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README.md

@@ -1,12 +1,72 @@
----
-title: DataFlowPro
-emoji: 🔥
-colorFrom: indigo
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# DataFlow Pro
+Automating ML Workflows with Ease
+
+## Introduction
+The Automated ML is a Python application designed to automate the process of building, tuning, and evaluating machine learning models based on json provided in RTF/JSON?/TXT file format. <br>
+This application follows a structured flow to read the json file, extract dataset information, transform features, split data, build and tune models, and evaluate their performance.
+
+
+## Installation
+To use the Automated ML Pipeline, follow these steps:
+
+1. Clone this repository to your local machine: <br>
+```git clone https://github.com/Rupanshu-Kapoor/AutomateML.git```
+
+2. Install the required dependencies: <br>
+   `pip install -r requirements.txt`
+
+3. Run the application: <br>
+   `streamlit run app.py`
+
+ 
+ ## Steps to Use the Application:
+
+ You can use the application in following two ways:
+
+### (A). Create Json and Train Model
+
+1. Upload the dataset on the tool on which you want to train the different model.
+2. Once the data is uploaded, you can preview the dataset.
+3. Select prediction parameters (prediction type, target variable, k-fold, etc.). 
+4. Select features to be used for prediction.
+5. When you select any feature, you can choose how to handle it. (rescaling, encoding, etc.)
+6. Select the model to be used for prediction.
+7. When you select any model, you can choose hyperparameters for tuning.
+8. Once all the parameters are selected, click on `Generate Json and Train Model` button.
+9. Application will generate the json file and train the model and display the results.
+
+### (B). Upload Json and Train Model
+ 1. Upload the json file that contains all the dataset information.
+ 2. Click on Train Models.
+ 3. Application will train the model and display the results.
+
+## Working of the Application:
+The application performs the following tasks in sequence:
+1. **Read the JSON File and Parse JSON Content**: The RTF/JSON file is read, converted to plain text, and JSON content is extracted.
+2. **Extract Dataset Information**: Extract dataset information such as feature names, target variable, problem type (regression/classification), feature handling, etc.
+3. **Transform Features**: Features are transformed based on the specified feature handling methods.
+4. **Sample Data and Train-Test Split**: Data is sampled and split into training and testing sets.
+5. **Model Building**: Models are built based on the problem type (regression/classification).
+6. **Hyperparameter Tuning**: Hyperparameters of the models are tuned using grid search.
+7. **Model Evaluation**: Trained models are evaluated using specified evaluation metrics.
+<! --8. **Save Results**: Trained models and evaluation metrics are saved in the results/ directory. -->
+
+
+## Use Cases
+
+This application can be used for various use cases, including but not limited to:
+
+- Automated machine learning (AutoML) pipelines.
+- Data preprocessing and feature engineering tasks.
+- Model training and evaluation for regression or classification problems.
+- Hyperparameter tuning and model selection.
+- Experimentation with different datasets and configurations.
+
+## Future Work
+Possible future enhancements for the application include:
+
+- Adding support for additional data formats (e.g., CSV, Excel).
+- Implementing more advanced feature engineering techniques.
+- Incorporating more sophisticated model selection and evaluation methods.
+- Enhancing the user interface for easier interaction.
+- Integrating with external APIs or databases for data retrieval.

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+\par     "session_name": "test",
+\par     "session_description": "test",
+\par     "design_state_data": \{
+\par 
+\par       "session_info" : \{
+\par         "project_id": "1",
+\par         "experiment_id": "kkkk-11",
+\par         "dataset":"iris_modified.csv",
+\par         "session_name": "test",
+\par         "session_description": "test"
+\par         \},
+\par 
+\par       "target": \{
+\par         "prediction_type": "Classification",
+\par         "target": "species",
+\par         "type":"classifiation",
+\par         "partitioning": true
+\par       \},
+\par       "train": \{
+\par         "policy": "Split the dataset",
+\par         "time_variable": "sepal_length",
+\par         "sampling_method": "No sampling(whole data)",
+\par         "split": "Randomly",
+\par         "k_fold": false,
+\par         "train_ratio": 0.8,
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+\par       \},
+\par       "feature_handling": \{
+\par         "sepal_length": \{
+\par           "feature_name": "sepal_length",
+\par           "is_selected": true,
+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"
+\par             
+\par           \}
+\par         \},
+\par         "sepal_width": \{
+\par           "feature_name": "sepal_width",
+\par           "is_selected": true,
+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"
+\par             
+\par           \}
+\par         \},
+\par         "petal_length": \{
+\par           "feature_name": "petal_length",
+\par           "is_selected": true,
+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"
+\par             
+\par           \}
+\par         \},
+\par         "petal_width": \{
+\par           "feature_name": "petal_width",
+\par           "is_selected": }{\rtlch\fcs1 \af2\afs21 \ltrch\fcs0 \f2\fs21\insrsid1526327 false}{\rtlch\fcs1 \af2\afs21 \ltrch\fcs0 \f2\fs21\insrsid3218741\charrsid3218741 ,
+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"            
+\par           \}
+\par         \},
+\par         "species": \{
+\par           "feature_name": "species",
+\par           "is_selected": true,
+\par           "feature_variable_type": "text",
+\par           "feature_details": \{
+\par             "text_handling": "Tokenize and hash",
+\par             "hash_columns": 0
+\par           \}
+\par         \}
+\par       \},
+\par       
+\par       "algorithms": \{
+\par         "RandomForestClassifier": \{
+\par           "model_name": "Random Forest Classifier",
+\par           "is_selected": true,
+\par           "min_trees": 10,
+\par           "max_trees": 30,
+\par           "feature_sampling_statergy": "Default",
+\par           "min_depth": 20,
+\par           "max_depth": 30,
+\par           "min_samples_per_leaf_min_value": 5,
+\par           "min_samples_per_leaf_max_value": 50,
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data/algoparams_from_ui1_20240513_231725.rtf

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data/algoparams_from_ui1_20240513_232048.rtf

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+\par     "session_name": "test",
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+\par 
+\par       "session_info" : \{
+\par         "project_id": "1",
+\par         "experiment_id": "kkkk-11",
+\par         "dataset":"iris_modified.csv",
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+\par         \},
+\par 
+\par       "target": \{
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+\par         "target": "species",
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+\par       \},
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+\par       \},
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+\par           "is_selected": true,
+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"
+\par             
+\par           \}
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+\par           "feature_name": "sepal_width",
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+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"
+\par             
+\par           \}
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+\par             "make_derived_feats": false,
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+\par             "impute_with": "Average of values"
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+\par           \}
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+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"            
+\par           \}
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+\par           "feature_name": "species",
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+\par           "feature_variable_type": "text",
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+\par }\pard \ltrpar\ql \li0\ri0\sa160\sl259\slmult1\widctlpar\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0\itap0\pararsid3218741 {\rtlch\fcs1 \af2\afs21 \ltrch\fcs0 \f2\fs21\insrsid3218741\charrsid3218741         "LinearRegression": \{
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+\par     "design_state_data": \{
+\par 
+\par       "session_info" : \{
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+\par         "experiment_id": "kkkk-11",
+\par         "dataset":"iris_modified.csv",
+\par         "session_name": "test",
+\par         "session_description": "test"
+\par         \},
+\par 
+\par       "target": \{
+\par         "prediction_type": "Classification",
+\par         "target": "species",
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+\par       \},
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+\par         "train_ratio": 0.8,
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+\par       \},
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+\par         "sepal_length": \{
+\par           "feature_name": "sepal_length",
+\par           "is_selected": true,
+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"
+\par             
+\par           \}
+\par         \},
+\par         "sepal_width": \{
+\par           "feature_name": "sepal_width",
+\par           "is_selected": true,
+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"
+\par             
+\par           \}
+\par         \},
+\par         "petal_length": \{
+\par           "feature_name": "petal_length",
+\par           "is_selected": true,
+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"
+\par             
+\par           \}
+\par         \},
+\par         "petal_width": \{
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+\par           "feature_variable_type": "numerical",
+\par           "feature_details": \{
+\par             "numerical_handling": "Keep as regular numerical feature",
+\par             "rescaling": "No rescaling",
+\par             "make_derived_feats": false,
+\par             "missing_values": "Impute",
+\par             "impute_with": "Average of values"            
+\par           \}
+\par         \},
+\par         "species": \{
+\par           "feature_name": "species",
+\par           "is_selected": true,
+\par           "feature_variable_type": "text",
+\par           "feature_details": \{
+\par             "text_handling": "Tokenize and hash",
+\par             "hash_columns": 0
+\par           \}
+\par         \}
+\par       \},
+\par       
+\par       "algorithms": \{
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+\par           "model_name": "Random Forest Classifier",
+\par           "is_selected": true,
+\par           "min_trees": 10,
+\par           "max_trees": 30,
+\par           "feature_sampling_statergy": "Default",
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+\par           "max_depth": 30,
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+\par           "min_samples_per_leaf_max_value": 50,
+\par           "parallelism": 0
+\par         \},
+\par         "RandomForestRegressor": \{
+\par           "model_name": "Random Forest Regressor",
+\par           "is_selected": false,
+\par           "min_trees": 10,
+\par           "max_trees": 20,
+\par           "feature_sampling_statergy": "Default",
+\par           "min_depth": 20,
+\par           "max_depth": 25,
+\par           "min_samples_per_leaf_min_value": 5,
+\par           "min_samples_per_leaf_max_value": 10,
+\par           "parallelism": 0
+\par         \},
+\par }\pard \ltrpar\ql \li0\ri0\sa160\sl259\slmult1\widctlpar\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0\itap0\pararsid12544274 {\rtlch\fcs1 \af2\afs21 \ltrch\fcs0 \f2\fs21\insrsid3218741\charrsid3218741         
+\par }\pard \ltrpar\ql \li0\ri0\sa160\sl259\slmult1\widctlpar\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0\itap0\pararsid3218741 {\rtlch\fcs1 \af2\afs21 \ltrch\fcs0 \f2\fs21\insrsid3218741\charrsid3218741         "LinearRegression": \{
+\par           "model_name": "LinearRegression",
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+\par         \},
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+\par         \},
+\par         "RidgeRegression": \{
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+\par           "is_selected": false,
+\par           "regularization_term": "Specify values to test",
+\par           "min_iter":30,
+\par           "max_iter":50,
+\par           "min_regparam":0.5,
+\par           "max_regparam":0.8
+\par         \},
+\par         "LassoRegression": \{
+\par           "model_name": "Lasso Regression",
+\par           "is_selected": false,
+\par           "regularization_term": "Specify values to test",
+\par           "min_iter":30,
+\par           "max_iter":50,
+\par           "min_regparam":0.5,
+\par           "max_regparam":0.8
+\par         \},
+\par         "ElasticNetRegression": \{
+\par           "model_name": "Lasso Regression",
+\par           "is_selected": false,
+\par           "regularization_term": "Specify values to test",
+\par           "min_iter":30,
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+\par           "max_elasticnet":0.8
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+\par         "xg_boost": \{
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+\par           "dart": true,
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+\par           "learningRate": [89, 76],
+\par           "l1_regularization": [77],
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+\par           "sub_sample": [67],
+\par           "col_sample_by_tree": [67],
+\par           "replace_missing_values": false,
+\par           "parallelism": 0
+\par         \},
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+\par           "max_depth": 7,
+\par           "use_gini": false,
+\par           "use_entropy": true,
+\par           "min_samples_per_leaf": [12, 6],
+\par           "use_best": true,
+\par           "use_random": true
+\par         \},
+\par         "DecisionTreeClassifier": \{
+\par           "model_name": "Decision Tree",
+\par           "is_selected": }{\rtlch\fcs1 \af2\afs21 \ltrch\fcs0 \f2\fs21\insrsid3950199 true}{\rtlch\fcs1 \af2\afs21 \ltrch\fcs0 \f2\fs21\insrsid3218741\charrsid3218741 ,
+\par           "min_depth":4,
+\par           "max_depth": 7,
+\par           "use_gini": false,
+\par           "use_entropy": true,
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data/iris_modified.csv

@@ -0,0 +1,151 @@
+sepal_length,sepal_width,petal_length,petal_width,species
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3,1.4,0.1,Iris-setosa
+4.3,3,1.1,0.1,Iris-setosa
+5.8,4,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5,3,1.6,0.2,Iris-setosa
+5,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5,3.3,1.4,0.2,Iris-setosa
+7,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5,2,3.5,1,Iris-versicolor
+5.9,3,4.2,1.5,Iris-versicolor
+6,2.2,4,1,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3,5,1.7,Iris-versicolor
+6,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6,2.7,5.1,1.6,Iris-versicolor
+5.4,3,4.5,1.5,Iris-versicolor
+6,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3,4.1,1.3,Iris-versicolor
+5.5,2.5,4,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3,4.6,1.4,Iris-versicolor
+5.8,2.6,4,1.2,Iris-versicolor
+5,2.3,3.3,1,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3,5.8,2.2,Iris-virginica
+7.6,3,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3,5.5,2.1,Iris-virginica
+5.7,2.5,5,2,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6,2.2,5,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2,Iris-virginica
+7.7,2.8,6.7,2,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6,3,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3,5.2,2.3,Iris-virginica
+6.3,2.5,5,1.9,Iris-virginica
+6.5,3,5.2,2,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3,5.1,1.8,Iris-virginica

data/uploaded_20240513_231616.json

@@ -0,0 +1 @@
+{"session_name": "20240513_231616", "session_description": "20240513_231616", "design_state_data": {"session_info": {"dataset": "iris_modified.csv", "session_name": "20240513_231616", "session_description": "20240513_231616"}, "target": {"prediction_type": "Regression", "target": "sepal_length", "type": "Regression", "partitioning": true}, "train": {"k_fold": 5, "train_ratio": 0.8, "random_seed": 42}, "feature_handling": {"sepal_length": {"feature_name": "sepal_length", "is_selected": true, "feature_variable_type": "float64", "feature_details": {}}, "sepal_width": {"feature_name": "sepal_width", "is_selected": true, "feature_variable_type": "float64", "feature_details": {"numerical_handling": null, "rescaling": false, "scaling_type": null, "make_derived_feats": false, "missing_values": false, "impute_with": null}}, "petal_length": {"feature_name": "petal_length", "is_selected": false, "feature_variable_type": "float64", "feature_details": {"numerical_handling": null, "rescaling": false, "scaling_type": null, "make_derived_feats": false, "missing_values": "Impute", "impute_with": null}}, "petal_width": {"feature_name": "petal_width", "is_selected": false, "feature_variable_type": "float64", "feature_details": {"numerical_handling": null, "rescaling": false, "scaling_type": null, "make_derived_feats": false, "missing_values": "Impute", "impute_with": null}}, "species": {"feature_name": "species", "is_selected": false, "feature_variable_type": "object", "feature_details": {"numerical_handling": null, "rescaling": false, "scaling_type": null, "make_derived_feats": false, "missing_values": "Impute", "impute_with": null}}}, "algorithms": {"RandomForestRegressor": {"model_name": "RandomForestRegressor", "is_selected": false, "random_state": [42]}, "LinearRegression": {"model_name": "LinearRegression", "is_selected": false, "random_state": [42]}, "RidgeRegression": {"model_name": "RidgeRegression", "is_selected": false, "random_state": [42]}, "LassoRegression": {"model_name": "LassoRegression", "is_selected": false, "random_state": [42]}, "ElasticNetRegression": {"model_name": "ElasticNetRegression", "is_selected": false, "random_state": [42]}, "xg_boost": {"model_name": "xg_boost", "is_selected": false, "random_state": [42]}, "DecisionTreeRegressor": {"model_name": "DecisionTreeRegressor", "is_selected": false, "random_state": [42]}, "SVM": {"model_name": "SVM", "is_selected": false, "random_state": [42]}, "KNN": {"model_name": "KNN", "is_selected": false, "random_state": [42]}, "neural_network": {"model_name": "neural_network", "is_selected": false, "random_state": [42]}}}}

data/uploaded_20240513_231619.json

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+{"session_name": "20240513_231619", "session_description": "20240513_231619", "design_state_data": {"session_info": {"dataset": "iris_modified.csv", "session_name": "20240513_231619", "session_description": "20240513_231619"}, "target": {"prediction_type": "Regression", "target": "sepal_length", "type": "Regression", "partitioning": true}, "train": {"k_fold": 5, "train_ratio": 0.8, "random_seed": 42}, "feature_handling": {"sepal_length": {"feature_name": "sepal_length", "is_selected": true, "feature_variable_type": "float64", "feature_details": {}}, "sepal_width": {"feature_name": "sepal_width", "is_selected": true, "feature_variable_type": "float64", "feature_details": {"numerical_handling": null, "rescaling": false, "scaling_type": null, "make_derived_feats": false, "missing_values": false, "impute_with": null}}, "petal_length": {"feature_name": "petal_length", "is_selected": false, "feature_variable_type": "float64", "feature_details": {"numerical_handling": null, "rescaling": false, "scaling_type": null, "make_derived_feats": false, "missing_values": "Impute", "impute_with": null}}, "petal_width": {"feature_name": "petal_width", "is_selected": false, "feature_variable_type": "float64", "feature_details": {"numerical_handling": null, "rescaling": false, "scaling_type": null, "make_derived_feats": false, "missing_values": "Impute", "impute_with": null}}, "species": {"feature_name": "species", "is_selected": false, "feature_variable_type": "object", "feature_details": {"numerical_handling": null, "rescaling": false, "scaling_type": null, "make_derived_feats": false, "missing_values": "Impute", "impute_with": null}}}, "algorithms": {"RandomForestRegressor": {"model_name": "RandomForestRegressor", "is_selected": false, "random_state": [42]}, "LinearRegression": {"model_name": "LinearRegression", "is_selected": false, "random_state": [42]}, "RidgeRegression": {"model_name": "RidgeRegression", "is_selected": false, "random_state": [42]}, "LassoRegression": {"model_name": "LassoRegression", "is_selected": false, "random_state": [42]}, "ElasticNetRegression": {"model_name": "ElasticNetRegression", "is_selected": false, "random_state": [42]}, "xg_boost": {"model_name": "xg_boost", "is_selected": false, "random_state": [42]}, "DecisionTreeRegressor": {"model_name": "DecisionTreeRegressor", "is_selected": false, "random_state": [42]}, "SVM": {"model_name": "SVM", "is_selected": false, "random_state": [42]}, "KNN": {"model_name": "KNN", "is_selected": true, "random_state": [42], "k_value": [78], "distance_weighting": [true], "neighbour_finding_algorithm": "auto", "p_value": 1}, "neural_network": {"model_name": "neural_network", "is_selected": false, "random_state": [42]}}}}

requirements.txt

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+joblib==1.1.0
+matplotlib==3.5.0
+numpy==1.21.0
+pandas==1.4.0
+scikit-learn==1.0.2
+xgboost==1.5.0

src/.ipynb_checkpoints/data_reader-checkpoint.py

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+# Contains the logic for reading and parsing the RTF file and extracting JSON content
+import pandas as pd
+import json
+from striprtf.striprtf import rtf_to_text
+from sklearn.preprocessing import train_test_split
+
+
+class DataReader:
+    def rtf_parser(self, file_path, encoding='utf-8'):
+        # Read the RTF file
+        with open(file_path, 'r', encoding=encoding) as file:
+            rtf_content = file.read()
+        
+        # Convert the RTF content to text
+        text_content = rtf_to_text(rtf_content)
+        
+        return text_content
+    
+
+    def rtf_to_json_parser(self, rtf_file_path):
+        plain_text = self.rtf_parser(rtf_file_path)
+        json_data = json.loads(plain_text)
+        self.json_content = json_data
+        return json_data
+    
+    def get_selected_features_and_details(self):
+        selected_features  = []
+        feature_details = {}
+        design_state = self.json_content["design_state_data"]
+        feature_handling = design_state["feature_handling"]
+        target_variable = design_state["target"]["target"]
+        for feature, details in feature_handling.items():
+            if(details["is_selected"]):
+                name = details["feature_name"]
+                selected_features.append(name)
+                feature_details[name] = details
+        selected_features.remove(target_variable)
+        return selected_features, feature_details
+    
+    
+    def get_problem_type_and_target_variable(self):
+        design_state = self.json_content["design_state_data"]
+        problem_type  = design_state["target"]["prediction_type"]
+        target_variable = design_state["target"]["target"]
+        return problem_type,target_variable

src/.ipynb_checkpoints/feature_handler-checkpoint.py

@@ -0,0 +1,101 @@
+# Contains classes and functions for handling and transforming 
+# features based on the JSON file information.
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+
+class FeatureHandler:
+    def __init__(self, json_content):
+        self.json_content = json_content
+
+    def impute_missing_values(self, feature_details, X_train, X_test=None):
+        mean_impute_features = []
+        median_impute_features = []
+        mode_impute_features = []
+        for feature, details in feature_details.items():
+            if details["missing_values"] == "Impute":
+                if "mean" in details["impute_with"].lower() or "average" in details["impute_with"].lower():
+                    mean_impute_features.append(feature)
+                elif "median" in details["impute_with"].lower():
+                    median_impute_features.append(feature)
+                elif "mode" in details["impute_with"].lower() or "most frequent" in details["impute_with"].lower():
+                    mode_impute_features.append(feature)
+        if mean_impute_features:
+            X_train[mean_impute_features] = X_train[mean_impute_features].fillna(X_train[mean_impute_features].mean())
+        if median_impute_features:
+            X_train[median_impute_features] = X_train[median_impute_features].fillna(X_train[median_impute_features].median())
+        if mode_impute_features:
+            X_train[mode_impute_features] = X_train[mode_impute_features].fillna(X_train[mode_impute_features].mode().iloc[0])
+        if X_test is not None:
+            if mean_impute_features:
+                X_test[mean_impute_features] = X_test[mean_impute_features].fillna(X_train[mean_impute_features].mean())
+            if median_impute_features:
+                X_test[median_impute_features] = X_test[median_impute_features].fillna(X_train[median_impute_features].median())
+            if mode_impute_features:
+                X_test[mode_impute_features] = X_test[mode_impute_features].fillna(X_train[mode_impute_features].mode().iloc[0])
+        return X_train, X_test
+
+    
+
+    # TODO: Add imputation for categorical features
+    def scale_features(self, feature_details, X_train, X_test=None):
+        min_max_scaler_features = []
+        standard_scaler_features = []
+        for feature, details in feature_details.items():
+            if details["rescaling"] == "MinMaxScaler":
+                min_max_scaler_features.append(feature)
+            elif details["rescaling"] == "StandardScaler":
+                standard_scaler_features.append(feature)
+        
+        if min_max_scaler_features:
+            scaler = MinMaxScaler()
+            X_train_scaled = scaler.fit_transform(X_train[min_max_scaler_features])
+            X_train[min_max_scaler_features] = X_train_scaled
+        if standard_scaler_features:
+            scaler = StandardScaler()
+            X_train_scaled = scaler.fit_transform(X_train[standard_scaler_features])
+            X_train[standard_scaler_features] = X_train_scaled
+        if X_test is not None:
+            if min_max_scaler_features:
+                X_test_scaled = scaler.fit_transform(X_test[min_max_scaler_features])
+                X_test[min_max_scaler_features] = X_test_scaled
+            if standard_scaler_features:
+                X_test_scaled = scaler.fit_transform(X_test[standard_scaler_features])
+                X_test[standard_scaler_features] = X_test_scaled
+        return X_train, X_test
+
+    
+    def transform_X_features(self, X_train, X_test, feature_details):
+        X_train_transformed, X_test_transformed = self.impute_missing_values(feature_details, X_train, X_test)
+        X_train_transformed, X_test_transformed = self.scale_features(feature_details, X_train_transformed, X_test_transformed)
+
+    # tokenize and hash the target variable
+    def tokenize_target_variable(self, y_train, y_test):
+        # tokenize the target variable
+        y_train_tokenized = y_train.apply(lambda x: x.split("-")[1])
+        y_train_encoded = pd.get_dummies(y_train_tokenized, prefix="Iris")
+        y_test_tokenized = y_test.apply(lambda x: x.split("-")[1])
+        y_test_encoded = pd.get_dummies(y_test_tokenized, prefix="Iris")
+        return y_train_encoded, y_test_encoded
+
+    def transform_y_features(self, y_train, y_test, feature_details, target_variable):
+        pass    
+
+    def get_split_dataset(self, selected_features):
+        design_state = self.json_content["design_state_data"]
+        dataset = design_state["session_info"]["dataset"]
+        target_variable = design_state["target"]["target"]
+        
+        train_info = design_state["train"]
+        train_ratio = train_info["train_ratio"]
+        random_seed = train_info["random_seed"]
+
+        DATASET_PATH = "data/"+dataset
+        df = pd.read_csv(DATASET_PATH)
+        X = df[selected_features]
+        Y = df[target_variable]
+
+        X_train, X_test, y_train, y_test = train_test_split(X, Y, train_size=train_ratio, 
+                                                            random_state=random_seed)
+        
+        return X_train, X_test, y_train, y_test

src/app.py

@@ -0,0 +1,354 @@
+import streamlit as st
+from data_reader import DataReader
+from datetime import datetime
+from feature_handler import FeatureHandler
+from model_trainer import ModelTrainer
+from evaluator import Evaluator
+from config import * 
+import pandas as pd
+import json
+
+
+def extract_column_info(df):
+    column_info = {}
+    for column in df.columns:
+        column_info[column] = {
+            "feature_name": column,
+            "is_selected": True, 
+            "feature_variable_type": str(df[column].dtype),
+            "feature_details": {
+                "numerical_handling": None,
+                "rescaling": False,
+                "scaling_type": None,
+                "make_derived_feats": False,
+                "missing_values": "Impute",
+                "impute_with": None
+            }
+        }
+    return column_info
+
+
+def extract_algorithms_info(algo_list):
+    algo_info = {}
+    for algo in algo_list:
+        algo_info[algo] = {
+            "model_name" : algo,
+            "is_selected" : False,
+            "random_state" : [42]
+        }
+    return algo_info
+
+
+def generate_json(session_name, dataset_name, target, train, feature_handling, algorithms):
+    json_data = {
+        "session_name": session_name,
+        "session_description": session_name,
+        "design_state_data": {
+            "session_info": {
+                "dataset": dataset_name,
+                "session_name": session_name,
+                "session_description": session_name
+            },
+            "target": target,
+            "train": train,
+            "feature_handling": feature_handling,
+            "algorithms": algorithms
+        }
+    }
+    return json_data
+
+
+
+def train_models(save_file_path, json_file):
+    if json_file is not None:
+        with st.spinner('Hang On, Training Models For You...'):
+            # Read the RTF file and parse the JSON content
+            data_reader = DataReader(rtf_file_path=save_file_path)
+            json_content = data_reader.rtf_to_json_parser()
+
+            # Extract dataset information from JSON
+            problem_type, target_variable = data_reader.get_problem_type_and_target_variable()
+
+            # Extract feature names and target variable from JSON content
+            selected_features, feature_details = data_reader.get_selected_features_and_details()
+
+            # Transform features 
+            feature_handler = FeatureHandler(json_content)
+            X_train, X_test, y_train, y_test = feature_handler.get_split_dataset(selected_features)    
+            
+            X_train_transformed , X_test_transformed = feature_handler.transform_X_features(X_train, X_test, feature_details)
+            y_train_transformed , y_test_transformed = feature_handler.transform_y_features(y_train, y_test, feature_details, target_variable)
+            
+            # Model building and hyperparameter tuning
+            selected_models, model_parameters = data_reader.get_selected_models()
+            model_trainer = ModelTrainer(json_content)
+            trained_models = model_trainer.build_and_tune_model(X_train_transformed, y_train_transformed, 
+                                                            problem_type, selected_models, model_parameters)
+            
+            
+            # Evaluate the model
+            evaluator = Evaluator(json_content, problem_type, target_variable)
+            evaluation_results = evaluator.evaluate_model(trained_models, X_test_transformed, y_test_transformed)
+            # display bar chart of evaluation results
+            st.subheader("Different Model Comparison")
+            evaluator.display_metrics(evaluation_results)
+
+
+            
+    else:
+        st.error("Please upload a JSON file first.")
+
+
+def create_json_and_train():
+    
+    st.write("### Upload Dataset: ")
+    uploaded_file = st.file_uploader("Upload Dataset CSV", type=['csv'])
+    
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        st.write("### Sample Data:")
+        st.write(df.head())
+
+        # Extract column information
+        column_info = extract_column_info(df)
+
+        # take input for prediction_type
+        st.write("### Select Prediction Parameters:")
+        prediction_type = st.selectbox("Prediction Type", ["Regression", "Classification"], key="prediction_selectbox")
+
+        # Checkbox for selecting target columns and feature details
+        target_variable = st.selectbox("Target Variable", df.columns, key="target_selectbox")
+
+        # add option to let user select how to encode target variable
+
+        column_info[target_variable]["feature_details"] = {}
+        # if target_variable is of category type, add option to label encode
+        if column_info[target_variable]["feature_variable_type"] == "object":
+            column_info[target_variable]["feature_details"]["text_handling"] = st.selectbox("Text Handling", ["Tokenize and hash", "Label Encoding"], key="text_handling_selectbox", index=0)
+
+        train = {}
+        train["k_fold"] = st.number_input("K-Fold", min_value=2, value=5, step=1, key="kfold")
+        train["train_ratio"] = st.number_input("Train Ratio", min_value=0.0, max_value=1.0, value=0.8, step=0.1, key="train_ratio")
+        train["random_seed"] = st.number_input("Random Seed", min_value=0, value=42, step=1, key="random_seed")
+
+        target = {"prediction_type": prediction_type, 
+                  "target": target_variable,
+                  "type": prediction_type,
+                  "partitioning": True}
+
+        st.write("### Select Columns to Include:")
+        for column in column_info:
+            if column != target_variable:
+                column_info[column]["is_selected"] = st.checkbox(column, key=f"{column}_checkbox", value=False)
+                if column_info[column]["is_selected"]:
+                    with st.expander(f"{column} Feature Handling", expanded=False):
+                        column_info[column]["feature_details"]["rescaling"] = st.checkbox("Rescaling", key=f"{column}_scaling_checkbox")
+                        if column_info[column]["feature_details"]["rescaling"] and column_info[column]["feature_variable_type"] != "object":
+                            column_info[column]["feature_details"]["scaling_type"] = st.selectbox("Scaling Type", ["MinMaxScaler", "StandardScaler"], key=f"{column}_scaling_type_select")
+                        column_info[column]["feature_details"]["missing_values"] = st.checkbox("Imputation", key=f"{column}_imputation_checkbox")
+                        if column_info[column]["feature_details"]["missing_values"]:
+                            column_info[column]["feature_details"]["impute_with"] = st.selectbox("Imputation With", ["Mean", "Median", "Mode", "Custom"], key=f"{column}_imputation_type_select")
+                            if column_info[column]["feature_details"]["impute_with"] == "Custom":
+                                column_info[column]["feature_details"]["custom_impute_value"] = st.text_input(f"Custom Impute Value", key=f"{column}_imputation_value_input")
+                        if column_info[column]["feature_variable_type"] == "object":
+                            column_info[column]["feature_details"]["encoding"] = st.selectbox("Encode Categorical Feature with", ["OridnalEncoder", "OneHotEncoder"], key = f"{column}_encoding_type")
+        # Checkbox for selecting columns
+        st.write(f"### Select {prediction_type} Algorithms:")
+        if prediction_type == "Regression":
+            algorithms_list = ["RandomForestRegressor", "LinearRegression", "RidgeRegression", "LassoRegression",
+                               "ElasticNetRegression","xg_boost", "DecisionTreeRegressor", "SVM", "KNN", "neural_network"]
+        else:
+            algorithms_list = ["RandomForestClassifier", "LogisticRegression",  "xg_boost", 
+                         "DecisionTreeClassifier", "SVM", "KNN", "neural_network"]
+        
+        algo_info = extract_algorithms_info(algorithms_list)
+        for algo in algo_info:
+            algo_info[algo]["is_selected"] = st.checkbox(algo, key=f"{algo}_checkbox")
+            if algo_info[algo]["is_selected"]:
+                with st.expander(f"{algo} HyperParameters", expanded=False):
+                    if algo == "RandomForestClassifier" or algo == "RandomForestRegressor":
+                        algo_info[algo]["min_trees"] = st.number_input("Minimum Trees", min_value=1, max_value=100, value=10, step=1, key=f"{algo}_min_trees")
+                        algo_info[algo]["max_trees"] = st.number_input("Maximum Trees", min_value=1, max_value=100, value=30, step=1, key=f"{algo}_max_trees")
+                        algo_info[algo]["min_depth"] = st.number_input("Minimum Depth", min_value=1, max_value=100, value=20, step=1, key=f"{algo}_min_depth")
+                        algo_info[algo]["max_depth"] = st.number_input("Maximum Depth", min_value=1, max_value=100, value=30, step=1, key=f"{algo}_max_depth")
+                        algo_info[algo]["min_samples_per_leaf_min_value"] = st.number_input("Minimum Samples Per Leaf", min_value=1, max_value=100, value=5, step=1, key=f"{algo}_min_samples_per_leaf")
+                        algo_info[algo]["min_samples_per_leaf_max_value"] = st.number_input("Maximum Samples Per Leaf", min_value=1, max_value=100, value=50, step=1, key=f"{algo}_max_samples_per_leaf")
+                    
+                    elif algo == "LinearRegression" or algo == "LogisticRegression" or algo == "ElasticNetRegression":
+                        algo_info[algo]["min_iter"] = st.number_input("Minimum Iterations", min_value=1, max_value=100, value=30, step=1, key=f"{algo}_min_iter")
+                        algo_info[algo]["max_iter"] = st.number_input("Maximum Iterations", min_value=1, max_value=100, value=50, step=1, key=f"{algo}_max_iter")
+                        algo_info[algo]["min_regparam"] = st.number_input("Minimum Regularization Parameter", min_value=0.0, max_value=1.0, value=0.5, step=0.1, key=f"{algo}_min_regparam")
+                        algo_info[algo]["max_regparam"] = st.number_input("Maximum Regularization Parameter", min_value=0.0, max_value=1.0, value=0.8, step=0.1, key=f"{algo}_max_regparam")
+                        algo_info[algo]["min_elasticnet"] = st.number_input("Minimum Elasticnet", min_value=0.0, max_value=1.0, value=0.5, step=0.1, key=f"{algo}_min_elasticnet")
+                        algo_info[algo]["max_elasticnet"] = st.number_input("Maximum Elasticnet", min_value=0.0, max_value=1.0, value=0.8, step=0.1, key=f"{algo}_max_elasticnet")
+
+                    elif algo == "RidgeRegression" or algo == "LassoRegression":
+                        algo_info[algo]["min_iter"] = st.number_input("Minimum Iterations", min_value=1, max_value=100, value=30, step=1, key=f"{algo}_min_iter")
+                        algo_info[algo]["max_iter"] = st.number_input("Maximum Iterations", min_value=1, max_value=100, value=50, step=1, key=f"{algo}_max_iter")
+                        algo_info[algo]["min_regparam"] = st.number_input("Minimum Regularization Parameter", min_value=0.0, max_value=1.0, value=0.5, step=0.1, key=f"{algo}_min_regparam")
+                        algo_info[algo]["max_regparam"] = st.number_input("Maximum Regularization Parameter", min_value=0.0, max_value=1.0, value=0.8, step=0.1, key=f"{algo}_max_regparam")
+                    
+                    elif algo == "DecisionTreeClassifier" or algo == "DecisionTreeRegressor":
+                        algo_info[algo]["min_depth"] = st.number_input("Minimum Depth", min_value=1, max_value=100, value=4, step=1, key=f"{algo}_min_depth")
+                        algo_info[algo]["max_depth"] = st.number_input("Maximum Depth", min_value=1, max_value=100, value=7, step=1, key=f"{algo}_max_depth")
+                        algo_info[algo]["use_gini"] = st.checkbox("Use Gini Index", value=False, key=f"{algo}_use_gini")
+                        algo_info[algo]["use_entropy"] = st.checkbox("Use Entropy", value=True, key=f"{algo}_use_entropy")
+                        algo_info[algo]["min_samples_per_leaf"] = st.text_input("Minimum Samples Per Leaf", placeholder="Enter comma separated list of values for min_samples_per_leaf", 
+                                                                                key=f"{algo}_min_samples_per_leaf")
+                        # check if min_samples_per_leaf is there
+                        if algo_info[algo]["min_samples_per_leaf"]:
+                            algo_info[algo]["min_samples_per_leaf"] = [int(x) for x in algo_info[algo]["min_samples_per_leaf"].split(",")]
+                        else:
+                            algo_info[algo]["min_samples_per_leaf"] = [12, 6]
+                        algo_info[algo]["use_best"] = st.checkbox("Use Best", value=True, key=f"{algo}_use_best")
+                        algo_info[algo]["use_random"] = st.checkbox("Use Random", value=True, key=f"{algo}_use_random")
+                    
+                    elif algo == "SVM":
+                        algo_info[algo]["linear_kernel"] = st.checkbox("Linear Kernel", value=True, key=f"{algo}_linear_kernel")
+                        algo_info[algo]["rep_kernel"] = st.checkbox("Rep Kernel", value=True, key=f"{algo}_rep_kernel")
+                        algo_info[algo]["polynomial_kernel"] = st.checkbox("Polynomial Kernel", value=True, key=f"{algo}_polynomial_kernel")
+                        algo_info[algo]["sigmoid_kernel"] = st.checkbox("Sigmoid Kernel", value=True, key=f"{algo}_sigmoid_kernel")
+                        algo_info[algo]["c_value"] = st.text_input("C Value", placeholder="Enter comma separated list of values for C Value", key=f"{algo}_c_value")
+                        # convert c values into list of integers
+                        if algo_info[algo]["c_value"]:
+                            algo_info[algo]["c_value"] = [int(x) for x in algo_info[algo]["c_value"].split(",")]   
+                        else:
+                            algo_info[algo]["c_value"] = [566, 79]
+                        algo_info[algo]["auto"] = st.checkbox("Auto", value=True, key=f"{algo}_auto")
+                        algo_info[algo]["scale"] = st.checkbox("Scale", value=True, key=f"{algo}_scale")
+                        algo_info[algo]["custom_gamma_values"] = st.checkbox("Custom Gamma Values", value=True, key=f"{algo}_custom_gamma_values")
+                        algo_info[algo]["tolerance"] = [st.number_input("Tolerance", min_value=0.0, max_value=1.0, value=0.001, step=0.001, key=f"{algo}_tolerance")]
+                        algo_info[algo]["max_iterations"] = st.number_input("Maximum Iterations", min_value=1, max_value=100, value=10, step=1, key=f"{algo}_max_iterations")
+                        if algo_info[algo]["max_iterations"]:
+                            algo_info[algo]["max_iterations"] = [algo_info[algo]["max_iterations"]]
+
+                    elif algo == "KNN":
+                        algo_info[algo]["k_value"] = st.text_input("K Value", placeholder="Enter comma separated list of values for K Value", key=f"{algo}_k_value")
+                        if algo_info[algo]["k_value"]:
+                            algo_info[algo]["k_value"] = [int(x) for x in algo_info[algo]["k_value"].split(",")]
+                        else:
+                            algo_info[algo]["k_value"] = [78]
+                        algo_info[algo]["distance_weighting"] = [st.checkbox("Distance Weighting", value=True, key=f"{algo}_distance_weighting")]
+                        algo_info[algo]["neighbour_finding_algorithm"] = st.selectbox("Neighbour Finding Algorithm", ["auto", "ball_tree", "kd_tree", "brute"], key=f"{algo}_neighbour_finding_algorithm", index=0)
+                        algo_info[algo]["p_value"] = st.number_input("P Value", min_value=1, max_value=2, value=1, step=1, key=f"{algo}_p_value")
+
+                    elif algo == "neural_network":
+                        algo_info[algo]["hidden_layer_sizes"] = st.text_input("Hidden Layer Sizes", placeholder="Enter comma separated list of values for Hidden Layer Sizes", key=f"{algo}_hidden_layer_sizes")
+                        if algo_info[algo]["hidden_layer_sizes"]:
+                            algo_info[algo]["hidden_layer_sizes"] = [int(x) for x in algo_info[algo]["hidden_layer_sizes"].split(",")]
+                        else:
+                            algo_info[algo]["hidden_layer_sizes"] = [67, 89]
+                        algo_info[algo]["activation"] = ""
+                        algo_info[algo]["alpha_value"] = [st.number_input("Alpha Value", min_value=0.0, max_value=1.0, value=0.01, step=0.0001, key=f"{algo}_alpha_value")]
+                        algo_info[algo]["max_iterations"] = [st.number_input("Max Iterations", min_value=0, max_value=1000, value=10, step=100, key=f"{algo}_max_iterations")]
+                        algo_info[algo]["convergence_tolerance"] = [st.number_input("Convergence Tolerance", min_value=0.0, max_value=1.0, value=0.1, step=0.0001, key=f"{algo}_convergence_tolerance")]
+                        algo_info[algo]["early_stopping"] = [st.checkbox("Early Stopping", value=True, key=f"{algo}_early_stopping")]
+                        algo_info[algo]["solver"] = [st.selectbox("Solver", ["lbfgs", "sgd", "adam"], key=f"{algo}_solver", index=2)]
+                        algo_info[algo]["shuffle_data"] = [st.checkbox("Shuffle Data", value=True, key=f"{algo}_shuffle_data")]
+                        algo_info[algo]["initial_learning_rate"] = [st.number_input("Initial Learning Rate", min_value=0.0, max_value=1.0, value=0.1, step=0.001, key=f"{algo}_initial_learning_rate")]
+                        algo_info[algo]["automatic_batching"] = [st.checkbox("Automatic Batching", value=True, key=f"{algo}_automatic_batching")]
+                        algo_info[algo]["beta_1"] = [st.number_input("Beta 1", min_value=0.0, max_value=1.0, value=0.1, step=0.1, key=f"{algo}_beta_1")]
+                        algo_info[algo]["beta_2"] = [st.number_input("Beta 2", min_value=0.0, max_value=1.0, value=0.1, step=0.1, key=f"{algo}_beta_2")]
+                        algo_info[algo]["epsilon"] = [st.number_input("Epsilon", min_value=0.0, max_value=1.0, value=0.1, step=0.1, key=f"{algo}_epsilon")]
+                        algo_info[algo]["power_t"] = [st.number_input("Power T", min_value=0.0, max_value=1.0, value=0.1, step=0.1, key=f"{algo}_power_t")]
+                        algo_info[algo]["momentum"] = [st.number_input("Momentum", min_value=0.0, max_value=1.0, value=0.1, step=0.1, key=f"{algo}_momentum")]
+                        algo_info[algo]["use_nesterov_momentum"] = [st.checkbox("Use Nesterov Momentum", value=False, key=f"{algo}_use_nesterov_momentum")]
+                    
+                    elif algo == "xg_boost":
+                        algo_info[algo]["use_gradient_boosted_tree"] = st.checkbox("Use Gradient Boosted Tree", value=True, key=f"{algo}_use_gradient_boosted_tree")
+                        algo_info[algo]["dart"] = st.checkbox("DART", value=True, key=f"{algo}_dart")
+                        algo_info[algo]["tree_method"] = [st.selectbox("Tree Method", ["exact", "approx", "hist"], key=f"{algo}_tree_method", index=1)]
+                        algo_info[algo]["max_num_of_trees"] = [st.number_input("Max Number of Trees", min_value=0, max_value=1000, value=10, step=100, key=f"{algo}_max_num_of_trees")]
+                        algo_info[algo]["early_stopping"] = st.checkbox("Early Stopping", value=True, key=f"{algo}_early_stopping")
+                        if algo_info[algo]["early_stopping"]:
+                            algo_info[algo]["early_stopping_rounds"] = [st.number_input("Early Stopping Rounds", min_value=0, max_value=1000, value=2, step=100, key=f"{algo}_early_stopping_rounds")]
+                        algo_info[algo]["max_depth_of_tree"] = [st.number_input("Max Depth of Tree", min_value=0, max_value=1000, value=10, step=100, key=f"{algo}_max_depth_of_tree")]
+                        algo_info[algo]["learningRate"] = [st.number_input("Learning Rate", min_value=0.0, max_value=1.0, value=0.1, step=0.001, key=f"{algo}_learningRate")]
+                        algo_info[algo]["l1_regularization"] = [st.number_input("L1 Regularization", min_value=0.0, max_value=1.0, value=0.1, step=0.001, key=f"{algo}_l1_regularization")]
+                        algo_info[algo]["l2_regularization"] = [st.number_input("L2 Regularization", min_value=0.0, max_value=1.0, value=0.1, step=0.001, key=f"{algo}_l2_regularization")]
+                        algo_info[algo]["gamma"] = [st.number_input("Gamma", min_value=0.0, max_value=1.0, value=0.1, step=0.001, key=f"{algo}_gamma")]
+                        algo_info[algo]["min_child_weight"] = [st.number_input("Min Child Weight", min_value=0.0, max_value=1.0, value=0.1, step=0.001, key=f"{algo}_min_child_weight")]
+                        algo_info[algo]["sub_sample"] = [st.number_input("Sub Sample", min_value=0.0, max_value=1.0, value=0.1, step=0.001, key=f"{algo}_sub_sample")]
+                        algo_info[algo]["col_sample_by_tree"] = [st.number_input("Column Sample By Tree", min_value=0.0, max_value=1.0, value=0.1, step=0.001, key=f"{algo}_col_sample_by_tree")]
+                        algo_info[algo]["replace_missing_values"] = st.checkbox("Replace Missing Values", value=True, key=f"{algo}_replace_missing_values")
+
+        # Generate JSON
+        if st.button("Generate JSON and train models"):
+            session_name = datetime.now().strftime('%Y%m%d_%H%M%S')
+            json_data = generate_json(session_name, uploaded_file.name, target, train, column_info, algo_info)
+            # save json to file
+            if json_data is not None:
+                current_time = datetime.now().strftime('%Y%m%d_%H%M%S')
+                extension = "json"
+                file_name = f"uploaded_{current_time}.{extension}"
+                save_file_path = '../data/'+file_name
+                
+                with open(save_file_path, 'w') as file:
+                    # file.write(json_data.read())
+                    json.dump(json_data, file)
+                    st.success("JSON file generated successfully, models are being trained!")
+
+                train_models(save_file_path, json_data)
+
+
+def upload_json_and_train():
+ 
+    st.write("### Upload JSON File")
+    json_file = st.file_uploader("Upload RTF/JSON/TXT file", type=["rtf", "json", "txt"])
+    
+    if json_file is not None:
+        current_time = datetime.now().strftime('%Y%m%d_%H%M%S')
+        extension = json_file.name.split('.')[-1]
+        file_name = f"{json_file.name.split('.')[0]}_{current_time}.{extension}"
+        save_file_path = '../data/'+file_name
+        
+        with open(save_file_path, 'wb') as file:
+            file.write(json_file.read())
+
+            st.success("File uploaded successfully, mdoels are ready to be trained!")
+
+    # create button to train models
+    if st.button("Train Models"):
+        if json_file is not None:
+            train_models(save_file_path, json_file)
+        else:
+            st.warning("Please upload a JSON file")
+
+def main():
+    
+    # 
+    main_heading = "<h1 style='text-align: center; color: #cce7ff; margin-bottom: 0; margin-top:-50px'>DataFlow Pro</h1>"
+    tagline = "<h4 style='text-align: center; color: #cce7ff; margin-top: -25px;'>Automating ML Workflow with Ease</h4>"
+    header_content = main_heading + tagline
+    st.markdown(header_content, unsafe_allow_html=True)
+    st.markdown("---")
+
+    st.subheader("Navigation")
+    st.write("If you want to create a JSON and train a model, please click on the <u><b>Create Json and Train Model</b></u> button.", unsafe_allow_html=True)
+    st.write("If you have an RTF/JSON/TXT file, please upload it and click on the <u><b>Upload Json and train model</b></u> button.", unsafe_allow_html=True)
+    page = st.radio(" ", ("Create Json and Train Model", "Upload Json and train model"), index= None)
+
+    if page == "Create Json and Train Model":
+        create_json_and_train()
+    elif page == "Upload Json and train model":
+        upload_json_and_train()
+    st.markdown("""
+    <style>
+        .footer {
+            position: fixed;
+            bottom: 0;
+            left: 0;
+            width: 100%;
+            background-color: #000000;
+            text-align: center;
+            padding: 10px 0;
+        }
+    </style>
+    <div class="footer">
+        <p>Made with ❤️ by Rupanshu Kapoor.</p>
+    </div>
+""", unsafe_allow_html=True)
+    
+
+if __name__ == '__main__':
+    main()

src/config.py

@@ -0,0 +1,35 @@
+# Optional. Contains configuration settings 
+# for your application (e.g., paths, hyperparameter ranges).
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.linear_model import SGDRegressor, LogisticRegression, Ridge, Lasso, ElasticNet
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.svm import SVC, SVR
+from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
+from sklearn.neural_network import MLPClassifier
+from xgboost import XGBClassifier, XGBRegressor
+
+
+
+RTF_FILE_PATH = "data/algoparams_from_ui1.json.rtf"
+
+
+model_dict = {
+    "RandomForestClassifier" : RandomForestClassifier(),
+    "RandomForestRegressor" : RandomForestRegressor(),
+    "LinearRegression": SGDRegressor(),
+    "LogisticRegression": LogisticRegression(),
+    "RidgeRegression": Ridge(),
+    "LassoRegression": Lasso(),
+    "ElasticNetRegression": ElasticNet(),
+    "XGBoostClassifier": XGBClassifier(),
+    "XGBoostRegressor": XGBRegressor(),
+    "DecisionTreeRegressor": DecisionTreeRegressor(),
+    "DecisionTreeClassifier":DecisionTreeClassifier(),
+    "SVMClassifier": SVC(),
+    "SVMRegressor": SVR(),
+    "KNNRegressor": KNeighborsRegressor(),
+    "KNNClassifier": KNeighborsClassifier(),
+    "neural_network": MLPClassifier()    
+}
+
+

src/data_reader.py

@@ -0,0 +1,69 @@
+# Contains the logic for reading and parsing the RTF file and extracting JSON content
+import pandas as pd
+import json
+from striprtf.striprtf import rtf_to_text
+import streamlit as st
+
+class DataReader:
+    def __init__(self, rtf_file_path):
+        self.json_content = None
+        self.rtf_file_path = rtf_file_path
+    def rtf_parser(self, file_path, encoding='utf-8'):
+        # Read the RTF file
+        with open(file_path, 'r', encoding=encoding) as file:
+            rtf_content = file.read()
+         
+        # Convert the RTF content to text
+        text_content = rtf_to_text(rtf_content)
+        
+        return text_content
+    
+
+    def rtf_to_json_parser(self):
+        # check for extension, if rtf convert to json
+        if self.rtf_file_path.split('.')[-1] == 'rtf':
+            plain_text = self.rtf_parser(self.rtf_file_path)
+            json_data = json.loads(plain_text)
+        elif self.rtf_file_path.split('.')[-1] == 'json' or self.rtf_file_path.split('.')[-1] == 'txt':
+            with open(self.rtf_file_path, 'r') as file:
+                json_data = json.load(file) 
+        else:
+            st.error("Invalid file type. Please upload a .rtf, .json or .txt file.")
+        self.json_content = json_data
+        return json_data
+    
+    def get_selected_features_and_details(self):
+        selected_features  = []
+        feature_details = {}
+        design_state = self.json_content["design_state_data"]
+        feature_handling = design_state["feature_handling"]
+        target_variable = design_state["target"]["target"]
+        for feature, details in feature_handling.items():
+            if(details["is_selected"]):
+                name = details["feature_name"]
+                selected_features.append(name)
+                feature_details[name] = details
+        selected_features.remove(target_variable)
+        return selected_features, feature_details
+    
+    
+    def get_problem_type_and_target_variable(self):
+        design_state = self.json_content["design_state_data"]
+        problem_type  = design_state["target"]["prediction_type"]
+        target_variable = design_state["target"]["target"]
+        return problem_type,target_variable
+    
+    def get_selected_models(self):
+        algorithms = self.json_content["design_state_data"]["algorithms"]
+        selected_algorithms = []
+        algo_hyperparameters = {}
+        for algo, details in algorithms.items():
+            if(details["is_selected"]):
+                selected_algorithms.append(algo)      
+                algo_hyperparameters[algo] = details
+                algo_hyperparameters[algo].pop("model_name")
+                algo_hyperparameters[algo].pop("is_selected")
+
+        return selected_algorithms, algo_hyperparameters
+    
+    

src/evaluator.py

@@ -0,0 +1,120 @@
+#  Contains classes and functions for evaluating trained 
+# models using the specified metrics.
+
+
+from sklearn.metrics import accuracy_score, root_mean_squared_error, r2_score, mean_squared_error,classification_report, confusion_matrix, ConfusionMatrixDisplay
+import json
+import matplotlib.pyplot as plt
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+class Evaluator:
+    def __init__(self, json_content, problem_type, target_variable):
+        self.json_content = json_content
+        self.problem_type = problem_type
+    
+    def evaluate_model(self, models, X_test, y_test):
+        """Evaluates the model using specified metrics and returns results."""
+        metrics = {}
+        for model_name, model in models.items():
+            metrics[model_name] = {}
+            print(f"Evaluating model: {model_name}")
+            predictions = model.predict(X_test)
+        
+            # Choose evaluation metrics based on problem type
+            st.subheader(f"Model: {model_name}")
+            if self.problem_type == 'Classification':
+                self.log_confusion_matrix(y_test, predictions, model_name, model)
+                accuracy = self.log_classification_report(y_test, predictions, model_name)
+                metrics[model_name]["accuracy"] = accuracy
+            else:  # 'regression'
+                rmse_score = self.log_rmse(y_test, predictions, model_name)
+                r2_score = self.log_r2(y_test, predictions, model_name)
+                adj_r2_score = self.log_adj_r2(X_test,y_test, predictions, model_name)
+                metrics[model_name]["rmse"] = rmse_score
+                metrics[model_name]["r2"] = r2_score
+                metrics[model_name]["adj_r2"] = adj_r2_score
+                
+        return metrics
+            
+        # return metrics
+    
+    def save_metrics(self, metrics, file_path: str):
+        """Saves evaluation metrics to a file."""
+        with open(file_path, 'w') as file:
+            json.dump(metrics, file)
+
+
+    def log_confusion_matrix(self, y_test, predictions, model_name, model):
+        """Logs the confusion matrix."""
+        cm = confusion_matrix(y_test, predictions, labels=model.classes_)
+        # st.set_option('deprecation.showPyplotGlobalUse', False)
+        st.markdown(f"#### Confusion matrix for : {model_name} ")
+        fig, ax = plt.subplots()
+        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)
+        disp.plot(ax=ax)
+        st.pyplot(fig)
+
+    
+    def log_classification_report(self, y_test, predictions, model_name):
+        """Logs the classification report."""
+        st.markdown(f"#### Classification report for: {model_name} ")
+        accuracy = accuracy_score(y_test, predictions)
+        cr = classification_report(y_test, predictions, output_dict=True)
+        report_df = pd.DataFrame(cr).T
+        report_df = report_df.rename(columns={'precision': 'Precision', 'recall': 'Recall', 'f1-score': 'F1-Score', 'support': 'Support'})
+        st.table(report_df)
+        return round(accuracy,2)
+
+    def log_rmse(self, y_test, predictions, model_name):
+        """Logs the root mean squared error."""
+        rmse = root_mean_squared_error(y_test, predictions)
+        st.markdown(f"RMSE for {model_name}: {round(rmse,2)} ")
+        return round(rmse,2)
+
+    def log_r2(self, y_test, predictions, model_name):
+        """Logs the R-squared score."""
+        r2 = r2_score(y_test, predictions)
+        st.markdown(f"R-squared score for {model_name}: {round(r2,2)} ")
+        return round(r2,2)
+
+
+    def log_adj_r2(self,X_test, y_test, predictions, model_name):
+        """Logs the adjusted R-squared score."""
+        sample_size, n_variables = X_test.shape
+        r2 = r2_score(y_test, predictions)
+        adj_r2 = 1 - ((1 - r2) * (sample_size - 1)) / (sample_size - n_variables - 1)
+        print(f" model: {model_name}")
+        st.markdown(f"Adjusted R-squared score for {model_name}: {round(r2,2)} ")
+        return round(adj_r2,2)
+
+    def display_metrics(self, metrics):
+        
+        available_metrics = list(next(iter(metrics.values())).keys())
+        available_models = list(metrics.keys())
+
+        num_models = len(available_models)
+        hue_colors = plt.cm.tab10(np.linspace(0, 1, num_models))
+
+        for metric in available_metrics:
+            fig, ax = plt.subplots(figsize=(10, 5))
+            
+            for i, model in enumerate(available_models):
+                metric_value = metrics[model][metric]
+                bar = ax.bar(model, metric_value, color=hue_colors[i], label=model)
+
+                for rect in bar:
+                    height = rect.get_height()
+                    ax.annotate('{}'.format(round(height, 2)),
+                                xy=(rect.get_x() + rect.get_width() / 2, height),
+                                xytext=(0, 3), textcoords="offset points",
+                                ha='center', va='bottom')
+            
+            ax.set_xlabel('Algorithm Models')
+            ax.set_ylabel(metric.upper())
+            ax.legend()
+            plt.xticks(rotation=45)
+            plt.title(f'{metric.upper()}')
+            st.pyplot(fig)
+

src/feature_handler.py

@@ -0,0 +1,169 @@
+# Contains classes and functions for handling and transforming 
+# features based on the JSON file information.
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder, OrdinalEncoder, OneHotEncoder
+import streamlit as st
+
+class FeatureHandler:
+    def __init__(self, json_content):
+        self.json_content = json_content
+
+    def impute_missing_values(self, feature_details, X_train, X_test=None):
+        mean_impute_features = []
+        median_impute_features = []
+        mode_impute_features = []
+        custom_impute_features = []
+        for feature in X_train.columns:
+            details = feature_details[feature]['feature_details']
+            if details["missing_values"]:
+                if details["impute_with"].lower() == "mean":
+                    mean_impute_features.append(feature)
+                elif details["impute_with"].lower() == "median":
+                    median_impute_features.append(feature)
+                elif details["impute_with"].lower() == "mode":
+                    mode_impute_features.append(feature)
+                elif details["impute_with"].lower() == "custom":
+                    custom_impute_features.append(feature)
+
+        if mean_impute_features:
+            X_train[mean_impute_features] = X_train[mean_impute_features].fillna(X_train[mean_impute_features].mean())
+        if median_impute_features:
+            X_train[median_impute_features] = X_train[median_impute_features].fillna(X_train[median_impute_features].median())
+        if mode_impute_features:
+            X_train[mode_impute_features] = X_train[mode_impute_features].fillna(X_train[mode_impute_features].mode().iloc[0])
+        if custom_impute_features:
+            for feature in custom_impute_features:
+                X_train[feature] = X_train[feature].fillna(feature_details[feature]['feature_details']['custom_impute_value'])
+        if X_test is not None:
+            if mean_impute_features:
+                X_test[mean_impute_features] = X_test[mean_impute_features].fillna(X_train[mean_impute_features].mean())
+            if median_impute_features:
+                X_test[median_impute_features] = X_test[median_impute_features].fillna(X_train[median_impute_features].median())
+            if mode_impute_features:
+                X_test[mode_impute_features] = X_test[mode_impute_features].fillna(X_train[mode_impute_features].mode().iloc[0])
+            if custom_impute_features:
+                for feature in custom_impute_features:
+                    X_test[feature] = X_test[feature].fillna(feature_details[feature]['feature_details']['custom_impute_value'])
+        return X_train, X_test
+
+    
+
+    # TODO: Add imputation for categorical features
+    def scale_features(self, feature_details, X_train, X_test=None):
+        min_max_scaler_features = []
+        standard_scaler_features = []
+        # for feature, details in feature_details.items():
+        for feature in feature_details.keys():
+            details = feature_details[feature]['feature_details']
+            if details.get("rescaling"):
+                if details["rescaling"]!= "No rescaling" and details["scaling_type"] == "MinMaxScaler" :
+                    min_max_scaler_features.append(feature)
+                elif details["rescaling"] != "No rescaling" and details["scaling_type"] == "StandardScaler" :
+                    standard_scaler_features.append(feature)
+        if min_max_scaler_features:
+            scaler = MinMaxScaler()
+            X_train_scaled = scaler.fit_transform(X_train[min_max_scaler_features])
+            X_train[min_max_scaler_features] = X_train_scaled
+        if standard_scaler_features:
+            scaler = StandardScaler()
+            X_train_scaled = scaler.fit_transform(X_train[standard_scaler_features])
+            X_train[standard_scaler_features] = X_train_scaled
+        if X_test is not None:
+            if min_max_scaler_features:
+                X_test_scaled = scaler.fit_transform(X_test[min_max_scaler_features])
+                X_test[min_max_scaler_features] = X_test_scaled
+            if standard_scaler_features:
+                X_test_scaled = scaler.fit_transform(X_test[standard_scaler_features])
+                X_test[standard_scaler_features] = X_test_scaled
+        return X_train, X_test
+
+    def encode_features(self, feature_details, X_train, X_test=None):
+       
+        ordinal_encoder_cols = []
+        one_hot_encoder_cols = []
+        for feature in X_train.columns:
+            if feature_details[feature]["feature_variable_type"] == "object":
+                details = feature_details[feature]['feature_details']
+                if details["encoding"] == "OridnalEncoder":
+                    ordinal_encoder_cols.append(feature)
+                elif details["encoding"] == "OneHotEncoder":
+                    one_hot_encoder_cols.append(feature)
+        if ordinal_encoder_cols:
+            ordinal_encoder = OrdinalEncoder()
+            X_train[ordinal_encoder_cols] = ordinal_encoder.fit_transform(X_train[ordinal_encoder_cols])
+            if X_test is not None:
+                X_test[ordinal_encoder_cols] = ordinal_encoder.transform(X_test[ordinal_encoder_cols])
+   
+        if one_hot_encoder_cols:
+            one_hot_encoder = OneHotEncoder( drop="first", sparse_output=False)
+            temp_df = pd.DataFrame(one_hot_encoder.fit_transform(X_train[one_hot_encoder_cols]), 
+                                   columns=one_hot_encoder.get_feature_names_out(),
+                                   index=X_train.index)
+            X_train = X_train.drop(one_hot_encoder_cols, axis=1)
+            X_train = pd.concat([X_train, temp_df], axis=1)
+            
+            if X_test is not None:
+                temp_df = pd.DataFrame(one_hot_encoder.transform(X_test[one_hot_encoder_cols]), 
+                                       columns=one_hot_encoder.get_feature_names_out(), 
+                                       index=X_test.index)
+                X_test = X_test.drop(one_hot_encoder_cols, axis=1)
+                X_test = pd.concat([X_test, temp_df], axis=1)
+        return X_train, X_test
+
+    def transform_X_features(self, X_train, X_test, feature_details):
+        
+        X_train_transformed, X_test_transformed = self.impute_missing_values(feature_details, X_train, X_test)
+        X_train_transformed, X_test_transformed = self.encode_features(feature_details, X_train_transformed, X_test_transformed)
+        X_train_transformed, X_test_transformed = self.scale_features(feature_details, X_train_transformed, X_test_transformed)
+        return X_train_transformed, X_test_transformed
+    # tokenize and hash the target variable
+    def tokenize_target_variable(self, y_train, y_test):
+        details = self.json_content["design_state_data"]["feature_handling"]
+        feature_details =  details[y_train.name]["feature_details" ]
+        if feature_details["text_handling"] == "Tokenize and hash":
+        # tokenize the target variable
+            label_encoder = LabelEncoder()
+            y_train_tokenized = y_train.apply(lambda x: x.split("-")[1])
+            y_train_encoded = label_encoder.fit_transform(y_train_tokenized)
+
+            y_test_tokenized = y_test.apply(lambda x: x.split("-")[1])
+            y_test_encoded = label_encoder.transform(y_test_tokenized)
+            return y_train_encoded, y_test_encoded
+        
+
+    def label_encode_target_variable(self, y_train, y_test):
+        label_encoder = LabelEncoder()
+        y_train_encoded = label_encoder.fit_transform(y_train)
+        y_test_encoded = label_encoder.transform(y_test)
+        return y_train_encoded, y_test_encoded
+
+
+    def transform_y_features(self, y_train, y_test, feature_details, target_variable):
+        if feature_details[target_variable]["feature_variable_type"] == "object":
+            if feature_details[target_variable]["feature_details"]["text_handling"] == "Tokenize and hash":
+                y_train_transformed, y_test_transformed = self.tokenize_target_variable(y_train, y_test)
+            elif feature_details[target_variable]["feature_details"]["text_handling"] == "Label Encoding":
+                y_train_transformed, y_test_transformed = self.label_encode_target_variable(y_train, y_test)
+            return y_train_transformed, y_test_transformed
+        else:
+            return y_train, y_test
+
+    def get_split_dataset(self, selected_features):
+        design_state = self.json_content["design_state_data"]
+        dataset = design_state["session_info"]["dataset"]
+        target_variable = design_state["target"]["target"]
+        
+        train_info = design_state["train"]
+        train_ratio = train_info["train_ratio"]
+        random_seed = train_info["random_seed"]
+
+        DATASET_PATH = "../data/"+dataset
+        df = pd.read_csv(DATASET_PATH)
+        X = df[selected_features]
+        Y = df[target_variable]
+
+        X_train, X_test, y_train, y_test = train_test_split(X, Y, train_size=train_ratio, 
+                                                            random_state=random_seed)
+        
+        return X_train, X_test, y_train, y_test

src/model_trainer.py

@@ -0,0 +1,302 @@
+#  Contains classes and functions for model 
+# building, hyperparameter tuning, and training models.
+
+import numpy as np
+from sklearn.model_selection import GridSearchCV
+from joblib import dump  # For saving models
+from config import model_dict
+import streamlit as st
+class ModelTrainer:
+    def __init__(self, json_content: dict):
+        self.json_content = json_content
+        self.k_fold = json_content["design_state_data"]["train"]["k_fold"]
+        if not self.k_fold:
+            self.k_fold = None
+        self.random_state = [42]
+        self.num_iter = 3
+        
+    
+    def tune_random_forest(self, model, X_train, y_train, model_name, model_parameters):
+        params = {"random_state": self.random_state}
+        min_trees = model_parameters[model_name]["min_trees"]
+        max_trees = model_parameters[model_name]["max_trees"]
+        params["n_estimators"] = np.linspace(min_trees, max_trees, self.num_iter, dtype=int)
+
+        min_depth = model_parameters[model_name]["min_depth"]
+        max_depth = model_parameters[model_name]["max_depth"]
+        params["max_depth"] = np.linspace(min_depth, max_depth, self.num_iter, dtype=int)
+
+        min_samples_per_leaf = model_parameters[model_name]["min_samples_per_leaf_min_value"]
+        max_samples_per_leaf = model_parameters[model_name]["min_samples_per_leaf_max_value"]
+        params["min_samples_leaf"] = np.linspace(min_samples_per_leaf, max_samples_per_leaf, self.num_iter, dtype=int)
+
+        if model_parameters[model_name].get("random_state"):
+            params["random_state"] = model_parameters[model_name]["random_state"]
+
+        gcv = GridSearchCV(model, params, cv=self.k_fold) 
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+
+    def tune_linear_elasticnet_regression(self, model, X_train, y_train, model_name, model_parameters):
+        params = {"random_state": self.random_state}
+        if model_parameters[model_name].get("random_state"):
+            params["random_state"] = model_parameters[model_name]["random_state"]
+
+        min_iter = model_parameters[model_name]["min_iter"]
+        max_iter = model_parameters[model_name]["max_iter"]
+        params["max_iter"] = np.linspace(min_iter, max_iter, self.num_iter, dtype=int)
+
+        min_reg = model_parameters[model_name]["min_regparam"]
+        max_reg = model_parameters[model_name]["max_regparam"]
+        params["alpha"] = np.logspace(min_reg, max_reg, self.num_iter)
+
+        min_elasticnet = model_parameters[model_name]["min_elasticnet"]
+        max_elasticnet = model_parameters[model_name]["max_elasticnet"]
+        params["l1_ratio"] = np.linspace(min_elasticnet, max_elasticnet, self.num_iter)
+
+        gcv = GridSearchCV(model, params, cv=self.k_fold)
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+
+  
+    
+    def tune_logistic_regression(self, model, X_train, y_train, model_parameters):
+        params = {"random_state": self.random_state}
+        if model_parameters["LogisticRegression"].get("random_state"):
+            params["random_state"] = model_parameters["LogisticRegression"]["random_state"]
+        
+        min_iter = model_parameters["LogisticRegression"]["min_iter"]
+        max_iter = model_parameters["LogisticRegression"]["max_iter"]
+        params["max_iter"] = np.linspace(min_iter, max_iter, self.num_iter, dtype=int)
+
+        min_reg = model_parameters["LogisticRegression"]["min_regparam"]
+        max_reg = model_parameters["LogisticRegression"]["max_regparam"]
+        params["C"] = np.logspace(min_reg, max_reg, self.num_iter)
+
+        min_elasticnet = model_parameters["LogisticRegression"]["min_elasticnet"]
+        max_elasticnet = model_parameters["LogisticRegression"]["max_elasticnet"]
+        params["l1_ratio"] = np.linspace(min_elasticnet, max_elasticnet, self.num_iter)
+
+        gcv = GridSearchCV(model, params, cv=self.k_fold)
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+
+    def tune_ridge_lasso_regression(self, model, X_train, y_train, model_name, model_parameters):
+        params = {"random_state": self.random_state}
+        if model_parameters[model_name].get("random_state"):
+            params["random_state"] = model_parameters[model_name]["random_state"]
+        
+        min_iter = model_parameters[model_name]["min_iter"]
+        max_iter = model_parameters[model_name]["max_iter"]
+        params["max_iter"] = np.linspace(min_iter, max_iter, self.num_iter, dtype=int)
+
+        min_regparam = model_parameters[model_name]["min_regparam"]
+        max_regparam = model_parameters[model_name]["max_regparam"]
+        params["alpha"] = np.logspace(min_regparam, max_regparam, self.num_iter)
+
+        gcv = GridSearchCV(model, params, cv=self.k_fold)
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+
+
+    def tune_decision_tree(self, model, X_train, y_train, model_name, model_parameters):
+        params = {"random_state": self.random_state}
+        if model_parameters[model_name].get("random_state"):
+            params["random_state"] = model_parameters[model_name]["random_state"]
+        
+        min_depth = model_parameters[model_name]["min_depth"]
+        max_depth = model_parameters[model_name]["max_depth"]
+        params["max_depth"] = np.linspace(min_depth, max_depth, self.num_iter, dtype=int)
+
+        criterion = []
+        if model_parameters[model_name]["use_gini"]:
+            criterion.append("gini")
+        if model_parameters[model_name]["use_entropy"]:
+            criterion.append("entropy")
+        params["criterion"] = criterion
+
+        splitter = []
+        if model_parameters[model_name]["use_random"]:
+            splitter.append("random")
+        if model_parameters[model_name]["use_best"]:
+            splitter.append("best")
+        params["splitter"] = splitter
+
+        if model_parameters[model_name].get("min_samples_per_leaf"):
+            params["min_samples_leaf"] = model_parameters[model_name]["min_samples_per_leaf"]
+
+        gcv = GridSearchCV(model, params, cv=self.k_fold)
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+    
+    def tune_svm(self, model, X_train, y_train, model_parameters):
+        params = {}
+        
+        kernel = []
+        if model_parameters["SVM"]["linear_kernel"]:
+            kernel.append("linear")
+        if model_parameters["SVM"]["rep_kernel"]:
+            kernel.append("rbf")
+        if model_parameters["SVM"]["polynomial_kernel"]:
+            kernel.append("poly")
+        if model_parameters["SVM"]["sigmoid_kernel"]:
+            kernel.append("sigmoid")
+        params["kernel"] = kernel
+
+        params["C"] = model_parameters["SVM"]["c_value"]
+
+        gamma = []
+        if model_parameters["SVM"]["scale"]:
+            gamma.append("scale")
+        if model_parameters["SVM"]["auto"]:
+            gamma.append("auto")
+
+        params["gamma"] = gamma
+        
+        params["max_iter"] = model_parameters["SVM"]["max_iterations"]
+        params["tol"] = model_parameters["SVM"]["tolerance"]
+
+        gcv = GridSearchCV(model, params, cv=self.k_fold)
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+
+    def tune_knn(self, model, X_train, y_train, model_parameters):
+        params = {}
+        
+        params["n_neighbors"] = model_parameters["KNN"]["k_value"]
+
+        if model_parameters["KNN"].get("distance_weighting"):
+            params["weights"] = ["distance"]
+
+        if model_parameters["KNN"]["neighbour_finding_algorithm"] == "Automatic":
+            params["algorithm"] = "auto"
+        
+        gcv = GridSearchCV(model, params, cv=self.k_fold)
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+        pass
+
+
+    def tune_neural_network(self, model, X_train, y_train, model_parameters):
+        parameters = model_parameters["neural_network"]
+        params = {"random_state": self.random_state,
+                  "hidden_layer_sizes": parameters["hidden_layer_sizes"],
+                  "alpha": parameters["alpha_value"],
+                  "max_iter": parameters["max_iterations"],
+                  "tol": parameters["convergence_tolerance"],
+                  "early_stopping": parameters["early_stopping"],
+                  "solver": parameters["solver"],
+                  "shuffle": parameters["shuffle_data"],
+                  "learning_rate_init": parameters["initial_learning_rate"],
+                  "batch_size": parameters["automatic_batching"],
+                  "beta_1": parameters["beta_1"],
+                  "beta_2": parameters["beta_2"],
+                  "epsilon": parameters["epsilon"],
+                  "power_t": parameters["power_t"],
+                  "momentum": parameters["momentum"],
+                  "nesterovs_momentum": parameters["use_nesterov_momentum"],
+                  } 
+        
+        if parameters.get("random_state"):
+            params["random_state"] = parameters["random_state"]
+        
+        if parameters.get("activation"):
+            params["activation"] = parameters["activation"]
+        
+        gcv = GridSearchCV(model, params, cv=self.k_fold)
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+    
+    def tune_xgb(self, model, X_train, y_train, model_name, model_parameters):
+        params = {"random_state": self.random_state,
+                  "booster": []
+                  }
+        if model_parameters["xg_boost"].get("random_state"):
+            params["random_state"] = model_parameters["xg_boost"]["random_state"]
+
+        if model_parameters["xg_boost"].get("use_gradient_boosted_tree"):
+            params["booster"].append("gbtree")
+
+        if model_parameters["xg_boost"].get("dart"):
+            params["booster"].append("dart")
+
+        params["n_estimators"] = model_parameters["xg_boost"]["max_num_of_trees"]
+        params["tree_method"] = model_parameters["xg_boost"]["tree_method"]
+        if model_parameters["xg_boost"]["early_stopping"]:
+            params["early_stopping_rounds"] = model_parameters["xg_boost"]["early_stopping_rounds"]
+
+        params["max_depth"] = model_parameters["xg_boost"]["max_depth_of_tree"]
+        params["learning_rate"] = model_parameters["xg_boost"]["learningRate"]
+        params["l1_regularization"] = model_parameters["xg_boost"]["l1_regularization"]
+        params["l2_regularization"] = model_parameters["xg_boost"]["l2_regularization"]
+        params["min_child_weight"] = model_parameters["xg_boost"]["min_child_weight"]
+        params["gamma"] = model_parameters["xg_boost"]["gamma"]
+        params["sub_sample"] = model_parameters["xg_boost"]["sub_sample"]
+        params["col_sample_by_tree"] = model_parameters["xg_boost"]["col_sample_by_tree"]
+
+        gcv = GridSearchCV(model, params, cv=self.k_fold)
+        gcv.fit(X_train, y_train)
+        return gcv.best_estimator_
+
+    
+    def build_and_tune_model(self, X_train, y_train, problem_type, selected_models, model_parameters):
+        self.best_models = {}
+        for model_name in selected_models:
+            if model_name == "xg_boost":
+                st.warning("As of now xg_boost is not supported")
+                continue
+            if model_name == "SVM" and problem_type == "Regression":
+                model = model_dict["SVMRegressor"]
+                best_model = self.tune_svm(model, X_train, y_train, model_parameters)
+
+            elif model_name == "SVM" and problem_type == "Classification":
+                model = model_dict["SVMClassifier"]
+                best_model = self.tune_svm(model, X_train, y_train, model_parameters)
+
+            elif model_name == "KNN" and problem_type == "Regression":
+                model = model_dict["KNNRegressor"]
+                best_model = self.tune_knn(model, X_train, y_train, model_parameters)
+
+            elif model_name == "KNN" and problem_type == "Classification":
+                model = model_dict["KNNClassifier"]
+                best_model = self.tune_knn(model, X_train, y_train, model_parameters)
+
+            elif model_name == "neural_network" and problem_type == "Regression":
+                model = model_dict["neural_network"]
+                best_model = self.tune_neural_network(model, X_train, y_train, model_parameters)
+
+            elif model_name == "neural_network" and problem_type == "Classification":
+                model = model_dict["neural_network"]
+                best_model = self.tune_neural_network(model, X_train, y_train, model_parameters)
+
+            elif model_name == "xg_boost" and problem_type == "Regression":
+                model = model_dict["XGBoostRegressor"]
+                best_model = self.tune_xgb(model, X_train, y_train, model_name, model_parameters)
+
+            elif model_name == "xg_boost" and problem_type == "Classification":
+                model = model_dict["XGBoostClassifier"]
+                best_model = self.tune_xgb(model, X_train, y_train, model_name, model_parameters)
+            else:
+                model = model_dict[model_name]
+
+            if (model_name == "RandomForestClassifier" or model_name == "RandomForestRegressor"):
+                best_model = self.tune_random_forest(model, X_train, y_train, model_name, model_parameters)
+            
+            elif (model_name == "LinearRegression" or  model_name == "ElasticNetRegression"):
+                best_model = self.tune_linear_elasticnet_regression(model, X_train, y_train, model_name, model_parameters)
+            
+            elif model_name == "LogisticRegression":
+                best_model = self.tune_logistic_regression(model, X_train, y_train, model_parameters)
+
+            elif (model_name == "RidgeRegression" or model_name == "LassoRegression"):
+                best_model = self.tune_ridge_lasso_regression(model, X_train, y_train, model_name, model_parameters)
+            
+            elif (model_name == "DecisionTreeRegressor" or model_name == "DecisionTreeClassifier"):
+                best_model = self.tune_decision_tree(model, X_train, y_train, model_name, model_parameters)
+                        
+            self.best_models[model_name] = best_model        
+        
+        
+        return self.best_models
+    
+