Rename to original_data

Co-authored-by: bastien ollier <bastien.ollier@etu.uca.fr>
Co-authored-by: clfreville2 <clement.freville2@etu.uca.fr>
Reviewed-on: https://codefirst.iut.uca.fr/git/clement.freville2/miner/pulls/2
Reviewed-by: Clément FRÉVILLE <clement.freville2@etu.uca.fr>
Co-authored-by: Bastien OLLIER <bastien.ollier@noreply.codefirst.iut.uca.fr>
Co-committed-by: Bastien OLLIER <bastien.ollier@noreply.codefirst.iut.uca.fr>

.gitignore

@@ -0,0 +1 @@
+__pycache__

frontend/exploration.py

@@ -0,0 +1,48 @@
+import pandas as pd
+import streamlit as st
+
+st.set_page_config(
+    page_title="Project Miner",
+    layout="wide"
+)
+
+st.title("Home")
+
+### Exploration
+uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
+
+if uploaded_file is not None:
+    st.session_state.data = pd.read_csv(uploaded_file)
+    st.session_state.original_data = st.session_state.data
+    st.success("File loaded successfully!")
+
+
+if "data" in st.session_state:
+    data = st.session_state.data
+    st.write(data.head(10))
+    st.write(data.tail(10))
+
+    st.header("Data Preview")
+    
+    st.subheader("First 5 Rows")
+    st.write(data.head())
+    
+    st.subheader("Last 5 Rows")
+    st.write(data.tail())
+    
+    st.header("Data Summary")
+    
+    st.subheader("Basic Information")
+    col1, col2 = st.columns(2)
+    col1.metric("Number of Rows", data.shape[0])
+    col2.metric("Number of Columns", data.shape[1])
+    
+    st.write(f"Column Names: {list(data.columns)}")
+    
+    st.subheader("Missing Values by Column")
+    missing_values = data.isnull().sum()
+    st.write(missing_values)
+    
+    st.subheader("Statistical Summary")
+    st.write(data.describe())
+    

frontend/main.py

@@ -1,64 +0,0 @@
-import pandas as pd
-import streamlit as st
-import matplotlib.pyplot as plt
-import seaborn as sns
-from pandas.api.types import is_numeric_dtype
-
-st.set_page_config(
-    page_title="Project Miner",
-    layout="wide"
-)
-
-### Exploration
-uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
-
-if uploaded_file:
-    data = pd.read_csv(uploaded_file)
-    st.success("File loaded successfully!")
-
-    st.header("Data Preview")
-    
-    st.subheader("First 5 Rows")
-    st.write(data.head())
-    
-    st.subheader("Last 5 Rows")
-    st.write(data.tail())
-    
-    st.header("Data Summary")
-    
-    st.subheader("Basic Information")
-    col1, col2 = st.columns(2)
-    col1.metric("Number of Rows", data.shape[0])
-    col2.metric("Number of Columns", data.shape[1])
-    
-    st.write(f"Column Names: {list(data.columns)}")
-    
-    st.subheader("Missing Values by Column")
-    missing_values = data.isnull().sum()
-    st.write(missing_values)
-    
-    st.subheader("Statistical Summary")
-    st.write(data.describe())
-    
-    ### Visualization
-
-    st.header("Data Visualization")
-
-    st.subheader("Histogram")
-    column_to_plot = st.selectbox("Select Column for Histogram", data.columns)
-    if column_to_plot:
-        fig, ax = plt.subplots()
-        ax.hist(data[column_to_plot].dropna(), bins=20, edgecolor='k')
-        ax.set_title(f'Histogram of {column_to_plot}')
-        ax.set_xlabel(column_to_plot)
-        ax.set_ylabel('Frequency')
-        st.pyplot(fig)
-    
-    st.subheader("Boxplot")
-    dataNumeric = data.select_dtypes(include='number')
-    column_to_plot = st.selectbox("Select Column for Boxplot", dataNumeric.columns)
-    if column_to_plot:
-        fig, ax = plt.subplots()
-        sns.boxplot(data=data, x=column_to_plot, ax=ax)
-        ax.set_title(f'Boxplot of {column_to_plot}')
-        st.pyplot(fig)

frontend/normstrategy.py

@@ -0,0 +1,138 @@
+from abc import ABC, abstractmethod
+from pandas import DataFrame, Series
+from pandas.api.types import is_numeric_dtype
+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, 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()))
+        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 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/normalization.py

@@ -0,0 +1,32 @@
+import streamlit as st
+from normstrategy import MVStrategy, ScalingStrategy
+
+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, series)
+        option = col1.selectbox(
+            f"Missing values of {column} ({missing_count})",
+            choices,
+            index=1,
+            key=f"mv-{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/visualization.py

@@ -0,0 +1,30 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+st.header("Data Visualization")
+
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    st.subheader("Histogram")
+    column_to_plot = st.selectbox("Select Column for Histogram", data.columns)
+    if column_to_plot:
+        fig, ax = plt.subplots()
+        ax.hist(data[column_to_plot].dropna(), bins=20, edgecolor='k')
+        ax.set_title(f"Histogram of {column_to_plot}")
+        ax.set_xlabel(column_to_plot)
+        ax.set_ylabel("Frequency")
+        st.pyplot(fig)
+    
+    st.subheader("Boxplot")
+    dataNumeric = data.select_dtypes(include="number")
+    column_to_plot = st.selectbox("Select Column for Boxplot", dataNumeric.columns)
+    if column_to_plot:
+        fig, ax = plt.subplots()
+        sns.boxplot(data=data, x=column_to_plot, ax=ax)
+        ax.set_title(f"Boxplot of {column_to_plot}")
+        st.pyplot(fig)
+else:
+    st.error("file not loaded")