Remove unused imports

.gitignore

@@ -1,2 +0,0 @@
-__pycache__
-.venv

frontend/exploration.py

@@ -1,6 +1,5 @@
 import pandas as pd
 import streamlit as st
-import codecs
 
 st.set_page_config(
     page_title="Project Miner",
@@ -10,14 +9,10 @@ st.set_page_config(
 st.title("Home")
 
 ### Exploration
-uploaded_file = st.file_uploader("Upload your CSV file", type=["csv", "tsv"])
+uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
-separator = st.selectbox("Separator", [",", ";", "\\t"])
-separator = codecs.getdecoder("unicode_escape")(separator)[0]
-has_header = st.checkbox("Has header", value=True)
 
 if uploaded_file is not None:
-    st.session_state.data = pd.read_csv(uploaded_file, sep=separator, header=0 if has_header else 1)
+    st.session_state.data = pd.read_csv(uploaded_file)
-    st.session_state.original_data = st.session_state.data
     st.success("File loaded successfully!")
 
 

frontend/normstrategy.py

@@ -1,179 +0,0 @@
-from abc import ABC, abstractmethod
-from pandas import DataFrame, Series
-from pandas.api.types import is_numeric_dtype
-from sklearn.neighbors import KNeighborsClassifier
-from typing import Any, Union
-
-class DataFrameFunction(ABC):
-    """A command that may be applied in-place to a dataframe."""
-
-    @abstractmethod
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        """Apply the current function to the given dataframe, in-place.
-
-        The series is described by its label and dataframe."""
-        return df
-
-
-class MVStrategy(DataFrameFunction):
-    """A way to handle missing values in a dataframe."""
-
-    @staticmethod
-    def list_available(df: DataFrame, label: str, series: Series) -> list['MVStrategy']:
-        """Get all the strategies that can be used."""
-        choices = [DropStrategy(), ModeStrategy()]
-        if is_numeric_dtype(series):
-            choices.extend((MeanStrategy(), MedianStrategy(), LinearRegressionStrategy()))
-            other_columns = df.select_dtypes(include="number").drop(label, axis=1).columns.to_list()
-            if len(other_columns):
-                choices.append(KNNStrategy(other_columns))
-        return choices
-
-
-class ScalingStrategy(DataFrameFunction):
-    """A way to handle missing values in a dataframe."""
-
-    @staticmethod
-    def list_available(df: DataFrame, series: Series) -> list['MVStrategy']:
-        """Get all the strategies that can be used."""
-        choices = [KeepStrategy()]
-        if is_numeric_dtype(series):
-            choices.extend((MinMaxStrategy(), ZScoreStrategy()))
-            if series.sum() != 0:
-                choices.append(UnitLengthStrategy())
-        return choices
-
-
-class DropStrategy(MVStrategy):
-    #@typing.override
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        df.dropna(subset=label, inplace=True)
-        return df
-
-    def __str__(self) -> str:
-        return "Drop"
-
-
-class PositionStrategy(MVStrategy):
-    #@typing.override
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        series.fillna(self.get_value(series), inplace=True)
-        return df
-
-    @abstractmethod
-    def get_value(self, series: Series) -> Any:
-        pass
-
-
-class MeanStrategy(PositionStrategy):
-    #@typing.override
-    def get_value(self, series: Series) -> Union[int, float]:
-        return series.mean()
-
-    def __str__(self) -> str:
-        return "Use mean"
-
-
-class MedianStrategy(PositionStrategy):
-    #@typing.override
-    def get_value(self, series: Series) -> Union[int, float]:
-        return series.median()
-
-    def __str__(self) -> str:
-        return "Use median"
-
-
-class ModeStrategy(PositionStrategy):
-    #@typing.override
-    def get_value(self, series: Series) -> Any:
-        return series.mode()[0]
-
-    def __str__(self) -> str:
-        return "Use mode"
-
-
-class LinearRegressionStrategy(MVStrategy):
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        series.interpolate(inplace=True)
-        return df
-
-    def __str__(self) -> str:
-        return "Use linear regression"
-
-
-class KNNStrategy(MVStrategy):
-    def __init__(self, training_features: list[str]):
-        self.available_features = training_features
-        self.training_features = training_features
-        self.n_neighbors = 3
-
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        # Remove any training column that have any missing values
-        usable_data = df.dropna(subset=self.training_features)
-        # Select columns to impute from
-        train_data = usable_data.dropna(subset=label)
-        # Create train dataframe
-        x_train = train_data.drop(label, axis=1)
-        y_train = train_data[label]
-
-        reg = KNeighborsClassifier(self.n_neighbors).fit(x_train, y_train)
-
-        # Create test dataframe
-        test_data = usable_data[usable_data[label].isnull()]
-        if test_data.empty:
-            return df
-        x_test = test_data.drop(label, axis=1)
-        predicted = reg.predict(x_test)
-
-        # Fill with predicated values and patch the original data
-        usable_data[label].fillna(Series(predicted), inplace=True)
-        df.fillna(usable_data, inplace=True)
-        return df
-
-    def count_max(self, df: DataFrame, label: str) -> int:
-        usable_data = df.dropna(subset=self.training_features)
-        return usable_data[label].count()
-
-    def __str__(self) -> str:
-        return "kNN"
-
-
-class KeepStrategy(ScalingStrategy):
-    #@typing.override
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        return df
-
-    def __str__(self) -> str:
-        return "No-op"
-
-
-class MinMaxStrategy(ScalingStrategy):
-    #@typing.override
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        minimum = series.min()
-        maximum = series.max()
-        df[label] = (series - minimum) / (maximum - minimum)
-        return df
-
-    def __str__(self) -> str:
-        return "Min-max"
-
-
-class ZScoreStrategy(ScalingStrategy):
-    #@typing.override
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        df[label] = (series - series.mean()) / series.std()
-        return df
-
-    def __str__(self) -> str:
-        return "Z-Score"
-
-
-class UnitLengthStrategy(ScalingStrategy):
-    #@typing.override
-    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
-        df[label] = series / series.sum()
-        return df
-
-    def __str__(self) -> str:
-        return "Unit length"

frontend/pages/clustering_dbscan.py

@@ -1,35 +0,0 @@
-import streamlit as st
-import matplotlib.pyplot as plt
-from sklearn.cluster import DBSCAN
-
-st.header("Clustering: dbscan")
-
-
-if "data" in st.session_state:
-    data = st.session_state.data
-
-    with st.form("my_form"):
-        data_name = st.multiselect("Data Name", data.select_dtypes(include="number").columns, max_selections=3)
-        eps = st.slider("eps", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
-        min_samples = st.number_input("min_samples", step=1, min_value=1, value=5)
-        st.form_submit_button("launch")
-
-    if len(data_name) >= 2 and len(data_name) <=3:
-        x = data[data_name].to_numpy()
-
-        dbscan = DBSCAN(eps=eps, min_samples=min_samples)
-        y_dbscan = dbscan.fit_predict(x)
-
-        fig = plt.figure()
-        if len(data_name) == 2:
-            ax = fig.add_subplot(projection='rectilinear')
-            plt.scatter(x[:, 0], x[:, 1], c=y_dbscan, s=50, cmap="viridis")
-        else:
-            ax = fig.add_subplot(projection='3d')
-            ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_dbscan, s=50, cmap="viridis")
-        st.pyplot(fig)
-
-    
-
-else:
-    st.error("file not loaded")

frontend/pages/clustering_kmeans.py

@@ -1,44 +0,0 @@
-import streamlit as st
-from sklearn.cluster import KMeans
-import matplotlib.pyplot as plt
-
-st.header("Clustering: kmeans")
-
-
-if "data" in st.session_state:
-    data = st.session_state.data
-
-    with st.form("my_form"):
-        row1 = st.columns([1,1,1])
-        n_clusters = row1[0].selectbox("Number of clusters", range(1,data.shape[0]))
-        data_name = row1[1].multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=3)
-        n_init = row1[2].number_input("n_init",step=1,min_value=1)
-
-        row2 = st.columns([1,1])
-        max_iter = row1[0].number_input("max_iter",step=1,min_value=1)
-
-
-        st.form_submit_button("launch")
-
-    if len(data_name) >= 2 and len(data_name) <=3:
-        x = data[data_name].to_numpy()
-
-        kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
-        y_kmeans = kmeans.fit_predict(x)
-
-        fig = plt.figure()
-        if len(data_name) == 2:
-            ax = fig.add_subplot(projection='rectilinear')
-            plt.scatter(x[:, 0], x[:, 1], c=y_kmeans, s=50, cmap="viridis")
-            centers = kmeans.cluster_centers_
-            plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
-        else:
-            ax = fig.add_subplot(projection='3d')
-
-            ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_kmeans, s=50, cmap="viridis")
-            centers = kmeans.cluster_centers_
-            ax.scatter(centers[:, 0], centers[:, 1],centers[:, 2], c="black", s=200, marker="X")
-        st.pyplot(fig)
-
-else:
-    st.error("file not loaded")

frontend/pages/normalization.py

@@ -1,35 +0,0 @@
-import streamlit as st
-from normstrategy import MVStrategy, ScalingStrategy, KNNStrategy
-
-if "data" in st.session_state:
-    data = st.session_state.original_data
-    st.session_state.original_data = data.copy()
-
-    for column, series in data.items():
-        col1, col2 = st.columns(2)
-        missing_count = series.isna().sum()
-        choices = MVStrategy.list_available(data, column, series)
-        option = col1.selectbox(
-            f"Missing values of {column} ({missing_count})",
-            choices,
-            index=1,
-            key=f"mv-{column}",
-        )
-        if isinstance(option, KNNStrategy):
-            option.training_features = st.multiselect("Training columns", option.training_features, default=option.available_features, key=f"cols-{column}")
-            option.n_neighbors = st.number_input("Number of neighbors", min_value=1, max_value=option.count_max(data, column), value=option.n_neighbors, key=f"neighbors-{column}")
-        # Always re-get the series to avoid reusing an invalidated series pointer
-        data = option.apply(data, column, data[column])
-
-        choices = ScalingStrategy.list_available(data, series)
-        option = col2.selectbox(
-            "Scaling",
-            choices,
-            key=f"scaling-{column}",
-        )
-        data = option.apply(data, column, data[column])
-
-    st.write(data)
-    st.session_state.data = data
-else:
-    st.error("file not loaded")

frontend/pages/prediction_classification.py

@@ -1,64 +0,0 @@
-import streamlit as st
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
-from sklearn.preprocessing import LabelEncoder
-import pandas as pd
-
-st.header("Prediction: Classification")
-
-if "data" in st.session_state:
-    data = st.session_state.data
-
-    with st.form("classification_form"):
-        st.subheader("Classification Parameters")
-        data_name = st.multiselect("Features", data.columns)
-        target_name = st.selectbox("Target", data.columns)
-        test_size = st.slider("Test Size", min_value=0.1, max_value=0.5, value=0.2, step=0.1)
-        st.form_submit_button('Train and Predict')
-
-    if data_name and target_name:
-        X = data[data_name]
-        y = data[target_name]
-
-        label_encoders = {}
-        for column in X.select_dtypes(include=['object']).columns:
-            le = LabelEncoder()
-            X[column] = le.fit_transform(X[column])
-            label_encoders[column] = le
-        
-        if y.dtype == 'object':
-            le = LabelEncoder()
-            y = le.fit_transform(y)
-            label_encoders[target_name] = le
-
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
-
-        model = LogisticRegression()
-        model.fit(X_train, y_train)
-        y_pred = model.predict(X_test)
-        accuracy = accuracy_score(y_test, y_pred)
-
-        st.subheader("Model Accuracy")
-        st.write(f"Accuracy on test data: {accuracy:.2f}")
-
-        st.subheader("Enter values for prediction")
-        pred_values = []
-        for feature in data_name:
-            if feature in label_encoders:
-                values = list(label_encoders[feature].classes_)
-                value = st.selectbox(f"Value for {feature}", values)
-                value_encoded = label_encoders[feature].transform([value])[0]
-                pred_values.append(value_encoded)
-            else:
-                value = st.number_input(f"Value for {feature}", value=0.0)
-                pred_values.append(value)
-        
-        prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
-
-        if target_name in label_encoders:
-            prediction = label_encoders[target_name].inverse_transform(prediction)
-        
-        st.write("Prediction:", prediction[0])
-else:
-    st.error("File not loaded")

frontend/pages/prediction_regression.py

@@ -1,29 +0,0 @@
-import streamlit as st
-from sklearn.linear_model import LinearRegression
-import pandas as pd
-
-st.header("Prediction: Regression")
-
-if "data" in st.session_state:
-    data = st.session_state.data
-
-    with st.form("regression_form"):
-        st.subheader("Linear Regression Parameters")
-        data_name = st.multiselect("Features", data.select_dtypes(include="number").columns)
-        target_name = st.selectbox("Target", data.select_dtypes(include="number").columns)
-        st.form_submit_button('Train and Predict')
-
-    if data_name and target_name:
-        X = data[data_name]
-        y = data[target_name]
-
-        model = LinearRegression()
-        model.fit(X, y)
-        
-        st.subheader("Enter values for prediction")
-        pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
-        prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
-
-        st.write("Prediction:", prediction[0])
-else:
-    st.error("File not loaded")