refractor: place all code in src

app.py → src/app.py

@@ -2,14 +2,14 @@ import streamlit as st
 from typing import OrderedDict
 
 
-from src.features.build_features import initialise_data
+from features.build_features import initialise_data
 
-from src.models.xgboost_model import xgboost_class
-from src.models.logistic_model import logistic_class
+from models.xgboost_model import xgboost_class
+from models.logistic_model import logistic_class
 
-from src.models.util_model_comparison import model_comparison_view
+from models.util_model_comparison import model_comparison_view
 
-from src.models.util_strategy_table import strategy_table_view
+from models.util_strategy_table import strategy_table_view
 
 
 def main():

src/features/build_features.py

@@ -6,14 +6,14 @@ import pandas as pd
 import streamlit as st
 
 
-from src.features.util_build_features import (
+from  features.util_build_features import (
     Dataset,
     SplitDataset,
     undersample_training_data,
     select_predictors,
     import_data)
 
-from src.visualization.metrics import (
+from  visualization.metrics import (
     streamlit_2columns_metrics_df_shape,
     streamlit_2columns_metrics_series,
     streamlit_2columns_metrics_pct_series,

src/models/logistic_model.py

@@ -1,10 +1,10 @@
-from src.features.build_features import SplitDataset
+from  features.build_features import SplitDataset
 
-from src.models.logistic_train_model import logistic_train_model
-from src.models.logistic_predict_model import logistic_predict_model
-from src.models.logistic_test_model import logistic_test_model
+from  models.logistic_train_model import logistic_train_model
+from  models.logistic_predict_model import logistic_predict_model
+from  models.logistic_test_model import logistic_test_model
 
-from src.models.util_model_class import ModelClass
+from  models.util_model_class import ModelClass
 
 
 def logistic_class(split_dataset: SplitDataset, currency: str) -> ModelClass:

src/models/logistic_predict_model.py

@@ -1,4 +1,4 @@
-from src.models.util_predict_model import make_prediction_view
+from  models.util_predict_model import make_prediction_view
 
 logistic_predict_model = make_prediction_view(
     "Logistic", "Logisitic Model")

src/models/logistic_test_model.py

@@ -1,4 +1,4 @@
-from src.models.util_test import make_tests_view
+from  models.util_test import make_tests_view
 
 logistic_test_model = make_tests_view(
     "Logistic", "Logistic Model")

src/models/logistic_train_model.py

@@ -1,11 +1,11 @@
 
 import numpy as np
 from sklearn.linear_model import LogisticRegression
-from src.features.build_features import SplitDataset
+from  features.build_features import SplitDataset
 import streamlit as st
 import pandas as pd
 
-from src.visualization.graphs_logistic import plot_logistic_coeff_barh
+from  visualization.graphs_logistic import plot_logistic_coeff_barh
 
 
 @st.cache(suppress_st_warning=True)

src/models/util_model_comparison.py

@@ -1,18 +1,18 @@
 from typing import OrderedDict
 import streamlit as st
 from sklearn.metrics import roc_auc_score
-from src.features.util_build_features import SplitDataset
-from src.visualization.graphs_settings import (
+from  features.util_build_features import SplitDataset
+from  visualization.graphs_settings import (
     streamlit_chart_setting_height_width
 )
 
-from src.visualization.graphs_test import (
+from  visualization.graphs_test import (
     roc_auc_compare_n_models,
     calibration_curve_report_commented_n
 )
 
 
-from src.models.util_model_class import ModelClass
+from  models.util_model_class import ModelClass
 
 
 def roc_auc_for_model(split_dataset: SplitDataset, model_view: ModelClass):

src/models/util_predict_model.py

@@ -7,9 +7,9 @@ import pandas as pd
 from dataclasses import dataclass
 
 from xgboost import XGBClassifier
-from src.features.util_build_features import SplitDataset
+from  features.util_build_features import SplitDataset
 
-from src.models.util_predict_model_threshold import (
+from  models.util_predict_model_threshold import (
     user_defined_probability_threshold,
     J_statistic_driven_probability_threshold,
     tradeoff_threshold,

src/models/util_predict_model_threshold.py

@@ -10,9 +10,9 @@ import pandas as pd
 
 from numpy import argmax
 
-from src.visualization.metrics import streamlit_2columns_metrics_df, streamlit_2columns_metrics_pct_df
+from  visualization.metrics import streamlit_2columns_metrics_df, streamlit_2columns_metrics_pct_df
 
-from src.visualization.graphs_threshold import acceptance_rate_driven_threshold_graph
+from  visualization.graphs_threshold import acceptance_rate_driven_threshold_graph
 
 
 def model_probability_values_df(model, X):

src/models/util_strategy_table.py

@@ -2,8 +2,8 @@ from typing import OrderedDict
 import plotly.express as px
 import numpy as np
 import streamlit as st
-from src.models.util_test import create_strategyTable_df
-from src.models.util_model_class import ModelClass
+from  models.util_test import create_strategyTable_df
+from  models.util_model_class import ModelClass
 
 
 def strategy_table_view(

src/models/util_test.py

@@ -10,13 +10,13 @@ from sklearn.metrics import (
 from sklearn.linear_model import LogisticRegression
 import xgboost as xgb
 from xgboost.sklearn import XGBClassifier
-from src.features.util_build_features import SplitDataset
-"""from src.models.model_utils import (
+from  features.util_build_features import SplitDataset
+"""from  models.model_utils import (
     create_cross_validation_df,
     cross_validation_scores,
     get_df_trueStatus_probabilityDefault_threshStatus_loanAmount,
 )"""
-from src.visualization.graphs_test import (
+from  visualization.graphs_test import (
     cross_validation_graph,
 )
 

src/models/xgboost_model.py

@@ -1,10 +1,10 @@
-from src.features.build_features import SplitDataset
+from  features.build_features import SplitDataset
 
-from src.models.xgboost_train_model import xgboost_train_model
-from src.models.xgboost_predict_model import xgboost_predit_model
-from src.models.xgboost_test_model import xgboost_test_model
+from  models.xgboost_train_model import xgboost_train_model
+from  models.xgboost_predict_model import xgboost_predit_model
+from  models.xgboost_test_model import xgboost_test_model
 
-from src.models.util_model_class import ModelClass
+from  models.util_model_class import ModelClass
 
 
 def xgboost_class(split_dataset: SplitDataset, currency: str):

src/models/xgboost_predict_model.py

@@ -1,4 +1,4 @@
-from src.models.util_predict_model import make_prediction_view
+from  models.util_predict_model import make_prediction_view
 
 xgboost_predit_model = make_prediction_view(
     "XGBoost", "Gradient Boosted Tree with XGBoost")

src/models/xgboost_test_model.py

@@ -1,4 +1,4 @@
-from src.models.util_test import make_tests_view
+from  models.util_test import make_tests_view
 
 xgboost_test_model = make_tests_view(
     "XGBoost", "Gradient Boosted Tree with XGBoost")

src/models/xgboost_train_model.py

@@ -2,15 +2,15 @@ import pickle
 
 import numpy as np
 import xgboost as xgb
-from src.features.build_features import SplitDataset
+from  features.build_features import SplitDataset
 import streamlit as st
 
-from src.visualization.graphs_decision_tree import(plot_importance_gbt,
+from  visualization.graphs_decision_tree import(plot_importance_gbt,
                                                    plot_tree_gbt)
 
-from src.visualization.graphs_settings import streamlit_chart_setting_height_width
+from  visualization.graphs_settings import streamlit_chart_setting_height_width
 
-from src.visualization.graphs_download import (download_importance_gbt,
+from  visualization.graphs_download import (download_importance_gbt,
                                                download_tree_gbt)
 
 

src/visualization/graphs_test.py

@@ -4,7 +4,7 @@ from sklearn.metrics import roc_curve
 
 from typing import OrderedDict
 
-from src.models.util_model_class import ModelClass
+from  models.util_model_class import ModelClass
 
 from sklearn.calibration import calibration_curve