Improved Timeseries Counterfactuals for Glacier
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README.md
base
methods.pynotes_luis
db.sqlite3pipelines
static/js
templates/base
views.py
11
README.md
11
README.md
@ -22,7 +22,7 @@ are RSF and KNN. For counterfactuals and explainability the user can pick the pr
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counterfactual live.
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TODO:
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1) Offer more example entries to pick from
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1) Offer more example entries to pick from (done)
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2) Add more paraterization in training
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3) Compare different methods
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@ -32,9 +32,9 @@ A user can pick from a list of contraint factors, and a predicted margin weight
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because the counterfactuals are computed for all the X_test set which is 50 entries but then only 2 of them (one from each category) can be accessed. Also experiments with specific constraints cannot be accessed not presaved and need to be rerun.
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TODO:
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1) Run experiment first (constraint type, predicted margin weight etc)
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2) Give access to pre computed experiments
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3) Offer more examples entries to pick from
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1) Run experiment first (constraint type, predicted margin weight etc) (done)
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2) Give access to pre computed experiments (done)
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3) Offer more examples entries to pick from (done)
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4) Add more parameterization
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<h1> Import </h1>
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@ -43,4 +43,5 @@ Importing a dataset is available in this version using the dataset selection nav
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TODO:
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1) Navigate between the uploaded datasets
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2) Remove uploaded files
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2) Remove uploaded files
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@ -274,7 +274,7 @@ def compare_values(val1, val2):
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return val1 != val2
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def preprocess(data, value_list, name, class_label, dataset_type, path=None):
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def preprocess(data, value_list, name, dataset_type, path=None, class_label=None):
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if dataset_type == "tabular":
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@ -603,21 +603,21 @@ def training(
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elif model == "wildboar_knn" or model == "wildboar_rsf":
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X = data.iloc[:, :-1].values
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y = data.iloc[:, -1].values
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=test_size, random_state=1
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)
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n_samples, n_timestep = X_train.shape
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y_labels, counts = np.unique(y_train, return_counts=True)
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# n_samples, n_timestep = X_train.shape
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# y_labels, counts = np.unique(y_train, return_counts=True)
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print(
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f"""
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The dataset contains {n_samples} samples with {n_timestep} time steps each.
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Of the samples, {counts[0]} is labeled as {y_labels[0]} and {counts[1]} labeled
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as {y_labels[1]}. Here, we plot the time series.
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"""
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)
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# print(
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# f"""
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# The dataset contains {n_samples} samples with {n_timestep} time steps each.
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# Of the samples, {counts[0]} is labeled as {y_labels[0]} and {counts[1]} labeled
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# as {y_labels[1]}. Here, we plot the time series.
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# """
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# )
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from wildboar.utils.plot import plot_time_domain
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@ -992,7 +992,6 @@ def get_ecg_entry(X_test, y_test, i, class_label):
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# samples subplots
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# Create subplots
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fig = go.Figure()
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y = X_test[y_test == class_label].iloc[i]
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index = X_test[y_test == class_label].index[i]
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@ -1310,3 +1309,28 @@ def extract_arguments_from_line(line):
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# Extract argument values
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arguments = [value.strip() for _, value in matches if value.strip()]
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return arguments
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def create_tuple_of_models_text_value(available_pre_trained_models):
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available_pretrained_models_info = []
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for model in available_pre_trained_models:
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if "xgb" == model:
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available_pretrained_models_text = "XGBoost"
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elif "rf" == model:
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available_pretrained_models_text = "Random Forest"
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elif "lr" == model:
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available_pretrained_models_text = "Logistic Regression"
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elif "dt" == model:
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available_pretrained_models_text = "Decision Tree"
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elif "svm" == model:
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available_pretrained_models_text = "Support Vector Machine"
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elif "glacier" == model:
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available_pretrained_models_text = "Glacier 1dCNN"
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elif "wildboar_knn" == model:
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available_pretrained_models_text = "Wildboar K-Nearest Neighbours"
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elif "wildboar_rsf" == model:
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available_pretrained_models_text = "Wildboar Random Shapelet Forest"
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available_pretrained_models_info.append((model, available_pretrained_models_text))
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return available_pretrained_models_info
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4
base/notes_luis
Normal file
4
base/notes_luis
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@ -0,0 +1,4 @@
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1) combination of dataset+model+features that produce counterfactuals
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2) dataset buttons
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3) Text text text guide
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4) add buttons at the bottom of the pages to promp users
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@ -5,7 +5,7 @@
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"dt": {
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"preprocessing": [
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"std",
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"onehot"
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"imp"
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],
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"test_set_ratio": "0.3",
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"explainability": {
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@ -1,7 +0,0 @@
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radius_mean,texture_mean,perimeter_mean,area_mean,smoothness_mean,compactness_mean,concavity_mean,concave_points_mean,symmetry_mean,fractal_dimension_mean,radius_se,texture_se,perimeter_se,area_se,smoothness_se,compactness_se,concavity_se,concave_points_se,symmetry_se,fractal_dimension_se,radius_worst,texture_worst,perimeter_worst,area_worst,smoothness_worst,compactness_worst,concavity_worst,concave_points_worst,symmetry_worst,fractal_dimension_worst,diagnosis
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1.551486695315854,-0.265203857438333,1.5953362700257374,1.5901687615087483,1.1130075007525315,1.1799004563936333,2.033913908415601,2.0552865433305847,0.7863451938975079,-0.2803467735953654,1.1954712135450982,-0.3063581849282729,0.9533996371233175,1.2482212529580663,-0.9946999661987296,-0.1513566324902152,0.2783957697732808,0.2001444911963527,-0.4458472247069941,-0.1803895275288307,2.166251040678752,-1.5480453,2.014677990932348,2.3756728967997307,0.5011058702831275,0.8291124200476427,1.961282869578782,-1.2548602,1.2007884184088118,0.4590747606013498,0
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1.551486695315854,-0.265203857438333,1.5953362700257374,1.5901687615087483,1.1130075007525315,1.1799004563936333,2.033913908415601,2.0552865433305847,0.7863451938975079,-0.2803467735953654,1.1954712135450982,-0.3063581849282729,0.9533996371233175,1.2482212529580663,-0.9946999661987296,-0.1513566324902152,0.2783957697732808,0.2001444911963527,-0.4458472247069941,-0.1803895275288307,2.166251040678752,-2.0618849,2.014677990932348,2.3756728967997307,0.5011058702831275,0.8291124200476427,1.961282869578782,-1.6275002,1.2007884184088118,0.4590747606013498,0
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1.551486695315854,-0.265203857438333,1.5953362700257374,1.5901687615087483,1.1130075007525315,1.1799004563936333,2.033913908415601,2.0552865433305847,0.7863451938975079,-0.2803467735953654,1.1954712135450982,-0.3063581849282729,0.9533996371233175,1.2482212529580663,-0.9946999661987296,-0.1513566324902152,0.2783957697732808,0.2001444911963527,-0.4458472247069941,-0.1803895275288307,2.166251040678752,-1.9602913,2.014677990932348,2.3756728967997307,0.5011058702831275,0.8291124200476427,1.961282869578782,-1.5538238,1.2007884184088118,0.4590747606013498,0
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1.551486695315854,-0.265203857438333,1.5953362700257374,1.5901687615087483,1.1130075007525315,1.1799004563936333,2.033913908415601,2.0552865433305847,0.7863451938975079,-0.2803467735953654,1.1954712135450982,-0.3063581849282729,0.9533996371233175,1.2482212529580663,-0.9946999661987296,-0.1513566324902152,0.2783957697732808,0.2001444911963527,-0.4458472247069941,-0.1803895275288307,2.166251040678752,-2.1445536,2.014677990932348,2.3756728967997307,0.5011058702831275,0.8291124200476427,1.961282869578782,-1.6874521,1.2007884184088118,0.4590747606013498,0
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1.551486695315854,-0.265203857438333,1.5953362700257374,1.5901687615087483,1.1130075007525315,1.1799004563936333,2.033913908415601,2.0552865433305847,0.7863451938975079,-0.2803467735953654,1.1954712135450982,-0.3063581849282729,0.9533996371233175,1.2482212529580663,-0.9946999661987296,-0.1513566324902152,0.2783957697732808,0.2001444911963527,-0.4458472247069941,-0.1803895275288307,2.166251040678752,-1.6833138,2.014677990932348,2.3756728967997307,0.5011058702831275,0.8291124200476427,1.961282869578782,-1.3529579,1.2007884184088118,0.4590747606013498,0
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1.551486695315854,-0.265203857438333,1.5953362700257374,1.5901687615087483,1.1130075007525315,1.1799004563936333,2.033913908415601,2.0552865433305847,0.7863451938975079,-0.2803467735953654,1.1954712135450982,-0.3063581849282729,0.9533996371233175,1.2482212529580663,-0.9946999661987296,-0.1513566324902152,0.2783957697732808,0.2001444911963527,-0.4458472247069941,-0.1803895275288307,2.166251040678752,-1.8508138,2.014677990932348,2.3756728967997307,0.5011058702831275,0.8291124200476427,1.961282869578782,-1.4744301,1.2007884184088118,0.4590747606013498,0
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base/pipelines/breast-cancer/trained_models/dt/imp.sav
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base/pipelines/breast-cancer/trained_models/dt/imp.sav
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@ -1,7 +1,7 @@
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age,hypertension,heart_disease,avg_glucose_level,bmi,gender_Female,gender_Male,gender_Other,ever_married_No,ever_married_Yes,work_type_Govt_job,work_type_Never_worked,work_type_Private,work_type_Self-employed,work_type_children,Residence_type_Rural,Residence_type_Urban,smoking_status_Unknown,smoking_status_formerly smoked,smoking_status_never smoked,smoking_status_smokes,stroke
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1.5379353389188812,1.0,0.0,-0.90953787,-1.1423846541919351,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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1.5379353389188812,1.0,0.0,0.5387100724827014,5.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0
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1.5379353389188812,1.0,0.0,0.5387100724827014,5.9,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0
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1.5379353389188812,1.0,0.0,0.5387100724827014,1.9,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0
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1.5379353389188812,1.0,0.0,1.71995584,4.4,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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1.5379353389188812,1.0,0.0,-0.95210065,-1.1423846541919351,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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0.3391861,1.0,0.0,0.5387100724827014,-1.1423846541919351,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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1.5379353389188812,1.0,0.0,-1.0008157,-1.1423846541919351,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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1.5379353389188812,1.0,0.0,0.5387100724827014,4.5,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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0.8089252,1.0,0.0,0.5387100724827014,-1.1423846541919351,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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1.5379353389188812,1.0,0.0,2.36068705,5.9,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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-0.3085493,1.0,0.0,0.5387100724827014,-1.1423846541919351,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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@ -1,14 +1,36 @@
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{
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"name": "two-lead-ecg",
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"preprocessed_name": "two-lead-ecg_preprocessed.csv",
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"classifier": {
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"glacier": {
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"autoencoder": "Yes",
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"explainability": {
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"technique": "glacier"
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},
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"pos": "1",
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"neg": "2"
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"name": "two-lead-ecg",
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"preprocessed_name": "two-lead-ecg_preprocessed.csv",
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"classifier": {
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"wildboar_knn": {
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"preprocessing": [
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"std",
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"imp"
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],
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"test_set_ratio": "0.3",
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"explainability": {
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"technique": "wildboar_knn"
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},
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"pos": "1",
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"neg": "2"
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},
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"glacier": {
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"autoencoder": "Yes",
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"explainability": {
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"technique": "glacier"
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},
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"pos": "1",
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"neg": "2",
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"experiments": {
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"0": {
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"constraint": "unconstrained",
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"w_value": "1"
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},
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"1": {
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"constraint": "local",
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"w_value": "0.5"
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}
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}
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}
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}
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}
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}
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@ -0,0 +1,50 @@
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0.2042720257886117,-0.032707889633314835,0.03759197611969256,0.03394244808070823,0.006008606930762992,0.0956712380811311,0.1145813885232485,0.17108086468614658,0.18696755927287967,0.2238007198887928,0.22889030429236357,0.1962785385151733,0.18061860311759403,0.11757984967385227,0.0690350135926252,0.05208258093097129,0.03386324799206619,0.01914618513265509,0.002659316490166928,-0.040605614061931306,-0.05913090627863227,-0.08218867200921665,-0.12359435789486639,-0.1989600179107498,-0.22794935834088648,-0.2896733300849661,-0.23082706194188773,-0.17936627734993493,-0.14707944273489204,-0.18527376076777102,-0.027949259212320502,-0.3224666838980923,-0.41004541918000825,-1.0251855814192368,-2.106219361672199,-2.509313031072203,-2.7365777230731636,-2.739580964837483,-2.3958769725537685,-2.2618789298052935,-2.042217408671341,-1.8630229921444261,-1.7305748469481124,-1.452145881333447,-1.3450011104646897,-1.0217829890178436,-0.777094917440305,-0.4280431123271242,-0.24041509092391777,0.09027659782282882,0.27566021519314887,0.491861098993823,0.5977368615981788,0.6829913904765235,0.7056263542129884,0.7723390281184521,0.7829120893816537,0.820935660455369,0.8596346908402026,0.9150630083897937,0.9455716052731589,1.013924594322475,1.0701976124372408,1.1428936572979467,1.180707634220731,1.2220293017613955,1.254098411417928,1.3125203718617797,1.3337369613872028,1.3282194555921478,1.3056877832260412,1.199112470028851,1.1458044864113077,0.9755417620101667,0.9085978118002274,0.7662722411002676,0.7250297736482452,0.30172517516395914,0.6117495556084123,0.2477570904273918,0.34285893070723716,0.0552249874898294,0
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|
||||
|
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base/pipelines/two-lead-ecg/trained_models/wildboar_knn/pca.sav
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base/pipelines/two-lead-ecg/trained_models/wildboar_knn/tsne.sav
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@ -6,25 +6,42 @@ $(document).ready(function () {
|
||||
if (document.getElementById("classifier")) {
|
||||
document.getElementById("classifier").addEventListener("change", function (e) {
|
||||
var classifier = document.getElementById("classifier").value
|
||||
|
||||
if (classifier == "wildboar_knn" || classifier == "wildboar_rsf") {
|
||||
// Loop through each checkbox and disable it
|
||||
$("#pre_computed_counterfactuals").hide()
|
||||
$("#constraint_div").hide()
|
||||
$("#w_value_div").hide()
|
||||
$("#class_label_container").show()
|
||||
$("#pre_computed_counterfactuals").hide()
|
||||
$("#entries_container_glacier").hide()
|
||||
$("#cfbtn_container_2").hide()
|
||||
}
|
||||
|
||||
if (classifier == "glacier") {
|
||||
// Loop through each checkbox and disable it
|
||||
$("#constraint_div").show()
|
||||
$("#w_value_div").show()
|
||||
$("#cfbtn_container_2").show()
|
||||
$("#pre_computed_counterfactuals").show()
|
||||
$("#entries_container").hide()
|
||||
$("#class_label_container").show()
|
||||
}
|
||||
|
||||
});
|
||||
}
|
||||
|
||||
$('#class_label_container').change(function () {
|
||||
$("#entries_container").show()
|
||||
});
|
||||
|
||||
$('#pre_computed_counterfactuals').change(function () {
|
||||
$("#entries_container_glacier").show()
|
||||
});
|
||||
|
||||
$('#entries').change(function () {
|
||||
var class_label = $("input:radio[name=class_label]:checked").val();
|
||||
var csrftoken = jQuery("[name=csrfmiddlewaretoken]").val();
|
||||
var cfrow_id = 0;
|
||||
var cfrow_id = document.getElementById("entries").value;
|
||||
var model_name = "";
|
||||
if (document.getElementById("classifier")) {
|
||||
// class_label = $("input:radio[name=class_label]:checked").val();
|
||||
@ -67,6 +84,82 @@ $(document).ready(function () {
|
||||
});
|
||||
});
|
||||
|
||||
|
||||
$('#entries_glacier').change(function () {
|
||||
var experiment_id = $("input:radio[name=experiment]:checked").val();
|
||||
var csrftoken = jQuery("[name=csrfmiddlewaretoken]").val();
|
||||
var cfrow_id = document.getElementById("entries_glacier").value;
|
||||
var model_name = "";
|
||||
if (document.getElementById("classifier")) {
|
||||
class_label = $("input:radio[name=class_label]:checked").val();
|
||||
model_name = document.getElementById("classifier").value
|
||||
}
|
||||
|
||||
$("#ecg_data_container").hide();
|
||||
if ($("#ecg_data_example")) {
|
||||
$("#ecg_data_example").remove()
|
||||
}
|
||||
|
||||
if ($("#ecg_cf")) {
|
||||
$("#ecg_cf").remove();
|
||||
$("#cf_ecg_container").hide();
|
||||
}
|
||||
|
||||
$("#class_label_loader").show();
|
||||
$.ajax({
|
||||
method: 'POST',
|
||||
url: '',
|
||||
headers: { 'X-CSRFToken': csrftoken, },
|
||||
data: { 'action': "class_label_selection", 'class_label': class_label, 'cfrow_id': cfrow_id, "model_name": model_name, "experiment_id": experiment_id },
|
||||
success: function (ret) {
|
||||
$("#class_label_loader").hide();
|
||||
var ret = JSON.parse(ret)
|
||||
var fig = ret["fig"]
|
||||
|
||||
// create div elements for plots
|
||||
var ecg_data_div = create_div("ecg_data_example", "plotly_fig")
|
||||
ecg_data_div.insertAdjacentHTML('beforeend', fig);
|
||||
|
||||
$("#ecg_data").append(ecg_data_div);
|
||||
$("#ecg_data_container").show();
|
||||
$("#cfbtn_container").show()
|
||||
$("#cfbtn").show()
|
||||
},
|
||||
error: function (ret) {
|
||||
console.log(":(")
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
$('.compute_counterfactual').click(function () {
|
||||
// only for glacier
|
||||
var csrftoken = jQuery("[name=csrfmiddlewaretoken]").val();
|
||||
var data_to_pass = {}
|
||||
var constraint = ""
|
||||
var w_value = ""
|
||||
var model_name = ""
|
||||
model_name = document.getElementById("classifier").value
|
||||
if (model_name == "glacier") {
|
||||
w_value = document.getElementById("slider").value
|
||||
constraint = document.getElementById("constraint").value
|
||||
}
|
||||
|
||||
data_to_pass = { "action": 'compute_cf', "constraint": constraint, 'w_value': w_value, 'model_name': model_name }
|
||||
$("#cfbtn_2").hide()
|
||||
$("#cfbtn_loader_2").show()
|
||||
$.ajax({
|
||||
method: 'POST',
|
||||
url: '',
|
||||
headers: { 'X-CSRFToken': csrftoken },
|
||||
data: data_to_pass,
|
||||
processData: true, // This should be `true` for form data
|
||||
contentType: 'application/x-www-form-urlencoded; charset=UTF-8', // Standard form content type
|
||||
success: function (ret) {
|
||||
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
$('.run_counterfactual').click(function () {
|
||||
// on click compute counterfactuals for respective point
|
||||
var csrftoken = jQuery("[name=csrfmiddlewaretoken]").val();
|
||||
@ -77,10 +170,8 @@ $(document).ready(function () {
|
||||
var features_to_vary = []
|
||||
if (document.getElementById("classifier")) {
|
||||
model_name = document.getElementById("classifier").value
|
||||
if (model_name == "glacier") {
|
||||
w_value = document.getElementById("slider").value
|
||||
constraint = document.getElementById("constraint").value
|
||||
}
|
||||
constraint = $("input:radio[name=experiment]:checked").val();
|
||||
alert(constraint)
|
||||
data_to_pass = { 'action': "cf", "constraint": constraint, "w_value": w_value, "model_name": model_name }
|
||||
} else {
|
||||
model_name = $("input:radio[name=modeling_options]:checked").val();
|
||||
@ -195,7 +286,8 @@ $(document).ready(function () {
|
||||
// $("#original_data").append(cp_tb)
|
||||
// $("#counterfactual").append(cf_tb);
|
||||
$("#og_cf_row").show()
|
||||
} else if (dataset_type == "timeseries") {
|
||||
}
|
||||
else if (dataset_type == "timeseries") {
|
||||
// ecg
|
||||
$("#cfbtn_container").show();
|
||||
$("#cfbtn").show()
|
||||
|
||||
@ -38,7 +38,7 @@ $(document).ready(function () {
|
||||
var data_to_pass = {}
|
||||
if (classifier != "wildboar_knn" && classifier != "wildboar_rsf" && classifier != "glacier") {
|
||||
class_label = document.getElementById("class_label_train").value
|
||||
test_set_ratio = document.getElementById("test_set_ratio").value
|
||||
test_set_ratio = document.getElementById("slider").value
|
||||
document.getElementsByName("boxes").forEach(function (elem) {
|
||||
if (elem.checked == true) {
|
||||
array_preprocessing.push(elem.value);
|
||||
@ -51,7 +51,7 @@ $(document).ready(function () {
|
||||
// TODO: maybe add test set ratio
|
||||
data_to_pass = { 'action': "train", 'model_name': classifier, 'autoencoder': autoencoder }
|
||||
} else if (classifier == "wildboar_knn" || classifier == "wildboar_rsf") {
|
||||
test_set_ratio = document.getElementById("test_set_ratio").value
|
||||
test_set_ratio = document.getElementById("slider").value
|
||||
document.getElementsByName("boxes").forEach(function (elem) {
|
||||
if (elem.checked == true) {
|
||||
array_preprocessing.push(elem.value);
|
||||
@ -70,9 +70,6 @@ $(document).ready(function () {
|
||||
contentType: 'application/x-www-form-urlencoded; charset=UTF-8', // Standard form content type,
|
||||
success: function (values) {
|
||||
$("#loader_train").hide();
|
||||
$("#preprocessing").hide();
|
||||
$("#figs").show()
|
||||
$("#counterfactuals").show()
|
||||
},
|
||||
error: function (ret) {
|
||||
}
|
||||
|
||||
@ -16,34 +16,34 @@
|
||||
</div>
|
||||
{% endif %}
|
||||
{% if dataset_type == "tabular" %}
|
||||
<div class="row">
|
||||
<div class="col-xl-4 col-lg-4">
|
||||
<div class="card shadow mb-4">
|
||||
<div class="card-header py-3 d-flex flex-row align-items-center justify-content-between">
|
||||
<h1 class="h3 mb-0 text-gray-800">Available pre trained models</h1>
|
||||
</div>
|
||||
<div class="row">
|
||||
<div class="col-xl-4 col-lg-4">
|
||||
<div class="card shadow mb-4">
|
||||
<div class="card-header py-3 d-flex flex-row align-items-center justify-content-between">
|
||||
<h1 class="h3 mb-0 text-gray-800">Available pre trained models</h1>
|
||||
</div>
|
||||
|
||||
{% csrf_token %}
|
||||
{% csrf_token %}
|
||||
|
||||
<div class="card-body">
|
||||
<div id="pre_trained_models">
|
||||
{% if not df_name %}
|
||||
<p>The available pre trained models will show up here. You first need to <a href="/">pick or upload a dataset</a></p>
|
||||
{% elif not available_pretrained_models_info %}
|
||||
<p>There are no available pre trained models for <b>{{df_name}}</b> dataset. You first need to <a href="/train.html">train a model</a>.</p>
|
||||
{% else %}
|
||||
{% for value, text in available_pretrained_models_info %}
|
||||
<div class="form-check">
|
||||
<input class="form-check-input" type="radio" name="modeling_options" value={{value}}>
|
||||
<label class="form-check-label">{{text}}</label>
|
||||
</div>
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
<div class="card-body">
|
||||
<div id="pre_trained_models">
|
||||
{% if not df_name %}
|
||||
<p>The available pre trained models will show up here. You first need to <a href="/">pick or upload a dataset</a></p>
|
||||
{% elif not available_pretrained_models_info %}
|
||||
<p>There are no available pre trained models for <b>{{df_name}}</b> dataset. You first need to <a href="/train.html">train a model</a>.</p>
|
||||
{% else %}
|
||||
{% for value, text in available_pretrained_models_info %}
|
||||
<div class="form-check">
|
||||
<input class="form-check-input" type="radio" name="modeling_options" value={{value}}>
|
||||
<label class="form-check-label">{{text}}</label>
|
||||
</div>
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{% csrf_token %}
|
||||
<div class="row" id="counterfactuals" style="display:none;">
|
||||
@ -161,7 +161,7 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row justify-content-center" id="og_cf_headers" style="display: none; padding-top: 30px;">
|
||||
<div class="row justify-content-center" id="og_cf_headers" style="display: none; padding-top: 30px;">
|
||||
<div class="col-xl-3 col-lg-3">
|
||||
<div id="cf_selection">
|
||||
|
||||
@ -169,45 +169,7 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- <span style="display: none; width: 48px; height: 48px;" id="reset_div">
|
||||
</span> -->
|
||||
|
||||
<div class="row justify-content-center" id="og_cf_row" style="display:none; padding-top: 20px;">
|
||||
<div class="col-xl-3 col-lg-3">
|
||||
<div class="card shadow mb-4">
|
||||
<div class="card-header py-3 d-flex flex-row align-items-center justify-content-between">
|
||||
<h6 class="m-0 font-weight-bold text-primary">Original values data point </h6>
|
||||
</div>
|
||||
<div class="card-body">
|
||||
|
||||
<div class="scrollit d-flex justify-content-center table-responsive table table-bordered" id="original_data">
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="col-xl-3 col-lg-3" >
|
||||
<div class="card shadow mb-4">
|
||||
<!-- Card Header - Dropdown -->
|
||||
<div
|
||||
class="card-header py-3 d-flex flex-row align-items-center justify-content-between">
|
||||
<h6 class="m-0 font-weight-bold text-primary">Counterfactual values</h6>
|
||||
</div>
|
||||
<!-- Card Body -->
|
||||
<div class="card-body">
|
||||
<!-- <div class="col d-flex justify-content-center">
|
||||
<span class="loader" id="loader_stats" style="display: none;">
|
||||
</span>
|
||||
</div> -->
|
||||
|
||||
<div class="scrollit d-flex justify-content-center table-responsive table table-bordered" id="counterfactual">
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{% elif dataset_type == "timeseries" %}
|
||||
|
||||
@ -229,7 +191,7 @@
|
||||
<!-- Card Body -->
|
||||
<div class="card-body">
|
||||
<select id="classifier" class="col mr-2 selectpicker form-control">
|
||||
<option type="submit" value="" disabled selected hidden >Pick a pre trained classifier</option>
|
||||
<option value="" disabled selected hidden >Pick a pre trained classifier</option>
|
||||
{% for value, text in available_pretrained_models_info %}
|
||||
<option type="submit" value="{{value}}">{{text}}</option>
|
||||
{% endfor %}
|
||||
@ -257,9 +219,17 @@
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row" id="cfbtn_container_2" style="display: none;">
|
||||
<div class="col-sm d-flex justify-content-center">
|
||||
<span class="loader" id="cfbtn_loader_2" style="display:none;">
|
||||
</span>
|
||||
<button class="button-6 compute_counterfactual" id="cfbtn_2" role="button" name="cf">Compute Counterfactuals!</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="col-xl-3 col-lg-3">
|
||||
<div id="class_label_container" class="col-xl-3 col-lg-3" style="display:none;">
|
||||
<div class="card shadow mb-4">
|
||||
<!-- Card Header - Dropdown -->
|
||||
<div
|
||||
@ -267,34 +237,44 @@
|
||||
<h6 class="m-0 font-weight-bold text-primary">Class label</h6>
|
||||
</div>
|
||||
<!-- Card Body -->
|
||||
<div class="card-body" id="class_label_container">
|
||||
<div class="card-body">
|
||||
{% for target_label_value, target_label_text in target_label_info %}
|
||||
<div class="form-check">
|
||||
<input class="form-check-input" type="radio" name="class_label" value="{{target_label_value}}">
|
||||
<label class="form-check-label">{{target_label_text}} ({{target_label_value}})</label>
|
||||
</div>
|
||||
{% endfor %}
|
||||
|
||||
<div id="pre_computed_counterfactuals" style="display:none;">
|
||||
<div class="py-3 d-flex flex-row align-items-center justify-content-between">
|
||||
<h6 class="m-0 font-weight-bold text-primary">Pre computed counterfactuals</h6>
|
||||
</div>
|
||||
<!-- Card Body -->
|
||||
{% for experiment, details in available_pre_computed_counterfactuals %}
|
||||
<div class="form-check">
|
||||
<input class="form-check-input" type="radio" name="experiment" value="{{experiment}}">
|
||||
<label class="form-check-label">{{experiment}} ({{details}})</label>
|
||||
</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
|
||||
<div id="entries_container" style="display:none;">
|
||||
<div
|
||||
class="py-3 d-flex flex-row align-items-center justify-content-between">
|
||||
<h6 class="m-0 font-weight-bold text-primary">Example entries</h6>
|
||||
</div>
|
||||
<select id="entries" class="col mr-2 selectpicker form-control .animated--fade-in">
|
||||
<option type="submit" value="" disabled selected hidden> Select a timeseries entry</option>
|
||||
{% for id in range %}
|
||||
<option type="submit" value={{id}}>Timeseries {{id}}</option>
|
||||
{% endfor %}
|
||||
</select>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<!-- <div class="row">
|
||||
<div class="col-xl-4 col-xl-4" id="constraint_div" style="display:none;">
|
||||
<div class="card shadow mb-4">
|
||||
<div
|
||||
class="py-3 d-flex flex-row align-items-center justify-content-between">
|
||||
<h6 class="m-0 font-weight-bold text-primary">Constraint</h6>
|
||||
</div>
|
||||
<select id="constraint" class="col mr-2 selectpicker form-control .animated--fade-in">
|
||||
<option type="submit" value="" disabled selected hidden>Select the constraints of the counterfactuals</option>
|
||||
<option type="submit" value="unconstrained">Unconstrained</option>
|
||||
<option type="submit" value="local">Local</option>
|
||||
<option type="submit" value="global">Global</option>
|
||||
<option type="submit" value="uniform">Uniform</option>
|
||||
</select>
|
||||
</div>
|
||||
</div>
|
||||
</div> -->
|
||||
|
||||
<div class="row">
|
||||
<div class="col-xl-6 col-lg-6 d-flex justify-content-center">
|
||||
<span class="loader" id="class_label_loader" style="display: none;">
|
||||
|
||||
352
base/views.py
352
base/views.py
@ -215,7 +215,6 @@ def counterfactuals(request):
|
||||
"available_pretrained_models_info": available_pretrained_models_info,
|
||||
}
|
||||
else:
|
||||
json_path = os.path.join(PIPELINE_PATH + f"{df_name}" + "/pipeline.json")
|
||||
# if it does not exist, obviously there are no pre trained
|
||||
# models
|
||||
available_pretrained_models = []
|
||||
@ -230,34 +229,10 @@ def counterfactuals(request):
|
||||
pipeline_json = json.load(jsonFile) # data becomes a dictionary
|
||||
jsonFile.close() # Close the JSON file
|
||||
available_pretrained_models = pipeline_json["classifier"].keys()
|
||||
|
||||
available_pretrained_models_text = []
|
||||
for model in available_pretrained_models:
|
||||
if "xgb" == model:
|
||||
available_pretrained_models_text.append("XGBoost")
|
||||
elif "rf" == model:
|
||||
available_pretrained_models_text.append("Random Forest")
|
||||
elif "lr" == model:
|
||||
available_pretrained_models_text.append("Logistic Regression")
|
||||
elif "dt" == model:
|
||||
available_pretrained_models_text.append("Decision Tree")
|
||||
elif "svm" == model:
|
||||
available_pretrained_models_text.append(
|
||||
"Support Vector Machine"
|
||||
)
|
||||
elif "glacier" == model:
|
||||
available_pretrained_models_text.append("Glacier 1dCNN")
|
||||
elif "wildboar_knn" == model:
|
||||
available_pretrained_models_text.append(
|
||||
"Wildboar K-Nearest Neighbours"
|
||||
)
|
||||
elif "wildboar_rsf" == model:
|
||||
available_pretrained_models_text.append(
|
||||
"Wildboar Random Shapelet Forest"
|
||||
)
|
||||
|
||||
available_pretrained_models_info = zip(
|
||||
available_pretrained_models, available_pretrained_models_text
|
||||
available_pretrained_models_info = (
|
||||
methods.create_tuple_of_models_text_value(
|
||||
available_pretrained_models
|
||||
)
|
||||
)
|
||||
|
||||
## get the type of the active dataset
|
||||
@ -271,11 +246,6 @@ def counterfactuals(request):
|
||||
dataset_type = datasets_types_pipeline_json[df_name]
|
||||
if dataset_type == "tabular":
|
||||
|
||||
excel_file_name_path = os.path.join(
|
||||
PIPELINE_PATH + f"{df_name}" + "/" + df_name + ".csv"
|
||||
)
|
||||
df = methods.get_dataframe(excel_file_name_path)
|
||||
|
||||
# pipeline path
|
||||
json_path = os.path.join(PIPELINE_PATH, f"{df_name}" + "/pipeline.json")
|
||||
|
||||
@ -295,43 +265,39 @@ def counterfactuals(request):
|
||||
}
|
||||
elif dataset_type == "timeseries":
|
||||
|
||||
available_pretrained_models_text = []
|
||||
|
||||
if "wildboar_knn" in available_pretrained_models:
|
||||
available_pretrained_models_text.append(
|
||||
"Wildboar K-Nearest Neighbour"
|
||||
available_pretrained_models_info = (
|
||||
methods.create_tuple_of_models_text_value(
|
||||
available_pretrained_models
|
||||
)
|
||||
if "wildboar_rsf" in available_pretrained_models:
|
||||
available_pretrained_models_text.append(
|
||||
"Wildboar Random Shapelet Forest"
|
||||
)
|
||||
if "glacier" in available_pretrained_models:
|
||||
available_pretrained_models_text.append("Glacier 1dCNN")
|
||||
|
||||
available_pretrained_models_info = zip(
|
||||
available_pretrained_models, available_pretrained_models_text
|
||||
)
|
||||
|
||||
# excel_file_name_path = os.path.join(
|
||||
# PIPELINE_PATH + f"{df_name}" + "/" + df_name + ".csv"
|
||||
# )
|
||||
# df = methods.get_dataframe(excel_file_name_path)
|
||||
# df.columns = df.columns.str.replace(" ", "_")
|
||||
|
||||
# target_labels = list(df.iloc[:, -1].unique())
|
||||
|
||||
positive = 1
|
||||
negative = 0
|
||||
|
||||
target_label_value = [negative, positive]
|
||||
target_label_text = ["Negative class label", "Positive class label"]
|
||||
target_label_info = zip(target_label_value, target_label_text)
|
||||
available_pre_computed_counterfactuals = []
|
||||
jsonFile = open(json_path, "r")
|
||||
pipeline_json = json.load(jsonFile) # data becomes a dictionary
|
||||
|
||||
if "experiments" in pipeline_json["classifier"]["glacier"]:
|
||||
# applies only to glacier
|
||||
# there are pre computed counterfactuals
|
||||
experiments_dict = pipeline_json["classifier"]["glacier"][
|
||||
"experiments"
|
||||
]
|
||||
list_of_experiment_keys = list(experiments_dict.keys())
|
||||
for key in list_of_experiment_keys:
|
||||
available_pre_computed_counterfactuals.append((experiments_dict[key]["constraint"], experiments_dict[key]))
|
||||
|
||||
context = {
|
||||
"df_name": df_name,
|
||||
"dataset_type": dataset_type,
|
||||
"available_pretrained_models_info": available_pretrained_models_info,
|
||||
"range": range(1, 6),
|
||||
"target_label_info": target_label_info,
|
||||
"available_pre_computed_counterfactuals": available_pre_computed_counterfactuals,
|
||||
}
|
||||
else:
|
||||
context = {}
|
||||
@ -466,32 +432,9 @@ def charts(request):
|
||||
pipeline_json = json.load(jsonFile) # data becomes a dictionary
|
||||
jsonFile.close() # Close the JSON file
|
||||
available_pretrained_models = pipeline_json["classifier"].keys()
|
||||
available_pretrained_models_text = []
|
||||
|
||||
for model in available_pretrained_models:
|
||||
if "xgb" == model:
|
||||
available_pretrained_models_text.append("XGBoost")
|
||||
elif "rf" == model:
|
||||
available_pretrained_models_text.append("Random Forest")
|
||||
elif "lr" == model:
|
||||
available_pretrained_models_text.append("Logistic Regression")
|
||||
elif "dt" == model:
|
||||
available_pretrained_models_text.append("Decision Tree")
|
||||
elif "svm" == model:
|
||||
available_pretrained_models_text.append("Support Vector Machine")
|
||||
elif "glacier" == model:
|
||||
available_pretrained_models_text.append("Glacier 1dCNN")
|
||||
elif "wildboar_knn" == model:
|
||||
available_pretrained_models_text.append(
|
||||
"Wildboar K-Nearest Neighbours"
|
||||
)
|
||||
elif "wildboar_rsf" == model:
|
||||
available_pretrained_models_text.append(
|
||||
"Wildboar Random Shapelet Forest"
|
||||
)
|
||||
|
||||
available_pretrained_models_info = zip(
|
||||
available_pretrained_models, available_pretrained_models_text
|
||||
available_pretrained_models_info = (
|
||||
methods.create_tuple_of_models_text_value(available_pretrained_models)
|
||||
)
|
||||
context = {
|
||||
"df_name": df_name,
|
||||
@ -894,42 +837,34 @@ def ajax_requests(action, request):
|
||||
else:
|
||||
# Path to the Bash script
|
||||
autoencoder = request.POST.get("autoencoder")
|
||||
dataset_camel = methods.convert_to_camel_case(df_name)
|
||||
if "Ecg" in dataset_camel:
|
||||
dataset_camel = dataset_camel.replace("Ecg", "ECG")
|
||||
preprocessing_info = {
|
||||
"autoencoder": autoencoder,
|
||||
"explainability": {"technique": model_name},
|
||||
}
|
||||
|
||||
experiment = methods.fetch_line_by_dataset_and_constraint(
|
||||
PIPELINE_PATH + "/glacier_experiments.txt",
|
||||
dataset_camel,
|
||||
# constraint_type,
|
||||
)
|
||||
if experiment is not None:
|
||||
stripped_arguments = methods.extract_arguments_from_line(experiment)
|
||||
preprocessing_info = {
|
||||
"autoencoder": autoencoder,
|
||||
"explainability": {"technique": model_name},
|
||||
}
|
||||
dataset_camel = methods.convert_to_camel_case(df_name)
|
||||
if "Ecg" in dataset_camel:
|
||||
dataset_camel = dataset_camel.replace("Ecg", "ECG")
|
||||
|
||||
indices_to_keys = {
|
||||
1: "pos",
|
||||
2: "neg",
|
||||
# 4: "n-lstmcells",
|
||||
# 5: "constraint",
|
||||
# 6: "w-type",
|
||||
# 7: "tau-value",
|
||||
# 8: "lr-list",
|
||||
}
|
||||
experiment = methods.fetch_line_by_dataset_and_constraint(
|
||||
PIPELINE_PATH + "/glacier_experiments.txt",
|
||||
dataset_camel,
|
||||
)
|
||||
|
||||
# Create a dictionary by fetching items from the list at the specified indices
|
||||
inner_dict = {
|
||||
key: stripped_arguments[index]
|
||||
for index, key in indices_to_keys.items()
|
||||
}
|
||||
if experiment is not None:
|
||||
stripped_arguments = methods.extract_arguments_from_line(experiment)
|
||||
|
||||
preprocessing_info.update(inner_dict)
|
||||
else:
|
||||
raise ("Error")
|
||||
class_label = ""
|
||||
indices_to_keys = {
|
||||
1: "pos",
|
||||
2: "neg",
|
||||
}
|
||||
|
||||
# Create a dictionary by fetching items from the list at the specified indices
|
||||
inner_dict = {
|
||||
key: stripped_arguments[index] for index, key in indices_to_keys.items()
|
||||
}
|
||||
|
||||
preprocessing_info.update(inner_dict)
|
||||
|
||||
# absolute excel_file_name_path
|
||||
excel_file_name_path = os.path.join(
|
||||
@ -1007,22 +942,47 @@ def ajax_requests(action, request):
|
||||
preprocess_df[class_label] = le.fit_transform(preprocess_df[class_label])
|
||||
pickle.dump(le, open(model_name_path + "/label_encoder.sav", "wb"))
|
||||
|
||||
if "array_preprocessing" in request.POST:
|
||||
preprocess_df = methods.preprocess(
|
||||
preprocess_df,
|
||||
array_preprocessing,
|
||||
excel_file_name_path,
|
||||
class_label,
|
||||
dataset_type,
|
||||
model_name_path,
|
||||
)
|
||||
if "array_preprocessing" in request.POST:
|
||||
preprocess_df = methods.preprocess(
|
||||
preprocess_df,
|
||||
array_preprocessing,
|
||||
excel_file_name_path,
|
||||
dataset_type,
|
||||
model_name_path,
|
||||
class_label,
|
||||
)
|
||||
elif dataset_type == "timeseries":
|
||||
|
||||
pos = inner_dict["pos"]
|
||||
neg = inner_dict["neg"]
|
||||
pos_label, neg_label = 1, 0
|
||||
if pos != pos_label:
|
||||
preprocess_df.iloc[:, -1] = preprocess_df.iloc[:, -1].apply(
|
||||
lambda x: pos_label if x == int(pos) else x
|
||||
)
|
||||
if neg != neg_label:
|
||||
preprocess_df.iloc[:, -1] = preprocess_df.iloc[:, -1].apply(
|
||||
lambda x: neg_label if x == int(neg) else x
|
||||
)
|
||||
|
||||
if "array_preprocessing" in request.POST:
|
||||
preprocess_df = methods.preprocess(
|
||||
preprocess_df,
|
||||
array_preprocessing,
|
||||
excel_file_name_path,
|
||||
dataset_type,
|
||||
model_name_path,
|
||||
)
|
||||
|
||||
# PCA
|
||||
pca = methods.generatePCA(preprocess_df)
|
||||
# TSNE
|
||||
tsne, projections = methods.generateTSNE(
|
||||
preprocess_df, dataset_type, class_label
|
||||
)
|
||||
if dataset_type == "tabular":
|
||||
tsne, projections = methods.generateTSNE(
|
||||
preprocess_df, dataset_type, class_label
|
||||
)
|
||||
else:
|
||||
tsne, projections = methods.generateTSNE(preprocess_df, dataset_type)
|
||||
|
||||
# save the plots
|
||||
pickle.dump(tsne, open(model_name_path + "/tsne.sav", "wb"))
|
||||
@ -1136,17 +1096,28 @@ def ajax_requests(action, request):
|
||||
# path = model_name_path_type_dir
|
||||
# else:
|
||||
path = model_name_path
|
||||
classification_report = methods.training(
|
||||
preprocess_df,
|
||||
model_name,
|
||||
float(test_set_ratio) if test_set_ratio != "" else 0,
|
||||
"",
|
||||
dataset_type,
|
||||
df_name,
|
||||
path,
|
||||
autoencoder,
|
||||
stripped_arguments,
|
||||
)
|
||||
if model_name == "glacier":
|
||||
classification_report = methods.training(
|
||||
preprocess_df,
|
||||
model_name,
|
||||
float(test_set_ratio) if test_set_ratio != "" else 0,
|
||||
"",
|
||||
dataset_type,
|
||||
df_name,
|
||||
path,
|
||||
autoencoder,
|
||||
stripped_arguments,
|
||||
)
|
||||
else:
|
||||
classification_report = methods.training(
|
||||
preprocess_df,
|
||||
model_name,
|
||||
float(test_set_ratio) if test_set_ratio != "" else 0,
|
||||
"",
|
||||
dataset_type,
|
||||
df_name,
|
||||
path,
|
||||
)
|
||||
|
||||
pickle.dump(
|
||||
classification_report,
|
||||
@ -1260,8 +1231,6 @@ def ajax_requests(action, request):
|
||||
else:
|
||||
df = pd.read_csv(excel_file_name_path)
|
||||
|
||||
df_id = request.session.get("cfrow_id")
|
||||
|
||||
datasets_types_pipeline_json_path = os.path.join(
|
||||
PIPELINE_PATH + "/dataset_types_pipeline.json"
|
||||
)
|
||||
@ -1270,11 +1239,12 @@ def ajax_requests(action, request):
|
||||
jsonFile
|
||||
) # data becomes a dictionary
|
||||
dataset_type = datasets_types_pipeline_json[df_name]
|
||||
print(dataset_type)
|
||||
|
||||
df_id = request.session.get("cfrow_id")
|
||||
if dataset_type == "tabular":
|
||||
|
||||
# get row
|
||||
features_to_vary = json.loads(request.POST.get("features_to_vary"))
|
||||
|
||||
row = df.iloc[[int(df_id)]]
|
||||
|
||||
# not preprocessed
|
||||
@ -1284,7 +1254,6 @@ def ajax_requests(action, request):
|
||||
# if feature_to_vary has a categorical column then I cannot just
|
||||
# pass that to dice since the trained model does not contain the
|
||||
# categorical column but the one-hot-encoded sub-columns
|
||||
|
||||
features_to_vary = methods.update_column_list_with_one_hot_columns(
|
||||
notpre_df, df, features_to_vary
|
||||
)
|
||||
@ -1321,7 +1290,6 @@ def ajax_requests(action, request):
|
||||
)
|
||||
|
||||
if counterfactuals:
|
||||
|
||||
cf_df = counterfactuals[0].final_cfs_df
|
||||
counterfactuals[0].final_cfs_df.to_csv(
|
||||
model_name_path + "counterfactuals.csv", index=False
|
||||
@ -1371,43 +1339,15 @@ def ajax_requests(action, request):
|
||||
elif dataset_type == "timeseries":
|
||||
model_name = request.POST["model_name"]
|
||||
model_name_path = os.path.join(
|
||||
PIPELINE_PATH + f"{df_name}/" + "trained_models/" + f"{model_name}/"
|
||||
)
|
||||
PIPELINE_PATH + f"{df_name}/" + "trained_models/" + f"{model_name}/"
|
||||
)
|
||||
path = model_name_path
|
||||
if model_name == "glacier":
|
||||
constraint_type = request.POST.get("constraint")
|
||||
print(constraint_type)
|
||||
w_value = request.POST.get("w_value")
|
||||
model_name_path_constraint = model_name_path + f"{constraint_type}/"
|
||||
if not os.path.exists(model_name_path_constraint):
|
||||
os.makedirs(model_name_path_constraint)
|
||||
# https://github.com/wildboar-foundation/wildboar/blob/master/docs/guide/explain/counterfactuals.rst#id27
|
||||
classifier = joblib.load(model_name_path + "/classifier.sav")
|
||||
|
||||
# pipeline path
|
||||
json_path = os.path.join(PIPELINE_PATH, f"{df_name}" + "/pipeline.json")
|
||||
# load pipeline data
|
||||
jsonFile = open(json_path, "r")
|
||||
pipeline_data = json.load(jsonFile) # data becomes a dictionary
|
||||
autoencoder = pipeline_data["classifier"][model_name]["autoencoder"]
|
||||
|
||||
if autoencoder == "Yes":
|
||||
autoencoder = joblib.load(model_name_path + "/autoencoder.sav")
|
||||
else:
|
||||
autoencoder = None
|
||||
|
||||
gc_compute_counterfactuals(
|
||||
model_name_path,
|
||||
model_name_path_constraint,
|
||||
constraint_type,
|
||||
[0.00001],
|
||||
float(w_value),
|
||||
0.5,
|
||||
classifier,
|
||||
autoencoder,
|
||||
constraint = request.POST["constraint"]
|
||||
path = os.path.join(
|
||||
PIPELINE_PATH + f"{df_name}/" + "trained_models/" + f"{model_name}/" + f"{constraint}/"
|
||||
)
|
||||
path = model_name_path_constraint
|
||||
|
||||
|
||||
X_test_path = os.path.join(model_name_path + "X_test.csv")
|
||||
y_test_path = os.path.join(model_name_path + "y_test.npy")
|
||||
y_pred_path = os.path.join(path + "y_pred.npy")
|
||||
@ -1425,7 +1365,6 @@ def ajax_requests(action, request):
|
||||
X_test = pd.DataFrame(scaler.inverse_transform(X_test))
|
||||
X_cf = scaler.inverse_transform(X_cf)
|
||||
|
||||
print(df_id)
|
||||
fig = methods.ecg_plot_counterfactuals(
|
||||
int(df_id), X_test, y_test, y_pred, X_cf, cf_pred
|
||||
)
|
||||
@ -1435,7 +1374,65 @@ def ajax_requests(action, request):
|
||||
"fig": fig.to_html(),
|
||||
"dataset_type": dataset_type,
|
||||
}
|
||||
elif action == "compute_cf":
|
||||
model_name = request.POST.get("model_name")
|
||||
if model_name == "glacier":
|
||||
constraint_type = request.POST.get("constraint")
|
||||
w_value = request.POST.get("w_value")
|
||||
df_name = request.session.get("df_name")
|
||||
model_name_path = os.path.join(
|
||||
PIPELINE_PATH + f"{df_name}/" + "trained_models/" + f"{model_name}/"
|
||||
)
|
||||
model_name_path_constraint = model_name_path + f"{constraint_type}/"
|
||||
if not os.path.exists(model_name_path_constraint):
|
||||
os.makedirs(model_name_path_constraint)
|
||||
|
||||
# https://github.com/wildboar-foundation/wildboar/blob/master/docs/guide/explain/counterfactuals.rst#id27
|
||||
classifier = joblib.load(model_name_path + "/classifier.sav")
|
||||
|
||||
# pipeline path
|
||||
json_path = os.path.join(PIPELINE_PATH, f"{df_name}" + "/pipeline.json")
|
||||
# load pipeline data
|
||||
jsonFile = open(json_path, "r")
|
||||
pipeline_data = json.load(jsonFile) # data becomes a dictionary
|
||||
autoencoder = pipeline_data["classifier"][model_name]["autoencoder"]
|
||||
|
||||
experiment_dict = {"constraint": constraint_type, "w_value": w_value}
|
||||
|
||||
if "experiments" in pipeline_data["classifier"][model_name]:
|
||||
# if there exists key with value "experiments"
|
||||
keys = pipeline_data["classifier"][model_name]["experiments"].keys()
|
||||
last_key_int = int(list(keys)[-1])
|
||||
last_key_int_incr_str = str(last_key_int + 1)
|
||||
else:
|
||||
last_key_int_incr_str = "0"
|
||||
experiment_key_dict = {"experiments": {last_key_int_incr_str: {}}}
|
||||
pipeline_data["classifier"][model_name].update(experiment_key_dict)
|
||||
|
||||
outter_dict = {last_key_int_incr_str: experiment_dict}
|
||||
pipeline_data["classifier"][model_name]["experiments"].update(outter_dict)
|
||||
|
||||
if autoencoder == "Yes":
|
||||
autoencoder = joblib.load(model_name_path + "/autoencoder.sav")
|
||||
else:
|
||||
autoencoder = None
|
||||
|
||||
gc_compute_counterfactuals(
|
||||
model_name_path,
|
||||
model_name_path_constraint,
|
||||
constraint_type,
|
||||
[0.0001],
|
||||
float(w_value),
|
||||
0.5,
|
||||
classifier,
|
||||
autoencoder,
|
||||
)
|
||||
path = model_name_path_constraint
|
||||
# write back changes in json
|
||||
with open(json_path, "w") as file:
|
||||
json.dump(pipeline_data, file, indent=4)
|
||||
|
||||
context = {}
|
||||
elif action == "class_label_selection":
|
||||
|
||||
df_name = request.session.get("df_name")
|
||||
@ -1508,6 +1505,7 @@ def ajax_requests(action, request):
|
||||
X_test, y_test, int(cfrow_id), class_label
|
||||
)
|
||||
request.session["cfrow_id"] = index
|
||||
request.session["class_label"] = class_label
|
||||
context = {"fig": fig.to_html(), "dataset_type": dataset_type}
|
||||
elif action == "dataset_charts":
|
||||
df_name = request.POST.get("df_name")
|
||||
|
||||
BIN
db.sqlite3
BIN
db.sqlite3
Binary file not shown.
Loading…
x
Reference in New Issue
Block a user