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

pull/95/head
Meng Li 2020-05-15 18:42:03 -04:00
parent 5fab99d8df
commit 8c8378f74a
6 changed files with 193 additions and 105 deletions

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@ -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"],
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"]),
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"],
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"],
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"]),

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@ -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:

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@ -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_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",
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_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",
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"
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"

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@ -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)

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@ -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 2. Extract summarised features based on daily features
# Step 3. Create pipeline
# Step 4. Nested cross validation
# Step 5. Model evaluation
# Step 6. Save results, parameters, and metrics to CSV files
# Step 1. Read parameters and data
# Step 2. Nested cross validation
# Step 3. Model evaluation
# Step 4. 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:
# 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)
categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, features)
data = pd.concat([features, demographic_features, targets], axis=1, join="inner")
# Read data and split
data = pd.read_csv(snakemake.input["data"], index_col=["pid"])
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()
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)

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@ -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.")