Split modeling module into two rules; Add RandomOverSampler for resampling; Add log; Fix bug of AUC
Meng Li
parent
5fab99d8df
commit
8c8378f74a
24
Snakefile
24
Snakefile
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@ -111,19 +111,31 @@ rule all:
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cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
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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/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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expand("data/processed/data_for_population_model/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.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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summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
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expand(
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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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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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result_component = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"]),
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zip,
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model = models,
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scaler = scalers),
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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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23
config.yaml
23
config.yaml
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@ -144,7 +144,7 @@ PARAMS_FOR_ANALYSIS:
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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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DROP_VALID_SENSED_DAYS:
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@ -166,26 +166,35 @@ PARAMS_FOR_ANALYSIS:
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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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NUMERICAL_OPERATORS: ["count", "sum", "length", "avg", "restinghr"]
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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_SCALER:
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LogReg: ["notnormalized", "minmaxscaler", "standardscaler", "robustscaler"]
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kNN: ["minmaxscaler", "standardscaler", "robustscaler"]
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SVM: ["minmaxscaler", "standardscaler", "robustscaler"]
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DT: ["notnormalized"]
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RF: ["notnormalized"]
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GB: ["notnormalized"]
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XGBoost: ["notnormalized"]
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LightGBM: ["notnormalized"]
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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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{"clf__n_neighbors": [1, 3, 5], "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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{"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 5, 7, 9], "clf__max_features": [null, "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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{"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [null, 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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@ -1,3 +1,5 @@
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ruleorder: nan_cells_ratio_of_cleaned_features > merge_features_and_targets
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def input_merge_features_of_single_participant(wildcards):
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if wildcards.source == "phone_fitbit_features":
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return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"]["PHONE_FEATURES"] + config["PARAMS_FOR_ANALYSIS"]["FITBIT_FEATURES"], day_segment=wildcards.day_segment)
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@ -93,11 +95,24 @@ rule nan_cells_ratio_of_cleaned_features:
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script:
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"../src/models/nan_cells_ratio_of_cleaned_features.py"
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rule modeling:
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rule merge_features_and_targets:
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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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cleaned_features = "data/processed/data_for_population_model/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{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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summarised = "{summarised}",
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cols_var_threshold = "{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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output:
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"data/processed/data_for_population_model/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv"
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script:
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"../src/models/merge_features_and_targets.py"
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rule modeling:
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input:
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data = "data/processed/data_for_population_model/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{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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@ -105,16 +120,16 @@ rule modeling:
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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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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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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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log:
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"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}/notes.log"
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script:
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"../src/models/modeling.py"
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"../src/models/modeling.py"
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@ -0,0 +1,50 @@
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import pandas as pd
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import numpy as np
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from modeling_utils import getMatchingColNames, dropZeroVarianceCols
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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 == "True": # double check the categorical features
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features = dropZeroVarianceCols(features)
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elif cols_var_threshold == "Flase":
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pass
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else:
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ValueError("COLS_VAR_THRESHOLD parameter in config.yaml can only be 'True' or 'False'")
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return features
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summarised = snakemake.params["summarised"]
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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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features = pd.read_csv(snakemake.input["cleaned_features"], parse_dates=["local_date"])
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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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# 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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data = pd.concat([features, demographic_features, targets], axis=1, join="inner")
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data.to_csv(snakemake.output[0], index=True)
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@ -1,34 +1,9 @@
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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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import numpy as np
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from modeling_utils import getMatchingColNames, 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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@ -67,17 +42,15 @@ def preprocesFeatures(train_numerical_features, test_numerical_features, categor
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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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# Step 1. Read parameters and data
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# Step 2. Nested cross validation
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# Step 3. Model evaluation
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# Step 4. 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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# Step 1. Read parameters and data
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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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@ -85,39 +58,24 @@ 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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# Compute the proportion of missing value cells among all features
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nan_ratio = features.isnull().sum().sum() / (features.shape[0] * features.shape[1])
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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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# Read data and split
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data = pd.read_csv(snakemake.input["data"], index_col=["pid"])
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data_x, data_y = data.drop("target", axis=1), data[["target"]]
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categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, data_x)
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# Step 3. Create pipeline
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pipeline = createPipeline(model)
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|
||||
# Step 2. Nested cross validation
|
||||
cv_class = globals()[cv_method]
|
||||
inner_cv = cv_class()
|
||||
outer_cv = cv_class()
|
||||
|
||||
# Step 4. Nested cross validation
|
||||
fold_id, pid, best_params, true_y, pred_y, pred_y_prob = [], [], [], [], [], []
|
||||
feature_importances_all_folds = pd.DataFrame()
|
||||
fold_count = 1
|
||||
|
|
@ -143,14 +101,33 @@ for train_index, test_index in outer_cv.split(data_x):
|
|||
num_of_participants = train_x.shape[0] + test_x.shape[0]
|
||||
num_of_features = train_x.shape[1]
|
||||
|
||||
targets_value_counts = train_y["target"].value_counts()
|
||||
if len(targets_value_counts) < 2 or max(targets_value_counts) < 5:
|
||||
notes = open(snakemake.log[0], mode="w")
|
||||
notes.write(targets_value_counts.to_string())
|
||||
notes.close()
|
||||
break
|
||||
|
||||
# Inner cross validation
|
||||
clf = GridSearchCV(estimator=pipeline, param_grid=model_hyperparams, cv=inner_cv, scoring="f1_micro")
|
||||
if min(targets_value_counts) >= 6:
|
||||
# SMOTE requires n_neighbors <= n_samples, the default value of n_neighbors is 6
|
||||
clf = GridSearchCV(estimator=createPipeline(model, "SMOTE"), param_grid=model_hyperparams, cv=inner_cv, scoring="f1_micro")
|
||||
else:
|
||||
# RandomOverSampler: over-sample the minority class(es) by picking samples at random with replacement.
|
||||
clf = GridSearchCV(estimator=createPipeline(model, "RandomOverSampler"), param_grid=model_hyperparams, cv=inner_cv, scoring="f1_micro")
|
||||
clf.fit(train_x, train_y.values.ravel())
|
||||
|
||||
# Collect results and parameters
|
||||
best_params = best_params + [clf.best_params_]
|
||||
pred_y = pred_y + clf.predict(test_x).tolist()
|
||||
pred_y_prob = pred_y_prob + clf.predict_proba(test_x)[:, 1].tolist()
|
||||
cur_fold_pred = clf.predict(test_x).tolist()
|
||||
pred_y = pred_y + cur_fold_pred
|
||||
|
||||
proba_of_two_categories = clf.predict_proba(test_x).tolist()
|
||||
if cur_fold_pred[0]:
|
||||
pred_y_prob = pred_y_prob + [row[proba_of_two_categories[0].index(max(proba_of_two_categories[0]))] for row in proba_of_two_categories]
|
||||
else:
|
||||
pred_y_prob = pred_y_prob + [row[proba_of_two_categories[0].index(min(proba_of_two_categories[0]))] for row in proba_of_two_categories]
|
||||
|
||||
true_y = true_y + test_y.values.ravel().tolist()
|
||||
pid = pid + test_y.index.tolist() # each test partition (fold) in the outer cv is a participant (LeaveOneOut cv)
|
||||
feature_importances_current_fold = getFeatureImportances(model, clf.best_estimator_.steps[1][1], train_x.columns)
|
||||
|
|
@ -158,13 +135,16 @@ for train_index, test_index in outer_cv.split(data_x):
|
|||
fold_id.append(fold_count)
|
||||
fold_count = fold_count + 1
|
||||
|
||||
# Step 5. Model evaluation
|
||||
acc, pre1, recall1, f11, auc, kappa = getMetrics(pred_y, pred_y_prob, true_y)
|
||||
# Step 3. Model evaluation
|
||||
if len(pred_y) > 1:
|
||||
metrics = getMetrics(pred_y, pred_y_prob, true_y)
|
||||
else:
|
||||
metrics = {"accuracy": None, "precision0": None, "recall0": None, "f10": None, "precision1": None, "recall1": None, "f11": None, "auc": None, "kappa": None}
|
||||
|
||||
# Step 6. Save results, parameters, and metrics to CSV files
|
||||
# Step 4. Save results, parameters, and metrics to CSV files
|
||||
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})
|
||||
fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []})
|
||||
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]})
|
||||
fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision0": [], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []})
|
||||
overall_results = pd.DataFrame({"num_of_participants": [num_of_participants], "num_of_features": [num_of_features], "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": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]})
|
||||
feature_importances_all_folds.insert(loc=0, column='fold_id', value=fold_id)
|
||||
feature_importances_all_folds.insert(loc=1, column='pid', value=pid)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,11 +1,18 @@
|
|||
import pandas as pd
|
||||
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
|
||||
from imblearn.over_sampling import SMOTE
|
||||
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix
|
||||
from sklearn.metrics import precision_recall_fscore_support
|
||||
from sklearn.metrics import cohen_kappa_score, roc_auc_score
|
||||
from imblearn.pipeline import Pipeline
|
||||
from imblearn.over_sampling import SMOTE
|
||||
from imblearn.over_sampling import SMOTE, RandomOverSampler
|
||||
|
||||
|
||||
def getMatchingColNames(operators, features):
|
||||
col_names = []
|
||||
for col in features.columns:
|
||||
if any(operator in col for operator in operators):
|
||||
col_names.append(col)
|
||||
return col_names
|
||||
|
||||
# drop columns with zero variance
|
||||
def dropZeroVarianceCols(data):
|
||||
|
|
@ -39,14 +46,21 @@ def getNormAllParticipantsScaler(features, scaler_flag):
|
|||
|
||||
# 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)
|
||||
metrics = {}
|
||||
# metrics for all categories
|
||||
metrics["accuracy"] = accuracy_score(true_y, pred_y)
|
||||
metrics["auc"] = roc_auc_score(true_y, pred_y_prob)
|
||||
metrics["kappa"] = cohen_kappa_score(true_y, pred_y)
|
||||
# metrics for label 0
|
||||
metrics["precision0"] = precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[0]
|
||||
metrics["recall0"] = recall_score(true_y, pred_y, average=None, labels=[0,1])[0]
|
||||
metrics["f10"] = f1_score(true_y, pred_y, average=None, labels=[0,1])[0]
|
||||
# metrics for label 1
|
||||
metrics["precision1"] = precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[1]
|
||||
metrics["recall1"] = recall_score(true_y, pred_y, average=None, labels=[0,1])[1]
|
||||
metrics["f11"] = f1_score(true_y, pred_y, average=None, labels=[0,1])[1]
|
||||
|
||||
return acc, pre1, recall1, f11, auc, kappa
|
||||
return metrics
|
||||
|
||||
# get feature importances
|
||||
def getFeatureImportances(model, clf, cols):
|
||||
|
|
@ -83,54 +97,62 @@ def getFeatureImportances(model, clf, cols):
|
|||
|
||||
return feature_importances
|
||||
|
||||
def createPipeline(model):
|
||||
def createPipeline(model, oversampler_type):
|
||||
|
||||
if oversampler_type == "SMOTE":
|
||||
oversampler = SMOTE(sampling_strategy="minority", random_state=0)
|
||||
elif oversampler_type == "RandomOverSampler":
|
||||
oversampler = RandomOverSampler(sampling_strategy="minority", random_state=0)
|
||||
else:
|
||||
raise ValueError("RAPIDS pipeline only support 'SMOTE' and 'RandomOverSampler' oversampling methods.")
|
||||
|
||||
if model == "LogReg":
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("sampling", oversampler),
|
||||
("clf", LogisticRegression(random_state=0))
|
||||
])
|
||||
elif model == "kNN":
|
||||
from sklearn.neighbors import KNeighborsClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("sampling", oversampler),
|
||||
("clf", KNeighborsClassifier())
|
||||
])
|
||||
elif model == "SVM":
|
||||
from sklearn.svm import SVC
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("sampling", oversampler),
|
||||
("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)),
|
||||
("sampling", oversampler),
|
||||
("clf", DecisionTreeClassifier(random_state=0))
|
||||
])
|
||||
elif model == "RF":
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("sampling", oversampler),
|
||||
("clf", RandomForestClassifier(random_state=0))
|
||||
])
|
||||
elif model == "GB":
|
||||
from sklearn.ensemble import GradientBoostingClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("sampling", oversampler),
|
||||
("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))
|
||||
("sampling", oversampler),
|
||||
("clf", XGBClassifier(random_state=0, n_jobs=36))
|
||||
])
|
||||
elif model == "LightGBM":
|
||||
from lightgbm import LGBMClassifier
|
||||
pipeline = Pipeline([
|
||||
("sampling", SMOTE(sampling_strategy="minority", random_state=0)),
|
||||
("clf", LGBMClassifier(random_state=0))
|
||||
("sampling", oversampler),
|
||||
("clf", LGBMClassifier(random_state=0, n_jobs=36))
|
||||
])
|
||||
else:
|
||||
raise ValueError("RAPIDS pipeline only support LogReg, kNN, SVM, DT, RF, GB, XGBoost, and LightGBM algorithms for classification problems.")
|
||||
|
|
|
|||
Loading…
Reference in New Issue