklearn.metrics import cohen_kappa_score, roc_auc_score
|
||||
from imblearn.pipeline import Pipeline
|
||||
from imblearn.over_sampling import SMOTE
|
||||
|
||||
# drop columns with zero variance
|
||||
def dropZeroVarianceCols(data):
|
||||
if not data.empty:
|
||||
var_df = data.var()
|
||||
keep_col = []
|
||||
for col in var_df.index:
|
||||
if var_df.loc[col] > 0:
|
||||
keep_col.append(col)
|
||||
data_drop_cols_var = data.loc[:, keep_col]
|
||||
else:
|
||||
data_drop_cols_var = data
|
||||
return data_drop_cols_var
|
||||
|
||||
# normalize based on all participants: return fitted scaler
|
||||
def getNormAllParticipantsScaler(features, scaler_flag):
|
||||
# MinMaxScaler
|
||||
if scaler_flag == "minmaxscaler":
|
||||
scaler = MinMaxScaler()
|
||||
# StandardScaler
|
||||
elif scaler_flag == "standardscaler":
|
||||
scaler = StandardScaler()
|
||||
# RobustScaler
|
||||
elif scaler_flag == "robustscaler":
|
||||
scaler = RobustScaler()
|
||||
else:
|
||||
# throw exception
|
||||
raise ValueError("The normalization method is not predefined, please check if the PARAMS_FOR_ANALYSIS.NORMALIZED in config.yaml file is correct.")
|
||||
scaler.fit(features)
|
||||
return scaler
|
||||
|
||||
# get metrics: accuracy, precision1, recall1, f11, auc, kappa
|
||||
def getMetrics(pred_y, pred_y_prob, true_y):
|
||||
acc = accuracy_score(true_y, pred_y)
|
||||
pre1 = precision_score(true_y, pred_y, average=None, labels=[0,1])[1]
|
||||
recall1 = recall_score(true_y, pred_y, average=None, labels=[0,1])[1]
|
||||
f11 = f1_score(true_y, pred_y, average=None, labels=[0,1])[1]
|
||||
auc = roc_auc_score(true_y, pred_y_prob)
|
||||
kappa = cohen_kappa_score(true_y, pred_y)
|
||||
|
||||
return acc, pre1, recall1, f11, auc, kappa
|
||||
|
||||
# get feature importances
|
||||
def getFeatureImportances(model, clf, cols):
|
||||
if model == "LogReg":
|
||||
# Extract the coefficient of the features in the decision function
|
||||
# Calculate the absolute value
|
||||
# Normalize it to sum 1
|
||||
feature_importances = pd.DataFrame(zip(clf.coef_[0],cols), columns=["Value", "Feature"])
|
||||
feature_importances["Value"] = feature_importances["Value"].abs()/feature_importances["Value"].abs().sum()
|
||||
elif model == "kNN":
|
||||
# Feature importance is not defined for the KNN Classification, return an empty dataframe
|
||||
feature_importances = pd.DataFrame(columns=["Value", "Feature"])
|
||||
elif model == "SVM":
|
||||
# Coefficient of the features are only available for linear kernel
|
||||
try:
|
||||
# For linear kernel
|
||||
# Extract the coefficient of the features in the decision function
|
||||
# Calculate the absolute value
|
||||
# Normalize it to sum 1
|
||||
feature_importances = pd.DataFrame(zip(clf.coef_[0],cols), columns=["Value", "Feature"])
|
||||
feature_importances["Value"] = feature_importances["Value"].abs()/feature_importances["Value"].abs().sum()
|
||||
except:
|
||||
# For nonlinear kernel, return an empty dataframe directly
|
||||
feature_importances = pd.DataFrame(columns=["Value", "Feature"])
|
||||
elif model == "LightGBM":
|
||||
# Extract feature_importances_ and normalize it to sum 1
|
||||
feature_importances = pd.DataFrame(zip(clf.feature_importances_,cols), columns=["Value", "Feature"])
|
||||
feature_importances["Value"] = feature_importances["Value"]/feature_importances["Value"].sum()
|
||||
else:
|
||||
# For DT, RF, GB, XGBoost classifier, extract feature_importances_. This field has already been normalized.
|
||||
feature_importances = pd.DataFrame(zip(clf.feature_importances_,cols), columns=["Value", "Feature"])
|
||||
|
||||
feature_importances = feature_importances.set_index(["Feature"]).T
|
||||
|
||||
return feature_importances
|
||||
|
||||
def createPipeline(model):
|
||||
if model == "LogReg":
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", LogisticRegression(random_state=0))
|
||||
])
|
||||
elif model == "kNN":
|
||||
from sklearn.neighbors import KNeighborsClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", KNeighborsClassifier())
|
||||
])
|
||||
elif model == "SVM":
|
||||
from sklearn.svm import SVC
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", SVC(random_state=0, probability=True))
|
||||
])
|
||||
elif model == "DT":
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", DecisionTreeClassifier(random_state=0))
|
||||
])
|
||||
elif model == "RF":
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", RandomForestClassifier(random_state=0))
|
||||
])
|
||||
elif model == "GB":
|
||||
from sklearn.ensemble import GradientBoostingClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", GradientBoostingClassifier(random_state=0))
|
||||
])
|
||||
elif model == "XGBoost":
|
||||
from xgboost import XGBClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", XGBClassifier(random_state=0))
|
||||
])
|
||||
elif model == "LightGBM":
|
||||
from lightgbm import LGBMClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", LGBMClassifier(random_state=0))
|
||||
])
|
||||
else:
|
||||
raise ValueError("RAPIDS pipeline only support LogReg, kNN, SVM, DT, RF, GB, XGBoost, and LightGBM algorithms for classification problems.")
|
||||
|
||||
return pipeline
|
||||
Loading…
Reference in New Issue