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@@ -0,0 +1,90 @@
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+import sys
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+import os
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+from pathlib import Path
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+sys.path.append(str(Path(os.path.dirname(__file__)).parent)) # Dirty but it works
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+
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+from bop_scripts.preprocessing import remove_outliers
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+from bop_scripts.nn_models import torchMLPClassifier_sklearn, torchMLP
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+from bop_scripts.models import generate_model, fit_all_classifiers
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+import torch
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+import pandas as pd
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+import numpy as np
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+
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+from sklearn.base import BaseEstimator
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+
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+qualitatives_variables = ["gender", "last_7", "last_30"]
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+quantitatives_variables = ['age', 'temperature', 'heartrate', 'resprate', 'o2sat', 'sbp', 'dbp', 'pain']
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+text_variables = ["chiefcomplaint"]
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+labels = ['Cardiaque', 'Coagulation', 'Gazometrie', 'Glycemie_Sanguine', 'Hepato-Biliaire', 'IonoC', 'Lipase', 'NFS', 'Phospho-Calcique']
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+variables_ranges = {
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+ "temperature":[60,130],
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+ "heartrate":[20, 300],
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+ "resprate":[5, 50],
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+ "o2sat":[20, 100],
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+ "sbp":[40, 250],
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+ "dbp":[20, 200],
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+ "pain":[0,10]
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+}
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+device = "cuda:0" if torch.cuda.is_available() else "cpu"
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+
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+def torch_classifier_fn ():
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+
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+ torch_classifier = torchMLPClassifier_sklearn(
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+ torchMLP,
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+ early_stop_validations_size=10000,
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+ early_stop=True,
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+ early_stop_metric="f1",
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+ early_stop_tol=1,
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+ n_epochs=50,
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+ device_train= device,
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+ device_predict="cpu",
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+ class_weight="balanced",
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+ learning_rate=1e-4,
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+ verbose=False
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+ )
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+
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+ torch_sklearn_classifier = generate_model(
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+ torch_classifier,
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+ qualitatives_variables,
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+ quantitatives_variables,
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+ text_variables[0],
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+ remove_outliers=True,
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+ outliers_variables_ranges=variables_ranges,
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+ CountVectorizer_kwargs={"ngram_range":(1,1), "max_features":600}
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+ )
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+
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+ return torch_sklearn_classifier
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+
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+class Classifier(BaseEstimator):
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+
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+ def preprocess (self, X, y=None):
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+ X_clean, outliers = remove_outliers(X, variables_ranges)
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+ if y is not None:
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+ y = pd.DataFrame(y, columns=labels)
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+
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+ return X_clean, y
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+
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+ def fit(self, X, y):
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+ X, y = self.preprocess(X, y)
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+ self.classifiers = fit_all_classifiers(
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+ torch_classifier_fn,
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+ X,
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+ y,
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+ verbose=False
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+ )
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+ return self
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+
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+ def predict_proba(self, X):
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+ X, y = self.preprocess(X)
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+ predictions = []
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+ y_columns = labels
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+ for y_column in y_columns:
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+ predictions.append(self.classifiers[y_column].predict(X).reshape(-1, 1))
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+ y_pred = np.concatenate(predictions, axis=1)
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+
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+ return y_pred
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+
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+ def predict(self, X):
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+ y_pred = self.predict_proba(X)
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+
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+ return (y_pred >= 0.5)*1
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