Revert "Add a rule for model baselines."
The example was for a classification rather than regression problem.
This reverts commit 9ab0c8f289.
# Conflicts:
# rules/models.smk
labels
junos
parent
4ad261fae5
commit
9f5edf1c2b
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@ -49,15 +49,3 @@ rule merge_features_and_targets_for_population_model:
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script:
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"../src/models/merge_features_and_targets_for_population_model.py"
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rule model_individual_baselines:
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input:
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"data/processed/models/individual_model/{pid}/input.csv"
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params:
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cv_method = "{cv_method}",
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colnames_demographic_features = config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["FEATURES"],
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output:
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"data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv"
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log:
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"data/processed/models/individual_model/{pid}/output_{cv_method}/baselines_notes.log"
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script:
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"../src/models/model_baselines.py"
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@ -1,105 +0,0 @@
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import numpy as np
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import pandas as pd
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from statistics import mean
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from modelling_utils import getMetrics, createPipeline
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from sklearn.model_selection import LeaveOneOut
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# As we do not have probability of each category, use label to denote the probability directly.
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# The probability will only be used to calculate the AUC value.
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def baselineAccuracyOfMajorityClassClassifier(targets):
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majority_class = targets["target"].value_counts().idxmax()
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pred_y = [majority_class] * targets.shape[0]
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pred_y_proba = pred_y
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metrics = getMetrics(pred_y, pred_y_proba, targets["target"].values.ravel().tolist())
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return metrics, majority_class
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def baselineMetricsOfRandomWeightedClassifier(targets, majority_ratio, majority_class, iter_times):
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metrics_all_iters = {"accuracy": [], "precision0":[], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "f1_macro": [], "auc": [], "kappa": []}
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probabilities = [0, 0]
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probabilities[majority_class], probabilities[1 - majority_class] = majority_ratio, 1 - majority_ratio
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for i in range(iter_times):
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pred_y = np.random.RandomState(i).multinomial(1, probabilities, targets.shape[0])[:,1].tolist()
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pred_y_proba = pred_y
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metrics = getMetrics(pred_y, pred_y_proba, targets["target"].values.ravel().tolist())
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for key in metrics_all_iters.keys():
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metrics_all_iters[key].append(metrics[key].item())
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# Calculate average metrics across all iterations
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avg_metrics = {}
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for key in metrics_all_iters.keys():
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avg_metrics[key] = mean(metrics_all_iters[key])
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return avg_metrics
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def baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x, data_y, oversampler_type):
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pred_y, true_y = [], []
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for train_index, test_index in cv_method.split(data_x):
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train_x, test_x = data_x.iloc[train_index], data_x.iloc[test_index]
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train_y, test_y = data_y.iloc[train_index], data_y.iloc[test_index]
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clf = createPipeline("DT", oversampler_type)
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clf.fit(train_x, train_y.values.ravel())
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pred_y = pred_y + clf.predict(test_x).ravel().tolist()
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pred_y_proba = pred_y
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true_y = true_y + test_y.values.ravel().tolist()
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return getMetrics(pred_y, pred_y_proba, true_y)
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cv_method = globals()[snakemake.params["cv_method"]]()
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colnames_demographic_features = snakemake.params["colnames_demographic_features"]
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data = pd.read_csv(snakemake.input[0])
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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if "pid" in data.columns:
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index_columns.append("pid")
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data.set_index(index_columns, inplace=True)
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data_x, data_y = data.drop("target", axis=1), data[["target"]]
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targets_value_counts = data_y["target"].value_counts()
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baseline_metrics = pd.DataFrame(columns=["method", "fullMethodName", "accuracy", "precision0", "recall0", "f10", "precision1", "recall1", "f11", "f1_macro", "auc", "kappa"])
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if len(targets_value_counts) < 2:
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fout = open(snakemake.log[0], "w")
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fout.write(targets_value_counts.to_string())
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fout.close()
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else:
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if min(targets_value_counts) >= 6:
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oversampler_type = "SMOTE"
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else:
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oversampler_type = "RandomOverSampler"
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# Baseline 1: majority class classifier => predict every sample as majority class
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baseline1_metrics, majority_class = baselineAccuracyOfMajorityClassClassifier(data_y)
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majority_ratio = baseline1_metrics["accuracy"]
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# Baseline 2: random weighted classifier => random classifier with binomial distribution
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baseline2_metrics = baselineMetricsOfRandomWeightedClassifier(data_y, majority_ratio, majority_class, 1000)
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if "pid" in index_columns:
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# Baseline 3: decision tree with demographic features
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baseline3_metrics = baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x[colnames_demographic_features], data_y, oversampler_type)
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baselines = [baseline1_metrics, baseline2_metrics, baseline3_metrics]
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methods = ["majority", "rwc", "dt"]
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fullMethodNames = ["MajorityClassClassifier", "RandomWeightedClassifier", "DecisionTreeWithDemographicFeatures"]
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else:
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# Only have 2 baselines
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baselines = [baseline1_metrics, baseline2_metrics]
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methods = ["majority", "rwc"]
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fullMethodNames = ["MajorityClassClassifier", "RandomWeightedClassifier"]
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baseline_metrics = pd.DataFrame({"method": methods,
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"fullMethodName": fullMethodNames,
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"accuracy": [baseline["accuracy"] for baseline in baselines],
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"precision0": [baseline["precision0"] for baseline in baselines],
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"recall0": [baseline["recall0"] for baseline in baselines],
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"f10": [baseline["f10"] for baseline in baselines],
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"precision1": [baseline["precision1"] for baseline in baselines],
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"recall1": [baseline["recall1"] for baseline in baselines],
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"f11": [baseline["f11"] for baseline in baselines],
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"f1_macro": [baseline["f1_macro"] for baseline in baselines],
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"auc": [baseline["auc"] for baseline in baselines],
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"kappa": [baseline["kappa"] for baseline in baselines]})
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baseline_metrics.to_csv(snakemake.output[0], index=False)
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@ -1,164 +0,0 @@
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix
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from sklearn.metrics import precision_recall_fscore_support
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from sklearn.metrics import cohen_kappa_score, roc_auc_score
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from imblearn.pipeline import Pipeline
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from imblearn.over_sampling import SMOTE, RandomOverSampler
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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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# drop columns with zero variance
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def dropZeroVarianceCols(data):
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if not data.empty:
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var_df = data.var()
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keep_col = []
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for col in var_df.index:
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if var_df.loc[col] > 0:
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keep_col.append(col)
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data_drop_cols_var = data.loc[:, keep_col]
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else:
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data_drop_cols_var = data
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return data_drop_cols_var
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# normalize based on all participants: return fitted scaler
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def getNormAllParticipantsScaler(features, scaler_flag):
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# MinMaxScaler
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if scaler_flag == "minmaxscaler":
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scaler = MinMaxScaler()
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# StandardScaler
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elif scaler_flag == "standardscaler":
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scaler = StandardScaler()
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# RobustScaler
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elif scaler_flag == "robustscaler":
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scaler = RobustScaler()
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else:
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# throw exception
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raise ValueError("The normalization method is not predefined, please check if the PARAMS_FOR_ANALYSIS.NORMALIZED in config.yaml file is correct.")
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scaler.fit(features)
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return scaler
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# get metrics: accuracy, precision0, recall0, f10, precision1, recall1, f11, f1_macro, auc, kappa
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def getMetrics(pred_y, pred_y_proba, true_y):
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metrics = {}
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count = len(np.unique(true_y))
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label= np.unique(true_y)[0]
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# metrics for all categories
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metrics["accuracy"] = accuracy_score(true_y, pred_y)
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metrics["f1_macro"] = f1_score(true_y, pred_y, average="macro") # unweighted mean
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metrics["auc"] = np.nan if count == 1 else roc_auc_score(true_y, pred_y_proba)
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metrics["kappa"] = cohen_kappa_score(true_y, pred_y)
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# metrics for label 0
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metrics["precision0"] = np.nan if (count == 1 and label == 1) else precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[0]
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metrics["recall0"] = np.nan if (count == 1 and label == 1) else recall_score(true_y, pred_y, average=None, labels=[0,1])[0]
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metrics["f10"] = np.nan if (count == 1 and label == 1) else f1_score(true_y, pred_y, average=None, labels=[0,1])[0]
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# metrics for label 1
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metrics["precision1"] = np.nan if (count == 1 and label == 0) else precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[1]
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metrics["recall1"] = np.nan if (count == 1 and label == 0) else recall_score(true_y, pred_y, average=None, labels=[0,1])[1]
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metrics["f11"] = np.nan if (count == 1 and label == 0) else f1_score(true_y, pred_y, average=None, labels=[0,1])[1]
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return metrics
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# get feature importances
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def getFeatureImportances(model, clf, cols):
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if model == "LogReg":
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# Extract the coefficient of the features in the decision function
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# Calculate the absolute value
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# Normalize it to sum 1
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feature_importances = pd.DataFrame(zip(clf.coef_[0],cols), columns=["Value", "Feature"])
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feature_importances["Value"] = feature_importances["Value"].abs()/feature_importances["Value"].abs().sum()
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elif model == "kNN":
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# Feature importance is not defined for the KNN Classification, return an empty dataframe
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feature_importances = pd.DataFrame(columns=["Value", "Feature"])
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elif model == "SVM":
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# Coefficient of the features are only available for linear kernel
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try:
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# For linear kernel
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# Extract the coefficient of the features in the decision function
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# Calculate the absolute value
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# Normalize it to sum 1
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feature_importances = pd.DataFrame(zip(clf.coef_[0],cols), columns=["Value", "Feature"])
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feature_importances["Value"] = feature_importances["Value"].abs()/feature_importances["Value"].abs().sum()
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except:
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# For nonlinear kernel, return an empty dataframe directly
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feature_importances = pd.DataFrame(columns=["Value", "Feature"])
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elif model == "LightGBM":
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# Extract feature_importances_ and normalize it to sum 1
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feature_importances = pd.DataFrame(zip(clf.feature_importances_,cols), columns=["Value", "Feature"])
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feature_importances["Value"] = feature_importances["Value"]/feature_importances["Value"].sum()
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else:
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# For DT, RF, GB, XGBoost classifier, extract feature_importances_. This field has already been normalized.
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feature_importances = pd.DataFrame(zip(clf.feature_importances_,cols), columns=["Value", "Feature"])
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feature_importances = feature_importances.set_index(["Feature"]).T
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return feature_importances
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def createPipeline(model, oversampler_type):
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if oversampler_type == "SMOTE":
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oversampler = SMOTE(sampling_strategy="minority", random_state=0)
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elif oversampler_type == "RandomOverSampler":
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oversampler = RandomOverSampler(sampling_strategy="minority", random_state=0)
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else:
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raise ValueError("RAPIDS pipeline only support 'SMOTE' and 'RandomOverSampler' oversampling methods.")
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if model == "LogReg":
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from sklearn.linear_model import LogisticRegression
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pipeline = Pipeline([
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("sampling", oversampler),
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("clf", LogisticRegression(random_state=0))
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])
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elif model == "kNN":
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from sklearn.neighbors import KNeighborsClassifier
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pipeline = Pipeline([
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("sampling", oversampler),
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("clf", KNeighborsClassifier())
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])
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elif model == "SVM":
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from sklearn.svm import SVC
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pipeline = Pipeline([
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("sampling", oversampler),
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("clf", SVC(random_state=0, probability=True))
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])
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elif model == "DT":
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from sklearn.tree import DecisionTreeClassifier
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pipeline = Pipeline([
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("sampling", oversampler),
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("clf", DecisionTreeClassifier(random_state=0))
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])
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elif model == "RF":
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from sklearn.ensemble import RandomForestClassifier
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pipeline = Pipeline([
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("sampling", oversampler),
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("clf", RandomForestClassifier(random_state=0))
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])
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elif model == "GB":
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from sklearn.ensemble import GradientBoostingClassifier
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pipeline = Pipeline([
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("sampling", oversampler),
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("clf", GradientBoostingClassifier(random_state=0))
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])
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elif model == "XGBoost":
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from xgboost import XGBClassifier
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pipeline = Pipeline([
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("sampling", oversampler),
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("clf", XGBClassifier(random_state=0, n_jobs=36))
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])
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elif model == "LightGBM":
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from lightgbm import LGBMClassifier
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pipeline = Pipeline([
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("sampling", oversampler),
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("clf", LGBMClassifier(random_state=0, n_jobs=36))
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])
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else:
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raise ValueError("RAPIDS pipeline only support LogReg, kNN, SVM, DT, RF, GB, XGBoost, and LightGBM algorithms for classification problems.")
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return pipeline
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