Glacier train and counterfactuals, navigation between uploaded files, improvements on style, layout

README.md

@@ -1,6 +1,6 @@
 <h1>Tabular dataset</h1>
 
-Functionality for tabular data is implemented. Dataset selection provides a prediction of malignancy of breast cancer dataset and a stroke prediction dataset. Which the user
+Functionality for tabular data is implemented. Dataset selection provides a prediction of malignancy of breast cancer dataset and a stroke prediction dataset which the user
 can access visually using either the plots and the features based on his needs but also the actual data in the form of a table that is depicted in the page. Training of the
 classifiers for the tabular dataset has been implemented for some time now and produces adequete results. A user can choose the type of preprocessing to do, the classifier and the
 train/test ratio. Visualizing the training results is available through the charts.html page (Pre trained models and visualization) from where plots like Feature importance PCA 
@@ -8,40 +8,22 @@ classification reports and others are loaded. For counterfactuals explanations D
 features to vary parameter which can be exploited using the additional functionality of the dashboard that provides for selection of specific features to vary, select/deselect 
 and sort based on the importance. 
 
-One thing to note is that for the stroke dataset, results of diceml are not so adequete and needs to be improved. 
-
-TODO:
-    1) Add more parameterization on the training
-    2) Improve Stroke counterfactuals
-
 <h1>Timeseries dataset</h1>
 
-Utilising timeseries datasets is furtherly improved in this version. In the dataset selection the user can pick from 4 different datasets proved by Wildboar and observe the 
-data in the page for each of them. Moving to training, Wildboar classifiers are added and preprocessing is allowed in them as long as test set ratio. The available classifiers
-are RSF and KNN. For counterfactuals and explainability the user can pick the pre trained classifier, decide on a example timeseries entry based on the class and see the computed
+Utilising timeseries datasets is furtherly improved in this version. In the dataset selection the user can pick from 6 different datasets provided by Wildboar and observe the 
+data for each of them. Moving to training, Wildboar and Glacier classifiers are added and preprocessing is allowed. The available classifiers
+are RSF and KNN and 1dCNN. For counterfactuals and explainability the user can pick the pre trained classifier, decide on a example timeseries entry based on the class and see the computed
 counterfactual live. 
 
-TODO: 
-    1) Offer more example entries to pick from (done)
-    2) Add more paraterization in training
-    3) Compare different methods
-
 For Glacier things are a bit more complicated and definetely need further improvement. Training of Glacier for now can only be 1dCNN with little configuration. A user can also decide
-on an autoencoder that itslef would maybe need more configuration. Then FOr counterfactuals and explainability, there is a computation process of the coutnerfactuals but is not efficient.
-A user can pick from a list of contraint factors, and a predicted margin weight value and then run some computations that would produce the counterfactual of the entry. That is not efficient
-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.
+on an autoencoder. After the train the user can access the counterfactuals section from where using the pre trained classifier (1dCNN) he can run some experiments for specific contraints
+types. These experiments are saved cand can be accessed using the displayed load button.To try the experiments live the user is asked to choose a class label of the example they would like 
+to try, and pick an actual entry from a series of example entries which will be displayed after its selection. "Run Counterfactual" button will accessd the experiments data and plot the
+respective computed counterfactual generated by that specific method. 
 
-TODO:
-    1) Run experiment first (constraint type, predicted margin weight etc) (done)
-    2) Give access to pre computed experiments (done)
-    3) Offer more examples entries to pick from (done)
-    4) Add more parameterization
+For wildboar the procedure is the same except for some extra configuration during the training where a test ratio and some prepropcessing can be selected. 
 
 <h1> Import </h1> 
 
-Importing a dataset is available in this version using the dataset selection navigator. It is important to state the type of the dataset (timeseries or tabular) for the backend. Timeseries and tabular dataset imports arre available.
-
-TODO: 
-    1) Navigate between the uploaded datasets 
-    2) Remove uploaded files
-
+Importing a dataset is available in this version using the dataset selection navigator. It is important to state the type of the dataset (timeseries or tabular) for the backend. Timeseries and tabular dataset imports are available and imported datasets are saved and can be reused later if the user likes to. From there depending on the type of the datasets the respective functionalities
+are available. 

base/download_wildboar_datasets.py

@@ -4,7 +4,7 @@ from wildboar.datasets import load_dataset, list_datasets
 # Get the list of all available datasets in the 'wildboar/ucr' repository
 available_datasets = list_datasets(repository='wildboar/ucr')
 
-available_datasets = ["ECGFiveDays", "ItalyPowerDemand"]
+available_datasets = ["FordB"]
 for dataset_name in available_datasets:
     try:
         # Load the dataset

base/methods.py

@@ -9,6 +9,7 @@ import numpy as np
 from pandas.api.types import is_numeric_dtype
 from sklearn.metrics import classification_report
 import plotly.express as px
+from django.conf import settings
 import joblib
 from sklearn.decomposition import PCA
 from sklearn.manifold import TSNE
@@ -26,15 +27,21 @@ from sklearn.metrics import classification_report
 from .glacier.src.gc_latentcf_search_1dcnn_function import gc_latentcf_search_1dcnn
 from .glacier.src.glacier_compute_counterfactuals import gc_compute_counterfactuals
 import re
+import json
+
+PIPELINE_PATH = os.path.join(settings.BASE_DIR, "base/pipelines/")
 
 def stats(
     dataset_path,
     dataset_type,
+    pos=None,
+    neg=None,
     feature1=None,
     feature2=None,
     label=None,
     name=None,
 ):
+    print(dataset_type)
     if dataset_type == "tabular":
         df = pd.read_csv(dataset_path)
 
@@ -110,6 +117,7 @@ def stats(
             fig = px.bar(df, x=feature1, y=feature2, color=label, barmode="group")
 
         fig.update_layout(clickmode="event+select", autosize=True)
+        
     elif dataset_type == "timeseries":
         # timeseries
         df = pd.read_csv(dataset_path)
@@ -136,8 +144,14 @@ def stats(
                 negative_label = "12/11/1990"
                 positive_label = "17/11/1990"
 
-            negative_label_value = 2
-            positive_label_value = 1
+            elif name == "ford-a":
+                negative_label = "Negative label"
+                positive_label = "Positive label"
+
+            # hard coded need to be dynamic based on
+            # dataset
+            negative_label_value = neg
+            positive_label_value = pos
 
         num_timesteps = df.shape[1] - 1
         fig = make_subplots(
@@ -586,24 +600,24 @@ def training(
         # TODO: add 1dcnn train
 
         if model == "glacier":
-            
+
             # Split the lr-list string and convert each value to float
             # experiment_arguments[8] = experiment_arguments[8].rstrip(';')
             # lr_list = [float(x) for x in experiment_arguments[8].split()]
-            
+
             gc_latentcf_search_1dcnn(
-                experiment_arguments[0],
+                data,
                 int(experiment_arguments[1]),
                 int(experiment_arguments[2]),
                 model_path + f"/{experiment_arguments[3]}",
                 model_path,
-                autoencoder         
+                autoencoder,
             )
-            
+
         elif model == "wildboar_knn" or model == "wildboar_rsf":
             X = data.iloc[:, :-1].values
             y = data.iloc[:, -1].values
-        
+
             X_train, X_test, y_train, y_test = train_test_split(
                 X, y, test_size=test_size, random_state=1
             )
@@ -994,7 +1008,7 @@ def get_ecg_entry(X_test, y_test, i, class_label):
     fig = go.Figure()
     y = X_test[y_test == class_label].iloc[i]
     index = X_test[y_test == class_label].index[i]
-    
+
     if class_label == 0:
         name = "Normal ECG"
     else:
@@ -1269,15 +1283,15 @@ def convert_to_camel_case(s):
     return camel_case
 
 
-# def fetch_line_by_dataset_and_constraint(file_path, dataset, constraint):
+# def fetch_line_by_dataset(file_path, dataset, constraint):
 
-def fetch_line_by_dataset_and_constraint(file_path, dataset):
+
+def fetch_line_by_dataset(file_path, dataset):
     """
-    Fetches a line from the file based on the specified dataset name and constraint.
+    Fetches a line from the file based on the specified dataset name to retrieve basic information about the dataset.
 
     :param file_path: Path to the input file.
     :param dataset: The dataset name to search for.
-    :param constraint: The constraint value to search for.
     :return: The line matching the dataset and constraint, or None if not found.
     """
     with open(file_path, "r") as file:
@@ -1289,9 +1303,7 @@ def fetch_line_by_dataset_and_constraint(file_path, dataset):
                 continue
             # Use regular expressions for exact match of the dataset
             dataset_pattern = rf"--dataset\s+{re.escape(dataset)}\b"
-            if (
-                re.search(dataset_pattern, stripped_line)
-            ):
+            if re.search(dataset_pattern, stripped_line):
                 return stripped_line
     return None
 
@@ -1304,7 +1316,8 @@ def extract_arguments_from_line(line):
     :return: A list of argument values found in the line.
     """
     # Find all arguments and their values
-    matches = re.findall(r"(--[\w-]+)((?:\s+[^-][^\s]*)*)", line)
+    # matches = re.findall(r"(--[\w-]+)((?:\s+[^-][^\s]*)*)", line)
+    matches = re.findall(r"(--[\w-]+)\s+([^\s]+)", line)
 
     # Extract argument values
     arguments = [value.strip() for _, value in matches if value.strip()]
@@ -1331,6 +1344,8 @@ def create_tuple_of_models_text_value(available_pre_trained_models):
         elif "wildboar_rsf" == model:
             available_pretrained_models_text = "Wildboar Random Shapelet Forest"
 
-        available_pretrained_models_info.append((model, available_pretrained_models_text))
-    
+        available_pretrained_models_info.append(
+            (model, available_pretrained_models_text)
+        )
+
     return available_pretrained_models_info

base/notes.txt

@@ -6,4 +6,89 @@ Tasks:
                     while for default he would have access to presets for glacier training following zhen dong's presets
         1.3) file management after training
     2) Tabular training
-    3) Uploaded files exploring
+    3) Uploaded files exploring
+
+----------
+14/10/2024
+----------
+
+1) glacier downloads the respective dataset -> dataset is given as a parameter (done)
+2) IntervalImportance error (when training a 1dCNN model and want to save the importance for future use in plots).
+    gc_latentcf_search_1dcnn_function.py
+    Commented out for now... 
+
+3) Counterfactual.html looks really bad should be improved (a bit)
+4) after 1dCnn train there is an error with classification_report
+    figure out what to plot after train of 1dCnn to improve 
+    charts.html content
+5) add learning rate as parameter to glacier_compute_counterfactuals.py
+6) check for more datasets
+7) positive negative labels in pipeline json to be part of the dataset information and not of glacier model (done)
+8) load experiments text is still json format, should look prettier
+9) add ford-a dataset (done)
+10) hard coded positive and negative label values during selection of timeseries dataset (done)
+11) If no experiments to load case (done)
+
+Counterfactuals.html
+
+1) new experiment, load experiment buttons
+    1.1) spacing
+    1.2) aHave buttons active all the time unless another is clicked
+2) New experiment run
+    2.1) enable loaded experiments 
+3) Load experiments
+    3.1) when changing timeseries page scrolls up and it is weird
+
+
+----------
+21/10/2024
+----------
+1) counterfactuals for tabular-> features to vary could have a drop down and a drop up to hide when needed (done)
+2) Original Data table does not take up all the card space 
+3) Tsne should be appended to the DOM imidiately since it is common plot for all the pre trained models
+
+----------
+23/10/2024
+----------
+1) replace dice ml
+2) fix counterfactuals.html for time series (prettyfie)
+
+25/10/2024
+1) description of extremum
+2) functionalitites->explain
+3) pages depend on (enabled or disabled) the actions the user took (train a model enables pre-trained.html and counterfactuals.html
+otherwise disables)
+4) explain workflow
+5) home page seperate from dataset selection
+6) main buttons
+    1) tabular
+    2) timeseries
+    3) upload -> information
+7) Train.html dataframe summary text
+
+- Functionality:
+    1) after training done: save model as (give a name) with all the cases
+        Give message: "Model trained succesfully" 
+
+        "See the results: (click)" -> demonstrate what is displayed at charts.html but in a different page
+        Goal is to distinguish between pre trained models and newly trained models
+
+        save the model or not
+            1) Save (name, message etc, prompt train another model or counterfactuals)
+            2) or Retrained (prompt back to the train.html)
+    2) delete available pre trained models if needed with the use of a button
+    3) check if coutnerfactual has similar class 
+- give information about the classifiers
+- merge both tables together
+
+feature|original value|counterfactual value
+
+Focus on the web version:
+    1) no upload 
+    2) no train[]
+
+
+----------
+29/10/2024
+----------
+1) choose between uploaded files

base/pipeline.py

@@ -0,0 +1,122 @@
+import os
+from .methods import PIPELINE_PATH
+import json
+
+
+class pipeline_json:
+    def __init__(self, path):
+        self.json_path = path
+        if not os.path.exists(self.json_path):
+            open(self.json_path, "w")
+            self.data = {}  # data becomes a dictionary
+        else:
+            jsonFile = open(self.json_path, "r")
+            self.data = json.load(jsonFile)  # data becomes a dictionary
+            print("Pipeline.json already exists.")
+        
+
+    def append_pipeline_json(self, info):
+        """
+        Appends to json object file located in path
+
+        :param info: Dictionary to append to json file
+        :param path: Path where json file is located
+        :return: File's content
+        """
+        try:
+            with open(self.json_path, "r") as f:
+                if os.path.getsize(self.json_path) > 0:
+                    data = json.load(f)  # Load the JSON data from the file
+                else:
+                    data = {}
+            
+            data.update(info)
+            
+            with open(self.json_path, 'w') as f:
+                json.dump(data, f, indent=4)  # Write the updated JSON data back to the file
+            
+            self.data = data
+                
+            return 1
+        except Exception as e:
+            print("herererr: ", e)
+            return -1
+
+    def read_pipline_json(self, key_list):
+        """
+        Fetch a specific key from a json file located in path
+
+        :param key_list: Key to look up
+        :return: result
+        """
+        jsonFile = open(self.json_path, "r")
+        pipeline_json_data = json.load(jsonFile)  # data becomes a dictionary
+        tmp1 = pipeline_json_data
+        tmp = {}
+        
+        if key_list != "":
+            for key in key_list:
+                try:
+                    tmp = tmp1[key]
+                    tmp1 = tmp
+                except Exception as e:
+                    print(e)
+                    return None
+            return tmp1
+        else:
+            return None
+
+    def update_pipeline_json(self, key_list, info):
+
+        jsonFile = open(self.json_path, "r")
+        pipeline_json_data = json.load(jsonFile)  # data becomes a dictionary
+        tmp1 = pipeline_json_data
+
+        if key_list != "":
+            for key in key_list:
+                try:
+                    tmp = tmp1[key]
+                    tmp1 = tmp
+                except Exception as e:
+                    print(e)
+                    return None
+                
+            tmp1.update(info)
+            with open(self.json_path, 'w') as file:
+                json.dump(pipeline_json_data, file, indent=4)
+            
+            self.data = data           
+        else:
+            return None
+
+    def check_key_pipeline_json(self, key):
+        keys = get_all_keys(self.data)
+        if key in keys:            
+            return 1
+        return 0
+    
+    def get_keys_with_specific_value(self, value):
+        list_of_keys = []
+        for key,value in self.data.items():
+            if "uploaded" in value:
+                list_of_keys.append(key)
+        print(list_of_keys)
+        return list_of_keys
+
+# if "classifier" in pipeline_json.keys():
+#     temp_jason = {model_name: preprocessing_info}
+#     pipeline_json["classifier"].update(temp_jason)
+# else:
+#     temp_jason = {
+#         "preprocessed_name": df_name + "_preprocessed.csv",
+#         "classifier": {model_name: preprocessing_info},
+#     }
+#     pipeline_json.update(temp_jason)
+
+def get_all_keys(d):
+    keys = []
+    for key, value in d.items():
+        keys.append(key)
+        if isinstance(value, dict):  # If value is a dictionary, call the function recursively
+            keys.extend(get_all_keys(value))
+    return keys

base/pipelines/Heart_Disease_Prediction/Heart_Disease_Prediction_preprocessed.csv

@@ -0,0 +1,271 @@
+Heart_Disease,Age,Sex,Chest_pain_type,BP,Cholesterol,FBS_over_120,EKG_results,Max_HR,Exercise_angina,ST_depression,Slope_of_ST,Number_of_vessels_fluro,Thallium
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base/pipelines/Heart_Disease_Prediction/pipeline.json

@@ -0,0 +1,14 @@
+{
+    "name": "Heart_Disease_Prediction",
+    "preprocessed_name": "Heart_Disease_Prediction_preprocessed.csv",
+    "classifier": {
+        "dt": {
+            "preprocessing": "[\"std\"]",
+            "test_set_ratio": "0.3",
+            "explainability": {
+                "technique": "dice"
+            },
+            "class_label": "Heart_Disease"
+        }
+    }
+}

base/pipelines/Heart_Disease_Prediction/trained_models/dt/counterfactuals.csv

@@ -0,0 +1,7 @@
+Age,Sex,Chest_pain_type,BP,Cholesterol,FBS_over_120,EKG_results,Max_HR,Exercise_angina,ST_depression,Slope_of_ST,Number_of_vessels_fluro,Thallium,Heart_Disease
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base/pipelines/Heart_Disease_Prediction/trained_models/dt/feature_importance_df.csv

@@ -0,0 +1,14 @@
+feature,importance
+Age,0.06434523809523808
+Sex,0.06714066246864882
+Chest_pain_type,0.2600416291629162
+BP,0.09327806122448981
+Cholesterol,0.07169544541954709
+FBS_over_120,0.0
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+Exercise_angina,0.0
+ST_depression,0.05801020408163264
+Slope_of_ST,0.10117424242424242
+Number_of_vessels_fluro,0.1252095919511363
+Thallium,0.061119960179193626

base/pipelines/breast-cancer/pipeline.json

@@ -1,17 +1,37 @@
 {
-  "name": "breast-cancer",
-  "preprocessed_name": "breast-cancer_preprocessed.csv",
-  "classifier": {
-    "dt": {
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-        "std",
-        "imp"
-      ],
-      "test_set_ratio": "0.3",
-      "explainability": {
-        "technique": "dice"
-      },
-      "class_label": "diagnosis"
+    "preprocessed_name": "breast-cancer_preprocessed.csv",
+    "classifier": {
+        "rf": {
+            "preprocessing": "[\"std\"]",
+            "test_set_ratio": "0.2",
+            "explainability": {
+                "technique": "dice"
+            },
+            "class_label": "diagnosis"
+        },
+        "lr": {
+            "preprocessing": "[\"std\"]",
+            "test_set_ratio": "0.2",
+            "explainability": {
+                "technique": "dice"
+            },
+            "class_label": "diagnosis"
+        },
+        "dt": {
+            "preprocessing": "[\"std\"]",
+            "test_set_ratio": "0.2",
+            "explainability": {
+                "technique": "dice"
+            },
+            "class_label": "diagnosis"
+        },
+        "svm": {
+            "preprocessing": "[\"std\"]",
+            "test_set_ratio": "0.3",
+            "explainability": {
+                "technique": "dice"
+            },
+            "class_label": "diagnosis"
+        }
     }
-  }
 }

base/pipelines/breast-cancer/trained_models/dt/feature_importance_df.csv

@@ -1,31 +1,31 @@
 feature,importance
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 perimeter_mean,0.0
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 smoothness_mean,0.0
 compactness_mean,0.0
 concavity_mean,0.0
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 symmetry_mean,0.0
 fractal_dimension_mean,0.0
 radius_se,0.0
 texture_se,0.0
 perimeter_se,0.0
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 symmetry_se,0.0
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base/pipelines/breast-cancer/trained_models/lr/counterfactuals.csv

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base/pipelines/breast-cancer/trained_models/lr/feature_importance_df.csv

@@ -0,0 +1,31 @@
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base/pipelines/breast-cancer/trained_models/rf/counterfactuals.csv

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base/pipelines/breast-cancer/trained_models/rf/feature_importance_df.csv

@@ -0,0 +1,31 @@
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base/pipelines/breast-cancer/trained_models/svm/counterfactuals.csv

@@ -0,0 +1,7 @@
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base/pipelines/breast-cancer/trained_models/svm/feature_importance_df.csv

@@ -0,0 +1,31 @@
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base/pipelines/dataset_types_pipeline.json

@@ -6,5 +6,13 @@
     "italy-power-demand": "timeseries",
     "ecg-five-days": "timeseries",
     "heart": "tabular",
-    "Heart_Disease_Prediction": "tabular"
+    "ford-a": "timeseries",
+    "diabetes": [
+        "tabular",
+        "uploaded"
+    ],
+    "Heart_Disease_Prediction": [
+        "tabular",
+        "uploaded"
+    ]
 }

base/pipelines/diabetes/diabetes.csv

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base/pipelines/diabetes/pipeline.json

@@ -0,0 +1,14 @@
+{
+    "name": "diabetes",
+    "preprocessed_name": "diabetes_preprocessed.csv",
+    "classifier": {
+        "xgb": {
+            "preprocessing": "[\"std\"]",
+            "test_set_ratio": "0.2",
+            "explainability": {
+                "technique": "dice"
+            },
+            "class_label": "Outcome"
+        }
+    }
+}

base/pipelines/diabetes/trained_models/xgb/feature_importance_df.csv

@@ -0,0 +1,9 @@
+feature,importance
+Pregnancies,0.09800142
+Glucose,0.24721502
+BloodPressure,0.09747202
+SkinThickness,0.08884042
+Insulin,0.09651642
+BMI,0.13837509
+DiabetesPedigreeFunction,0.10624578
+Age,0.1273338

base/pipelines/ecg-five-days/pipeline.json

@@ -0,0 +1,5 @@
+{
+    "name": "ecg-five-days",
+    "pos": "1",
+    "neg": "2"
+}

base/pipelines/ford-a/pipeline.json

@@ -0,0 +1,5 @@
+{
+    "name": "ford-a",
+    "pos": "1",
+    "neg": "-1"
+}

base/pipelines/gun-point/gun-point_preprocessed.csv

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base/pipelines/gun-point/pipeline.json

@@ -1,16 +1,15 @@
 {
-  "name": "gun-point",
-  "preprocessed_name": "gun-point_preprocessed.csv",
-  "classifier": {
-    "wildboar_knn": {
-      "preprocessing": [
-        "std",
-        "imp"
-      ],
-      "test_set_ratio": "0.2",
-      "explainability": {
-        "technique": "wildboar_knn"
-      }
+    "name": "gun-point",
+    "pos": "1",
+    "neg": "2",
+    "preprocessed_name": "gun-point_preprocessed.csv",
+    "classifier": {
+        "wildboar_knn": {
+            "preprocessing": "[\"std\"]",
+            "test_set_ratio": "0.2",
+            "explainability": {
+                "technique": "wildboar_knn"
+            }
+        }
     }
-  }
 }

base/pipelines/italy-power-demand/pipeline.json

@@ -1,16 +1,5 @@
 {
-  "name": "italy-power-demand",
-  "preprocessed_name": "italy-power-demand_preprocessed.csv",
-  "classifier": {
-    "wildboar_knn": {
-      "preprocessing": [
-        "std",
-        "imp"
-      ],
-      "test_set_ratio": "0.2",
-      "explainability": {
-        "technique": "wildboar_knn"
-      }
-    }
-  }
+    "name": "italy-power-demand",
+    "pos": "1",
+    "neg": "2"
 }

base/pipelines/italy-power-demand/trained_models/wildboar_knn/X_test.csv

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base/pipelines/stroke/pipeline.json

@@ -1,13 +1,9 @@
 {
-  "name": "stroke",
   "preprocessed_name": "stroke_preprocessed.csv",
   "classifier": {
-    "dt": {
-      "preprocessing": [
-        "std",
-        "onehot"
-      ],
-      "test_set_ratio": "0.3",
+    "rf": {
+      "preprocessing": "[\"std\",\"onehot\"]",
+      "test_set_ratio": "0.2",
       "explainability": {
         "technique": "dice"
       },

base/pipelines/stroke/trained_models/dt/counterfactuals.csv

@@ -1,7 +0,0 @@
-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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-1.5379353389188812,1.0,0.0,0.5387100724827014,4.5,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0
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--0.3085493,1.0,0.0,0.5387100724827014,-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

base/pipelines/stroke/trained_models/dt/feature_importance_df.csv

@@ -1,11 +0,0 @@
-feature,importance
-gender,0.03534413042961699
-age,0.4153170313132725
-hypertension,0.055058068443063615
-heart_disease,0.03941524990242443
-ever_married,0.013413876504697674
-work_type,0.057292013488553076
-Residence_type,0.029453837498799915
-avg_glucose_level,0.14570517147476392
-bmi,0.11927431943421114
-smoking_status,0.08972630151059674