Split modeling module into two rules; Add RandomOverSampler for resampling; Add log; Fix bug of AUC

Snakefile

@@ -111,19 +111,31 @@ rule all:
                             cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
                             source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
                             day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]),
-        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",
+        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",
+                            rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
+                            cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
+                            days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
+                            days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
+                            cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
+                            source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
+                            day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
+                            summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
+        expand(
+            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",
                 rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
                 cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
                 days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
                 days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
                 cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
-                            model = config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"],
                 cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"],
                 source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
                 day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
                 summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"],
-                            scaler = config["PARAMS_FOR_ANALYSIS"]["SCALER"],
                 result_component = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"]), 
+            zip, 
+            model = models,
+            scaler = scalers),
+
         # Vizualisations
         expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("reports/figures/{pid}/compliance_heatmap.html", pid=config["PIDS"]),

config.yaml

@@ -166,26 +166,35 @@ PARAMS_FOR_ANALYSIS:
   PARTICIPANT_DAYS_AFTER_THRESHOLD: 4
 
   # Extract summarised features from daily features with any of the following substrings
-  NUMERICAL_OPERATORS: ["count", "sum", "length", "avg"]
+  NUMERICAL_OPERATORS: ["count", "sum", "length", "avg", "restinghr"]
   CATEGORICAL_OPERATORS: ["mostcommon"]
 
   MODEL_NAMES: ["LogReg", "kNN", "SVM", "DT", "RF", "GB", "XGBoost", "LightGBM"]
   CV_METHODS: ["LeaveOneOut"]
   SUMMARISED: ["summarised"] # "summarised" or "notsummarised"
-  SCALER: ["notnormalized", "minmaxscaler", "standardscaler", "robustscaler"]
   RESULT_COMPONENTS: ["fold_predictions", "fold_metrics", "overall_results", "fold_feature_importances"]
 
+  MODEL_SCALER:
+    LogReg: ["notnormalized", "minmaxscaler", "standardscaler", "robustscaler"]
+    kNN: ["minmaxscaler", "standardscaler", "robustscaler"]
+    SVM:  ["minmaxscaler", "standardscaler", "robustscaler"]
+    DT: ["notnormalized"]
+    RF: ["notnormalized"]
+    GB: ["notnormalized"]
+    XGBoost: ["notnormalized"]
+    LightGBM: ["notnormalized"]
+
   MODEL_HYPERPARAMS:
     LogReg:
       {"clf__C": [0.01, 0.1, 1, 10, 100], "clf__solver": ["newton-cg", "lbfgs", "liblinear", "saga"], "clf__penalty": ["l2"]}
     kNN:
-      {"clf__n_neighbors": range(1, 21, 2), "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]}
+      {"clf__n_neighbors": [1, 3, 5], "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]}
     SVM:
       {"clf__C": [0.01, 0.1, 1, 10, 100], "clf__gamma": ["scale", "auto"], "clf__kernel": ["rbf", "poly", "sigmoid"]}
     DT:
-      {"clf__criterion": ["gini", "entropy"], "clf__max_depth": [None, 3, 5, 7, 9], "clf__max_features": [None, "auto", "sqrt", "log2"]}
+      {"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 5, 7, 9], "clf__max_features": [null, "auto", "sqrt", "log2"]}
     RF:
-      {"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [None, 3, 5, 7, 9]}
+      {"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [null, 3, 5, 7, 9]}
     GB:
       {"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]}
     XGBoost:

rules/models.snakefile

@@ -1,3 +1,5 @@
+ruleorder: nan_cells_ratio_of_cleaned_features > merge_features_and_targets
+
 def input_merge_features_of_single_participant(wildcards):
     if wildcards.source == "phone_fitbit_features":
         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)
@@ -93,11 +95,24 @@ rule nan_cells_ratio_of_cleaned_features:
     script:
         "../src/models/nan_cells_ratio_of_cleaned_features.py"
  
-rule modeling:
+rule merge_features_and_targets:
     input:
-        cleaned_features = "data/processed/data_for_population_model/{source}_{day_segment}_clean.csv",
+        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",
         demographic_features = "data/processed/data_for_population_model/demographic_features.csv",
         targets = "data/processed/data_for_population_model/targets_{summarised}.csv",
+    params:
+        summarised = "{summarised}",
+        cols_var_threshold = "{cols_var_threshold}",
+        numerical_operators = config["PARAMS_FOR_ANALYSIS"]["NUMERICAL_OPERATORS"],
+        categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"]
+    output:
+        "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"
+    script:
+        "../src/models/merge_features_and_targets.py"
+ 
+rule modeling:
+    input:
+        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"
     params:
         model = "{model}",
         cv_method = "{cv_method}",
@@ -105,16 +120,16 @@ rule modeling:
         day_segment = "{day_segment}",
         summarised = "{summarised}",
         scaler = "{scaler}",
-        cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
-        numerical_operators = config["PARAMS_FOR_ANALYSIS"]["NUMERICAL_OPERATORS"],
         categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"],
         categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_DEMOGRAPHIC_FEATURES"],
         model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"],
-        rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}"
+        rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}"
     output:
-        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",
+        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",
-        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",
+        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",
-        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",
+        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",
-        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"
+        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"
+    log:
+        "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"
     script:
         "../src/models/modeling.py"

src/models/merge_features_and_targets.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import numpy as np
+from modeling_utils import getMatchingColNames, dropZeroVarianceCols
+
+
+def summarisedNumericalFeatures(col_names, features):
+    numerical_features = features.groupby(["pid"])[col_names].var()
+    numerical_features.columns = numerical_features.columns.str.replace("daily", "overallvar")
+    return numerical_features
+
+def summarisedCategoricalFeatures(col_names, features):
+    categorical_features = features.groupby(["pid"])[col_names].agg(lambda x: int(pd.Series.mode(x)[0]))
+    categorical_features.columns = categorical_features.columns.str.replace("daily", "overallmode")
+    return categorical_features
+
+def summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold):
+    numerical_col_names = getMatchingColNames(numerical_operators, features)
+    categorical_col_names = getMatchingColNames(categorical_operators, features)
+    numerical_features = summarisedNumericalFeatures(numerical_col_names, features)
+    categorical_features = summarisedCategoricalFeatures(categorical_col_names, features)
+    features = pd.concat([numerical_features, categorical_features], axis=1)
+    if cols_var_threshold == "True": # double check the categorical features
+        features = dropZeroVarianceCols(features)
+    elif cols_var_threshold == "Flase":
+        pass
+    else:
+        ValueError("COLS_VAR_THRESHOLD parameter in config.yaml can only be 'True' or 'False'")
+    return features
+
+
+summarised = snakemake.params["summarised"]
+cols_var_threshold = snakemake.params["cols_var_threshold"]
+numerical_operators = snakemake.params["numerical_operators"]
+categorical_operators = snakemake.params["categorical_operators"]
+
+
+features = pd.read_csv(snakemake.input["cleaned_features"], parse_dates=["local_date"])
+demographic_features = pd.read_csv(snakemake.input["demographic_features"], index_col=["pid"])
+targets = pd.read_csv(snakemake.input["targets"], index_col=["pid"])
+
+
+# Extract summarised features based on daily features:
+# for categorical features: calculate variance across all days
+# for numerical features: calculate mode across all days
+if summarised == "summarised":
+    features = summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold)
+data = pd.concat([features, demographic_features, targets], axis=1, join="inner")
+
+data.to_csv(snakemake.output[0], index=True)
+

src/models/modeling.py

@@ -1,34 +1,9 @@
 import pandas as pd
-from modeling_utils import dropZeroVarianceCols, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline
+import numpy as np
+from modeling_utils import getMatchingColNames, dropZeroVarianceCols, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline
 from sklearn.model_selection import train_test_split, LeaveOneOut, GridSearchCV, cross_val_score, KFold
 
 
-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
-
-def summarisedNumericalFeatures(col_names, features):
-    numerical_features = features.groupby(["pid"])[col_names].var()
-    numerical_features.columns = numerical_features.columns.str.replace("daily", "overallvar")
-    return numerical_features
-
-def summarisedCategoricalFeatures(col_names, features):
-    categorical_features = features.groupby(["pid"])[col_names].agg(lambda x: int(pd.Series.mode(x)[0]))
-    categorical_features.columns = categorical_features.columns.str.replace("daily", "overallmode")
-    return categorical_features
-
-def summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold):
-    numerical_col_names = getMatchingColNames(numerical_operators, features)
-    categorical_col_names = getMatchingColNames(categorical_operators, features)
-    numerical_features = summarisedNumericalFeatures(numerical_col_names, features)
-    categorical_features = summarisedCategoricalFeatures(categorical_col_names, features)
-    features = pd.concat([numerical_features, categorical_features], axis=1)
-    if cols_var_threshold: # double check the categorical features
-        features = dropZeroVarianceCols(features)
-    return features
 
 def preprocessNumericalFeatures(train_numerical_features, test_numerical_features, scaler, flag):
     # fillna with mean
@@ -67,17 +42,15 @@ def preprocesFeatures(train_numerical_features, test_numerical_features, categor
 
 ##############################################################
 # Summary of the workflow
-# Step 1. Read parameters, features and targets
+# Step 1. Read parameters and data
-# Step 2. Extract summarised features based on daily features
+# Step 2. Nested cross validation
-# Step 3. Create pipeline
+# Step 3. Model evaluation
-# Step 4. Nested cross validation
+# Step 4. Save results, parameters, and metrics to CSV files
-# Step 5. Model evaluation
-# Step 6. Save results, parameters, and metrics to CSV files
 ##############################################################
 
 
 
-# Step 1. Read parameters, features and targets
+# Step 1. Read parameters and data
 # Read parameters
 model = snakemake.params["model"]
 source = snakemake.params["source"]
@@ -85,39 +58,24 @@ summarised = snakemake.params["summarised"]
 day_segment = snakemake.params["day_segment"]
 scaler = snakemake.params["scaler"]
 cv_method = snakemake.params["cv_method"]
-cols_var_threshold = snakemake.params["cols_var_threshold"]
-numerical_operators = snakemake.params["numerical_operators"]
 categorical_operators = snakemake.params["categorical_operators"]
 categorical_colnames_demographic_features = snakemake.params["categorical_demographic_features"]
 model_hyperparams = snakemake.params["model_hyperparams"][model]
 rowsnan_colsnan_days_colsvar_threshold = snakemake.params["rowsnan_colsnan_days_colsvar_threshold"] # thresholds for data cleaning
-# Read features and targets
-demographic_features = pd.read_csv(snakemake.input["demographic_features"], index_col=["pid"])
-targets = pd.read_csv(snakemake.input["targets"], index_col=["pid"])
-features = pd.read_csv(snakemake.input["cleaned_features"], parse_dates=["local_date"])
-# Compute the proportion of missing value cells among all features
-nan_ratio = features.isnull().sum().sum() / (features.shape[0] * features.shape[1])
 
 
-# Step 2. Extract summarised features based on daily features:
+# Read data and split
-# for categorical features: calculate variance across all days
+data = pd.read_csv(snakemake.input["data"], index_col=["pid"])
-# for numerical features: calculate mode across all days
-if summarised == "summarised":
-    features = summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold)
-
-categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, features)
-
-data = pd.concat([features, demographic_features, targets], axis=1, join="inner")
 data_x, data_y = data.drop("target", axis=1), data[["target"]]
+categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, data_x)
 
 
-# Step 3. Create pipeline
+
-pipeline = createPipeline(model)
+# 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()
+    cur_fold_pred = clf.predict(test_x).tolist()
-    pred_y_prob = pred_y_prob + clf.predict_proba(test_x)[:, 1].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
+# Step 3. Model evaluation
-acc, pre1, recall1, f11, auc, kappa = getMetrics(pred_y, pred_y_prob, true_y)
+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": []})
+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], "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]})
+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)
 

src/models/modeling_utils.py

@@ -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)
+    metrics = {}
-    pre1 = precision_score(true_y, pred_y, average=None, labels=[0,1])[1]
+    # metrics for all categories
-    recall1 = recall_score(true_y, pred_y, average=None, labels=[0,1])[1]
+    metrics["accuracy"] = accuracy_score(true_y, pred_y)
-    f11 = f1_score(true_y, pred_y, average=None, labels=[0,1])[1]
+    metrics["auc"] = roc_auc_score(true_y, pred_y_prob)
-    auc = roc_auc_score(true_y, pred_y_prob)
+    metrics["kappa"] = cohen_kappa_score(true_y, pred_y)
-    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)),
+            ("sampling", oversampler),
-            ("clf", XGBClassifier(random_state=0))
+            ("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)),
+            ("sampling", oversampler),
-            ("clf", LGBMClassifier(random_state=0))
+            ("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.")